Deck 13: Analysis of Variance

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Question
Fisher's 100(1 - α)% confidence interval for the difference between two population means μi - μj is:

A) <strong>Fisher's 100(1 - α)% confidence interval for the difference between two population means μ<sub>i</sub> - μ<sub>j</sub> is:</strong> A)   B)   C)   D)   <div style=padding-top: 35px>
B) <strong>Fisher's 100(1 - α)% confidence interval for the difference between two population means μ<sub>i</sub> - μ<sub>j</sub> is:</strong> A)   B)   C)   D)   <div style=padding-top: 35px>
C) <strong>Fisher's 100(1 - α)% confidence interval for the difference between two population means μ<sub>i</sub> - μ<sub>j</sub> is:</strong> A)   B)   C)   D)   <div style=padding-top: 35px>
D) <strong>Fisher's 100(1 - α)% confidence interval for the difference between two population means μ<sub>i</sub> - μ<sub>j</sub> is:</strong> A)   B)   C)   D)   <div style=padding-top: 35px>
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Question
If the amount of variability between treatments is significantly greater than the amount of variability within treatments,then:

A)reject the null hypothesis of equal population means
B)do not reject the null hypothesis of equal population means
C)conclude that the ratio of between-treatments variability to within-treatments variability is significantly less than 1
D)perform further analysis using the two-way ANOVA with interaction
Question
If units within each block are randomly assigned to each of the treatments,then the design of the experiment is referred to as a completely randomized design.
Question
Identify the assumption that is not applicable for a one-way ANOVA test.

A)The populations are normally distributed
B)The population standard deviations are not all equal
C)The samples are selected independently
D)The sample is drawn at random from each population
Question
When using Fisher's least significant difference (LSD)method at some stated significance level,the probability of committing a Type I error increases as the number of:

A)pairwise comparisons decreases
B)pairwise comparisons increases
C)sample size increases
D)treatments decreases
Question
The interaction test is performed before making any conclusions based on the tests for the main effects.
Question
The between-treatments variability is the estimate of σ2 which is based on the variability due to chance.
Question
In general,blocks are the levels at which we hold an integral factor fixed,so that we can measure its contribution to the variation within the samples.
Question
When the null hypothesis is rejected in an ANOVA test,Fisher's least significant difference method is superior to Tukey's honestly significant differences method to determine which population means differ.
Question
Fisher's least difference (LSD)method is applied when the:

A)ANOVA test has not rejected the null hypothesis of equal population means
B)ANOVA test has rejected the null hypothesis of equal population means
C)Two-sample t test is not applicable
D)None of the above
Question
When two factors interact,the effect of one factor on the population mean depends upon the specific value or level present for the other factor.
Question
One-Way ANOVA analyzes the effect of one factor on the population mean.It is based on a:

A)randomized block design
B)completely randomized design
C)factorial design
D)balanced incomplete block design
Question
We use ANOVA to test for differences between population means by examining the amount of variability between the samples relative to the amount of variability within the samples.
Question
ANOVA is a statistical technique used to determine if differences exist between the means of two populations.
Question
One-way ANOVA analyzes the effect of one factor on the population mean and it is based on a completely randomized design.
Question
Which of the following is the correct interpretation of the Fisher's 100(1 - α)% confidence interval for μi - μj?

A)If the interval includes the value zero,the null hypothesis,that H0: μi - μj = 0,is rejected for at α level of significance
B)If the interval does not include the value zero,the null hypothesis,that H0: μi - μj = 0,is rejected at 100(1 - α)% level of significance
C)If the interval does not include the value zero,the null hypothesis,that H0: μi - μj = 0,is rejected at α level of significance
D)If the interval includes the value zero,the null hypothesis,that H0: μi - μj = 0,is rejected at 100(1 - α)% level of significance
Question
When using Fisher's LSD method at some stated significance level α,the probability of committing a Type I error increases as the number of pairwise comparisons increases.
Question
If there are five treatments under study,the number of pairwise comparisons is:

A)15
B)5
C)20
D)10
Question
The variability due to chance,also known as within-treatments variability,is the estimate of σ2 which is based on the:

A)variability of the data across different samples
B)consistency of the data within each sample
C)variability of the data within each sample
D)reliability of the data within each sample
Question
Between-treatments variability is based on a weighted sum of squared differences between the:

A)population variances and the overall mean of the data set
B)sample means and the overall mean of the data set
C)sample variances and the overall mean of the data set
D)population means and the overall mean of the data set
Question
Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625. <strong>Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625.   Refer to Exhibit 13.2.Which of the following is the sum of squared errors?</strong> A)264.29 B)5,285.83 C)18,567.63 D)13,281.79 <div style=padding-top: 35px> Refer to Exhibit 13.2.Which of the following is the sum of squared errors?

A)264.29
B)5,285.83
C)18,567.63
D)13,281.79
Question
If units with each block are randomly assigned to each of the treatments,then the design of the experiment is referred to as a:

A)factorial design
B)completely randomized design
C)randomized block design
D)balanced incomplete block design
Question
Tukey's 100(1 - α)% confidence interval for the difference between two population means μi - μj for balanced data is given by ____.

A) <strong>Tukey's 100(1 - α)% confidence interval for the difference between two population means μ<sub>i</sub> - μ<sub>j</sub> for balanced data is given by ____.</strong> A)   B)   C)   D)   <div style=padding-top: 35px>
B) <strong>Tukey's 100(1 - α)% confidence interval for the difference between two population means μ<sub>i</sub> - μ<sub>j</sub> for balanced data is given by ____.</strong> A)   B)   C)   D)   <div style=padding-top: 35px>
C) <strong>Tukey's 100(1 - α)% confidence interval for the difference between two population means μ<sub>i</sub> - μ<sub>j</sub> for balanced data is given by ____.</strong> A)   B)   C)   D)   <div style=padding-top: 35px>
D) <strong>Tukey's 100(1 - α)% confidence interval for the difference between two population means μ<sub>i</sub> - μ<sub>j</sub> for balanced data is given by ____.</strong> A)   B)   C)   D)   <div style=padding-top: 35px>
Question
Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625. <strong>Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625.   Refer to Exhibit 13.2.At the 5% significance level,the critical value is:</strong> A)2.38 B)3.10 C)3.86 D)4.94 <div style=padding-top: 35px> Refer to Exhibit 13.2.At the 5% significance level,the critical value is:

A)2.38
B)3.10
C)3.86
D)4.94
Question
One of the disadvantages of Fisher's least difference (LSD)method is that the probability of committing a:

A)Type II error increases as the number of pairwise comparisons increases.
B)Type I error increases as the number of pairwise comparisons decreases.
C)Type II error increases as the number of pairwise comparisons decreases.
D)Type I error increases as the number of pairwise comparisons increases.
Question
In a two-way ANOVA test,how many null hypotheses are tested?

A)1
B)1 or 2
C)2 or 3
D)More than 3
Question
If the interaction between two factors is not significant,the next tests to be done are:

A)None,the analysis is complete.
B)None,gather more data.
C)Tests about the population means of factor A or factor B using two-way ANOVA without interaction.
D)Tukey's confidence intervals.
Question
Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625. <strong>Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625.   Refer to Exhibit 13.2.The conclusion for the hypothesis test is:</strong> A)Reject the null hypothesis,cannot conclude that not all mean commute times are equal B)Do not reject the null hypothesis,cannot conclude that not all mean commute times are equal C)Reject the null hypothesis,not all mean commute times are equal D)Do not reject the null hypothesis,not all mean commute times are equal <div style=padding-top: 35px> Refer to Exhibit 13.2.The conclusion for the hypothesis test is:

A)Reject the null hypothesis,cannot conclude that not all mean commute times are equal
B)Do not reject the null hypothesis,cannot conclude that not all mean commute times are equal
C)Reject the null hypothesis,not all mean commute times are equal
D)Do not reject the null hypothesis,not all mean commute times are equal
Question
Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625. <strong>Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625.   Refer to Exhibit 13.2.Which of the following is the sum of squares due to treatments?</strong> A)5,285.83 B)13,281.79 C)18,567.63 D)4,427.26 <div style=padding-top: 35px> Refer to Exhibit 13.2.Which of the following is the sum of squares due to treatments?

A)5,285.83
B)13,281.79
C)18,567.63
D)4,427.26
Question
Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625. <strong>Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625.   Refer to Exhibit 13.2.The value of the test statistic is:</strong> A)0.06 B)0.40 C)2.51 D)16.75 <div style=padding-top: 35px> Refer to Exhibit 13.2.The value of the test statistic is:

A)0.06
B)0.40
C)2.51
D)16.75
Question
Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625. <strong>Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625.   Refer to Exhibit 13.2.Based on the sample standard deviation,the one-way ANOVA assumption which is likely not met is:</strong> A)The populations are normally distributed B)The population standard deviations are assumed to be equal C)The samples are independent D)None of the above <div style=padding-top: 35px> Refer to Exhibit 13.2.Based on the sample standard deviation,the one-way ANOVA assumption which is likely not met is:

A)The populations are normally distributed
B)The population standard deviations are assumed to be equal
C)The samples are independent
D)None of the above
Question
Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625. <strong>Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625.   Refer to Exhibit 13.2.The competing hypotheses about the mean commute times are:</strong> A)   B)   C)   D)   <div style=padding-top: 35px> Refer to Exhibit 13.2.The competing hypotheses about the mean commute times are:

A) <strong>Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625.   Refer to Exhibit 13.2.The competing hypotheses about the mean commute times are:</strong> A)   B)   C)   D)   <div style=padding-top: 35px>
B) <strong>Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625.   Refer to Exhibit 13.2.The competing hypotheses about the mean commute times are:</strong> A)   B)   C)   D)   <div style=padding-top: 35px>
C) <strong>Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625.   Refer to Exhibit 13.2.The competing hypotheses about the mean commute times are:</strong> A)   B)   C)   D)   <div style=padding-top: 35px>
D) <strong>Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625.   Refer to Exhibit 13.2.The competing hypotheses about the mean commute times are:</strong> A)   B)   C)   D)   <div style=padding-top: 35px>
Question
Exhibit 13.1 The following is an incomplete ANOVA table. <strong>Exhibit 13.1 The following is an incomplete ANOVA table.   Refer to Exhibit 13.1.The value of the test statistic is:</strong> A)1.333 B)9.375 C)12.5 D)100 <div style=padding-top: 35px> Refer to Exhibit 13.1.The value of the test statistic is:

A)1.333
B)9.375
C)12.5
D)100
Question
Tukey's honestly significant differences (HSD)method ensures that the probability of a Type I error remains fixed irrespective of the number of:

A)pairwise comparisons
B)treatments
C)replications within each treatment
D)replications for each combination of factor A and factor B
Question
Tukey's honestly significant differences (HSD)method uses _____ instead of _____ when compared to Fishers least differences (LSD)method for pairwise comparisons.

A)t values;studentized range values
B)studentized range values;F values
C)F values;t values
D)studentized range values;t values
Question
Exhibit 13.1 The following is an incomplete ANOVA table. <strong>Exhibit 13.1 The following is an incomplete ANOVA table.   Refer to Exhibit 13.1.The mean square error is:</strong> A)1.333 B)9.375 C)25 D)75 <div style=padding-top: 35px> Refer to Exhibit 13.1.The mean square error is:

A)1.333
B)9.375
C)25
D)75
Question
Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625. <strong>Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625.   Refer to Exhibit 13.2.Which of the following is the mean square for treatments?</strong> A)18,567.63 B)13,281.79 C)5,285.83 D)4,427.26 <div style=padding-top: 35px> Refer to Exhibit 13.2.Which of the following is the mean square for treatments?

A)18,567.63
B)13,281.79
C)5,285.83
D)4,427.26
Question
Exhibit 13.1 The following is an incomplete ANOVA table. <strong>Exhibit 13.1 The following is an incomplete ANOVA table.   Refer to exhibit 13.1.The sum of squares due to treatments is:</strong> A)10 B)25 C)75 D)100 <div style=padding-top: 35px> Refer to exhibit 13.1.The sum of squares due to treatments is:

A)10
B)25
C)75
D)100
Question
Exhibit 13.1 The following is an incomplete ANOVA table. <strong>Exhibit 13.1 The following is an incomplete ANOVA table.   Refer to Exhibit 13.1.For the within groups,the degrees of freedom are:</strong> A)6 B)7 C)8 D)9 <div style=padding-top: 35px> Refer to Exhibit 13.1.For the within groups,the degrees of freedom are:

A)6
B)7
C)8
D)9
Question
Exhibit 13.1 The following is an incomplete ANOVA table. <strong>Exhibit 13.1 The following is an incomplete ANOVA table.   Refer to Exhibit 13.1.At the 5% significance level,the critical value is:</strong> A)3.11 B)4.46 C)6.06 D)8.65 <div style=padding-top: 35px> Refer to Exhibit 13.1.At the 5% significance level,the critical value is:

A)3.11
B)4.46
C)6.06
D)8.65
Question
Exhibit 13.4 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Tukey 95% confidence intervals are shown below. <strong>Exhibit 13.4 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Tukey 95% confidence intervals are shown below.   Refer to Exhibit 13.4.How many pairs of cities show a significant difference in average commute times to work?</strong> A)2 B)3 C)4 D)6 <div style=padding-top: 35px> Refer to Exhibit 13.4.How many pairs of cities show a significant difference in average commute times to work?

A)2
B)3
C)4
D)6
Question
Exhibit 13.3 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Fisher 95% confidence intervals are shown below. <strong>Exhibit 13.3 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Fisher 95% confidence intervals are shown below.   Refer to Exhibit 13.3.How many pairs of cities show a significant difference in average commute times to work?</strong> A)2 B)3 C)4 D)6 <div style=padding-top: 35px> Refer to Exhibit 13.3.How many pairs of cities show a significant difference in average commute times to work?

A)2
B)3
C)4
D)6
Question
Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.How many degrees of freedom are there for factors A and B?</strong> A)2,3 B)3,4 C)3,6 D)2,4 <div style=padding-top: 35px> <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.How many degrees of freedom are there for factors A and B?</strong> A)2,3 B)3,4 C)3,6 D)2,4 <div style=padding-top: 35px> Refer to Exhibit 13.6.How many degrees of freedom are there for factors A and B?

A)2,3
B)3,4
C)3,6
D)2,4
Question
Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.At the 5% significance level,the conclusion for the hypothesis test about factor A is:</strong> A)Do not reject the null hypothesis,the average mortgage payments differ by income level B)Do not reject the null hypothesis,cannot conclude the average mortgage payments differ by income level C)Reject the null hypothesis,the average mortgage payments differ by income level D)Reject the null hypothesis,cannot conclude the average mortgage payments differ by income level <div style=padding-top: 35px> <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.At the 5% significance level,the conclusion for the hypothesis test about factor A is:</strong> A)Do not reject the null hypothesis,the average mortgage payments differ by income level B)Do not reject the null hypothesis,cannot conclude the average mortgage payments differ by income level C)Reject the null hypothesis,the average mortgage payments differ by income level D)Reject the null hypothesis,cannot conclude the average mortgage payments differ by income level <div style=padding-top: 35px> Refer to Exhibit 13.6.At the 5% significance level,the conclusion for the hypothesis test about factor A is:

A)Do not reject the null hypothesis,the average mortgage payments differ by income level
B)Do not reject the null hypothesis,cannot conclude the average mortgage payments differ by income level
C)Reject the null hypothesis,the average mortgage payments differ by income level
D)Reject the null hypothesis,cannot conclude the average mortgage payments differ by income level
Question
Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below. <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.At the 1% significance level,the conclusion for the hypothesis test is:</strong> A)Reject the null hypothesis,not all mean number of crimes are equal B)Do not reject the null hypothesis,not all mean number of crimes are equal C)Reject the null hypothesis,cannot conclude that not all mean number of crimes are equal D)Do not reject the null hypothesis,cannot conclude that not all mean number of crimes are equal <div style=padding-top: 35px> <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.At the 1% significance level,the conclusion for the hypothesis test is:</strong> A)Reject the null hypothesis,not all mean number of crimes are equal B)Do not reject the null hypothesis,not all mean number of crimes are equal C)Reject the null hypothesis,cannot conclude that not all mean number of crimes are equal D)Do not reject the null hypothesis,cannot conclude that not all mean number of crimes are equal <div style=padding-top: 35px> Refer to Exhibit 13.5.At the 1% significance level,the conclusion for the hypothesis test is:

A)Reject the null hypothesis,not all mean number of crimes are equal
B)Do not reject the null hypothesis,not all mean number of crimes are equal
C)Reject the null hypothesis,cannot conclude that not all mean number of crimes are equal
D)Do not reject the null hypothesis,cannot conclude that not all mean number of crimes are equal
Question
Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below. <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.The competing hypotheses about the mean crime rates are:</strong> A)   B)   C)   D)   <div style=padding-top: 35px> <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.The competing hypotheses about the mean crime rates are:</strong> A)   B)   C)   D)   <div style=padding-top: 35px> Refer to Exhibit 13.5.The competing hypotheses about the mean crime rates are:

A) <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.The competing hypotheses about the mean crime rates are:</strong> A)   B)   C)   D)   <div style=padding-top: 35px>
B) <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.The competing hypotheses about the mean crime rates are:</strong> A)   B)   C)   D)   <div style=padding-top: 35px>
C) <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.The competing hypotheses about the mean crime rates are:</strong> A)   B)   C)   D)   <div style=padding-top: 35px>
D) <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.The competing hypotheses about the mean crime rates are:</strong> A)   B)   C)   D)   <div style=padding-top: 35px>
Question
Exhibit 13.3 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Fisher 95% confidence intervals are shown below. <strong>Exhibit 13.3 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Fisher 95% confidence intervals are shown below.   Refer to Exhibit 13.3.Which of the following is the   value used to calculate the Fisher 95% confidence intervals?</strong> A)1.725 B)2.086 C)2.080 D)2.090 <div style=padding-top: 35px> Refer to Exhibit 13.3.Which of the following is the <strong>Exhibit 13.3 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Fisher 95% confidence intervals are shown below.   Refer to Exhibit 13.3.Which of the following is the   value used to calculate the Fisher 95% confidence intervals?</strong> A)1.725 B)2.086 C)2.080 D)2.090 <div style=padding-top: 35px> value used to calculate the Fisher 95% confidence intervals?

A)1.725
B)2.086
C)2.080
D)2.090
Question
Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.At the 1% significance level,the conclusion for the hypothesis test about factor B is:</strong> A)Do not reject the null hypothesis,the average mortgage payments differ by zonal location B)Do not reject the null hypothesis,cannot conclude the average mortgage payments differ by zonal location C)Reject the null hypothesis,the average mortgage payments differ by zonal location D)Reject the null hypothesis,cannot conclude the average mortgage payments differ by zonal location <div style=padding-top: 35px> <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.At the 1% significance level,the conclusion for the hypothesis test about factor B is:</strong> A)Do not reject the null hypothesis,the average mortgage payments differ by zonal location B)Do not reject the null hypothesis,cannot conclude the average mortgage payments differ by zonal location C)Reject the null hypothesis,the average mortgage payments differ by zonal location D)Reject the null hypothesis,cannot conclude the average mortgage payments differ by zonal location <div style=padding-top: 35px> Refer to Exhibit 13.6.At the 1% significance level,the conclusion for the hypothesis test about factor B is:

A)Do not reject the null hypothesis,the average mortgage payments differ by zonal location
B)Do not reject the null hypothesis,cannot conclude the average mortgage payments differ by zonal location
C)Reject the null hypothesis,the average mortgage payments differ by zonal location
D)Reject the null hypothesis,cannot conclude the average mortgage payments differ by zonal location
Question
Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.Which of the following is the value of the test statistic for factor A?</strong> A)4.76 B)5.14 C)9.41 D)32.86 <div style=padding-top: 35px> <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.Which of the following is the value of the test statistic for factor A?</strong> A)4.76 B)5.14 C)9.41 D)32.86 <div style=padding-top: 35px> Refer to Exhibit 13.6.Which of the following is the value of the test statistic for factor A?

A)4.76
B)5.14
C)9.41
D)32.86
Question
Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below. <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.At the 1% significance level,the conclusion from Tukey's confidence intervals is:</strong> A)Cannot conclude the mean number of crimes differs for West and East B)Cannot conclude the mean number of crimes differs for West and South C)Cannot conclude the mean number of crimes differs for South and North D)Cannot conclude the mean number of crimes differs for West and North <div style=padding-top: 35px> <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.At the 1% significance level,the conclusion from Tukey's confidence intervals is:</strong> A)Cannot conclude the mean number of crimes differs for West and East B)Cannot conclude the mean number of crimes differs for West and South C)Cannot conclude the mean number of crimes differs for South and North D)Cannot conclude the mean number of crimes differs for West and North <div style=padding-top: 35px> Refer to Exhibit 13.5.At the 1% significance level,the conclusion from Tukey's confidence intervals is:

A)Cannot conclude the mean number of crimes differs for West and East
B)Cannot conclude the mean number of crimes differs for West and South
C)Cannot conclude the mean number of crimes differs for South and North
D)Cannot conclude the mean number of crimes differs for West and North
Question
Exhibit 13.3 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Fisher 95% confidence intervals are shown below. <strong>Exhibit 13.3 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Fisher 95% confidence intervals are shown below.   Refer to Exhibit 13.3.Which of these pair of cities shows no significant difference in average commute times to work?</strong> A)Houston,Akron B)Charlotte,Akron C)Charlotte,Tucson D)Houston,Tucson <div style=padding-top: 35px> Refer to Exhibit 13.3.Which of these pair of cities shows no significant difference in average commute times to work?

A)Houston,Akron
B)Charlotte,Akron
C)Charlotte,Tucson
D)Houston,Tucson
Question
Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below. <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.At the 1% significance level,the critical value is:</strong> A)2.38 B)3.10 C)3.86 D)4.94 <div style=padding-top: 35px> <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.At the 1% significance level,the critical value is:</strong> A)2.38 B)3.10 C)3.86 D)4.94 <div style=padding-top: 35px> Refer to Exhibit 13.5.At the 1% significance level,the critical value is:

A)2.38
B)3.10
C)3.86
D)4.94
Question
Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.At the 1% significance level,the critical value for the hypothesis test about factor B is:</strong> A)3.29 B)4.76 C)6.60 D)9.78 <div style=padding-top: 35px> <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.At the 1% significance level,the critical value for the hypothesis test about factor B is:</strong> A)3.29 B)4.76 C)6.60 D)9.78 <div style=padding-top: 35px> Refer to Exhibit 13.6.At the 1% significance level,the critical value for the hypothesis test about factor B is:

A)3.29
B)4.76
C)6.60
D)9.78
Question
Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.Which of the following is the value of the test statistic for factor B?</strong> A)4.76 B)5.14 C)9.41 D)32.86 <div style=padding-top: 35px> <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.Which of the following is the value of the test statistic for factor B?</strong> A)4.76 B)5.14 C)9.41 D)32.86 <div style=padding-top: 35px> Refer to Exhibit 13.6.Which of the following is the value of the test statistic for factor B?

A)4.76
B)5.14
C)9.41
D)32.86
Question
Exhibit 13.4 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Tukey 95% confidence intervals are shown below. <strong>Exhibit 13.4 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Tukey 95% confidence intervals are shown below.   Refer to Exhibit 13.4.Which of these pair of cities shows a significant difference in average commute times to work?</strong> A)Houston,Akron B)Charlotte,Akron C)Charlotte,Tucson D)Akron,Tucson <div style=padding-top: 35px> Refer to Exhibit 13.4.Which of these pair of cities shows a significant difference in average commute times to work?

A)Houston,Akron
B)Charlotte,Akron
C)Charlotte,Tucson
D)Akron,Tucson
Question
Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below. <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.The degrees of freedom for the hypothesis test are:</strong> A)4,20 B)3,23 C)3,20 D)4,23 <div style=padding-top: 35px> <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.The degrees of freedom for the hypothesis test are:</strong> A)4,20 B)3,23 C)3,20 D)4,23 <div style=padding-top: 35px> Refer to Exhibit 13.5.The degrees of freedom for the hypothesis test are:

A)4,20
B)3,23
C)3,20
D)4,23
Question
Exhibit 13.4 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Tukey 95% confidence intervals are shown below. <strong>Exhibit 13.4 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Tukey 95% confidence intervals are shown below.   Refer to Exhibit 13.4.Which of the following is the studentized range value with α = 0.05 for Tukey's HSD method?</strong> A)5.02 B)3.58 C)3.96 D)4.64 <div style=padding-top: 35px> Refer to Exhibit 13.4.Which of the following is the studentized range value with α = 0.05 for Tukey's HSD method?

A)5.02
B)3.58
C)3.96
D)4.64
Question
Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.At the 5% significance level,the critical value for the hypothesis test about factor A is:</strong> A)3.46 B)5.14 C)7.26 D)10.92 <div style=padding-top: 35px> <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.At the 5% significance level,the critical value for the hypothesis test about factor A is:</strong> A)3.46 B)5.14 C)7.26 D)10.92 <div style=padding-top: 35px> Refer to Exhibit 13.6.At the 5% significance level,the critical value for the hypothesis test about factor A is:

A)3.46
B)5.14
C)7.26
D)10.92
Question
Exhibit 13.4 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Tukey 95% confidence intervals are shown below. <strong>Exhibit 13.4 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Tukey 95% confidence intervals are shown below.   Refer to Exhibit 13.4.The conclusion of the Tukey confidence intervals is:</strong> A)The mean commute time in Houston is different from the mean commute time in Charlotte,Tucson,and Akron. B)The mean commute time in Charlotte is different from the mean commute time in Houston,Tucson,and Akron. C)The mean commute time in Tucson is different from the mean commute time in Houston,Charlotte,and Akron. D)The mean commute time in Akron is different from the mean time in Houston,Charlotte,and Tucson. <div style=padding-top: 35px> Refer to Exhibit 13.4.The conclusion of the Tukey confidence intervals is:

A)The mean commute time in Houston is different from the mean commute time in Charlotte,Tucson,and Akron.
B)The mean commute time in Charlotte is different from the mean commute time in Houston,Tucson,and Akron.
C)The mean commute time in Tucson is different from the mean commute time in Houston,Charlotte,and Akron.
D)The mean commute time in Akron is different from the mean time in Houston,Charlotte,and Tucson.
Question
Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.Which of the following are the total degrees of freedom?</strong> A)10 B)11 C)12 D)6 <div style=padding-top: 35px> <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.Which of the following are the total degrees of freedom?</strong> A)10 B)11 C)12 D)6 <div style=padding-top: 35px> Refer to Exhibit 13.6.Which of the following are the total degrees of freedom?

A)10
B)11
C)12
D)6
Question
Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.The value of SSE is:</strong> A)46,869 B)159,860 C)116,767 D)1,321,831 <div style=padding-top: 35px> <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.The value of SSE is:</strong> A)46,869 B)159,860 C)116,767 D)1,321,831 <div style=padding-top: 35px> Refer to Exhibit 13.8.The value of SSE is:

A)46,869
B)159,860
C)116,767
D)1,321,831
Question
Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below. <strong>Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below.   Refer to Exhibit 13.7.Which of the following is the value of MSE?</strong> A)15,623 B)79,930 C)37,170 D)1,321,831 <div style=padding-top: 35px> Refer to Exhibit 13.7.Which of the following is the value of MSE?

A)15,623
B)79,930
C)37,170
D)1,321,831
Question
Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.The degrees of freedom for the interaction and the error are:</strong> A)6,24 B)6,30 C)24,6 D)30,6 <div style=padding-top: 35px> <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.The degrees of freedom for the interaction and the error are:</strong> A)6,24 B)6,30 C)24,6 D)30,6 <div style=padding-top: 35px> Refer to Exhibit 13.8.The degrees of freedom for the interaction and the error are:

A)6,24
B)6,30
C)24,6
D)30,6
Question
A farmer plants tomato seeds into four different plots.In each plot,there is a different fertilizer treatment that is applied to the soil.After three weeks,he measures the height of each tomato plant from each of the four plots.The data he collects is given shown below. <strong>A farmer plants tomato seeds into four different plots.In each plot,there is a different fertilizer treatment that is applied to the soil.After three weeks,he measures the height of each tomato plant from each of the four plots.The data he collects is given shown below.   </strong> A)Construct an ANOVA table. B)Set up the competing hypothesis to test whether there are some differences in the mean heights between the different plots/fertilizers. C)At the 5% significance level,what is the conclusion to the test? D)Consider the sample standard deviations for each plot.Which of the assumptions might be violated? <div style=padding-top: 35px>

A)Construct an ANOVA table.
B)Set up the competing hypothesis to test whether there are some differences in the mean heights between the different plots/fertilizers.
C)At the 5% significance level,what is the conclusion to the test?
D)Consider the sample standard deviations for each plot.Which of the assumptions might be violated?
Question
Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below. <strong>Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below.   Refer to Exhibit 13.7.How many degrees of freedom are there for factors A and B?</strong> A)2,3 B)3,4 C)3,6 D)2,4 <div style=padding-top: 35px> Refer to Exhibit 13.7.How many degrees of freedom are there for factors A and B?

A)2,3
B)3,4
C)3,6
D)2,4
Question
Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.At the 5% significance level,the critical value for the test about the interaction is:</strong> A)2.04 B)2.51 C)2.99 D)3.67 <div style=padding-top: 35px> <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.At the 5% significance level,the critical value for the test about the interaction is:</strong> A)2.04 B)2.51 C)2.99 D)3.67 <div style=padding-top: 35px> Refer to Exhibit 13.8.At the 5% significance level,the critical value for the test about the interaction is:

A)2.04
B)2.51
C)2.99
D)3.67
Question
Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.For the analysis with an interaction between spending category and generation,the first hypothesis test to conduct should be about the:</strong> A)Average spending across spending B)The interaction between spending and generation C)Average spending across generation D)Both the average spending across spending and generation <div style=padding-top: 35px> <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.For the analysis with an interaction between spending category and generation,the first hypothesis test to conduct should be about the:</strong> A)Average spending across spending B)The interaction between spending and generation C)Average spending across generation D)Both the average spending across spending and generation <div style=padding-top: 35px> Refer to Exhibit 13.8.For the analysis with an interaction between spending category and generation,the first hypothesis test to conduct should be about the:

A)Average spending across spending
B)The interaction between spending and generation
C)Average spending across generation
D)Both the average spending across spending and generation
Question
Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.The value of MSAB is:</strong> A)983,335 B)116,767 C)159,860 D)166,389 <div style=padding-top: 35px> <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.The value of MSAB is:</strong> A)983,335 B)116,767 C)159,860 D)166,389 <div style=padding-top: 35px> Refer to Exhibit 13.8.The value of MSAB is:

A)983,335
B)116,767
C)159,860
D)166,389
Question
Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below. <strong>Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below.   Refer to Exhibit 13.7.At the 5% significance level,the conclusion of the hypothesis test for factor A is:</strong> A)Do not reject the null hypothesis,cannot conclude the average amount spent differs by spending category B)Do not reject the null hypothesis,the average amount spent differs by spending category C)Reject the null hypothesis,cannot conclude the average amount spent differs by spending category D)Reject the null hypothesis,the average amount spent differs by spending category <div style=padding-top: 35px> Refer to Exhibit 13.7.At the 5% significance level,the conclusion of the hypothesis test for factor A is:

A)Do not reject the null hypothesis,cannot conclude the average amount spent differs by spending category
B)Do not reject the null hypothesis,the average amount spent differs by spending category
C)Reject the null hypothesis,cannot conclude the average amount spent differs by spending category
D)Reject the null hypothesis,the average amount spent differs by spending category
Question
Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below. <strong>Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below.   Refer to Exhibit 13.7.At the 5% significance level,the critical value for the hypothesis test about factor B is:</strong> A)2.28 B)2.92 C)3.59 D)4.51 <div style=padding-top: 35px> Refer to Exhibit 13.7.At the 5% significance level,the critical value for the hypothesis test about factor B is:

A)2.28
B)2.92
C)3.59
D)4.51
Question
Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below. <strong>Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below.   Refer to Exhibit 13.7.At the 5% significance level,the critical value for the hypothesis test about factor A is:</strong> A)2.49 B)3.32 C)4.18 D)5.39 <div style=padding-top: 35px> Refer to Exhibit 13.7.At the 5% significance level,the critical value for the hypothesis test about factor A is:

A)2.49
B)3.32
C)4.18
D)5.39
Question
Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.For the interaction,the value of the test statistic is:</strong> A)34.20 B)16.43 C)3.21 D)24 <div style=padding-top: 35px> <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.For the interaction,the value of the test statistic is:</strong> A)34.20 B)16.43 C)3.21 D)24 <div style=padding-top: 35px> Refer to Exhibit 13.8.For the interaction,the value of the test statistic is:

A)34.20
B)16.43
C)3.21
D)24
Question
Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below. <strong>Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below.   Refer to Exhibit 13.7.For factor A,the value of the test statistic is:</strong> A)3.21 B)2 C)16.43 D)3 <div style=padding-top: 35px> Refer to Exhibit 13.7.For factor A,the value of the test statistic is:

A)3.21
B)2
C)16.43
D)3
Question
Exhibit 13.9 Psychology students want to determine if there are differences between the ability of business majors and science majors to solve various types of analytic problems.They conduct an experiment and record the amount of time it takes to complete each analytic problem.The following two-way ANOVA table summarizes their findings. <strong>Exhibit 13.9 Psychology students want to determine if there are differences between the ability of business majors and science majors to solve various types of analytic problems.They conduct an experiment and record the amount of time it takes to complete each analytic problem.The following two-way ANOVA table summarizes their findings.   Refer to Exhibit 13.9.At the 5% significance level,the conclusion for the hypothesis test about the interaction term is:</strong> A)Do not reject the null hypothesis,there is no evidence of an interaction effect between major and problem type B)Reject the null hypothesis,there is no evidence of an interaction effect between major and problem type C)Reject the null hypothesis,there is evidence of an interaction effect between major and problem type D)Do not reject the null hypothesis,there is evidence of an interaction effect between major and problem type <div style=padding-top: 35px> Refer to Exhibit 13.9.At the 5% significance level,the conclusion for the hypothesis test about the interaction term is:

A)Do not reject the null hypothesis,there is no evidence of an interaction effect between major and problem type
B)Reject the null hypothesis,there is no evidence of an interaction effect between major and problem type
C)Reject the null hypothesis,there is evidence of an interaction effect between major and problem type
D)Do not reject the null hypothesis,there is evidence of an interaction effect between major and problem type
Question
Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below. <strong>Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below.   Refer to Exhibit 13.7.Which of the following is the value of MSA?</strong> A)15,623 B)79,930 C)37,170 D)1,321,831 <div style=padding-top: 35px> Refer to Exhibit 13.7.Which of the following is the value of MSA?

A)15,623
B)79,930
C)37,170
D)1,321,831
Question
Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.At the 5% significance level,the conclusion for the hypothesis test about the interaction term is:</strong> A)Do not reject the null hypothesis,there is evidence of an interaction effect between spending category and generation B)Reject the null hypothesis,there is evidence of an interaction effect between spending category and generation C)Reject the null hypothesis,there is no evidence of an interaction effect between spending category and generation D)Do not reject the null hypothesis,there is no evidence of an interaction effect between spending category and generation <div style=padding-top: 35px> <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.At the 5% significance level,the conclusion for the hypothesis test about the interaction term is:</strong> A)Do not reject the null hypothesis,there is evidence of an interaction effect between spending category and generation B)Reject the null hypothesis,there is evidence of an interaction effect between spending category and generation C)Reject the null hypothesis,there is no evidence of an interaction effect between spending category and generation D)Do not reject the null hypothesis,there is no evidence of an interaction effect between spending category and generation <div style=padding-top: 35px> Refer to Exhibit 13.8.At the 5% significance level,the conclusion for the hypothesis test about the interaction term is:

A)Do not reject the null hypothesis,there is evidence of an interaction effect between spending category and generation
B)Reject the null hypothesis,there is evidence of an interaction effect between spending category and generation
C)Reject the null hypothesis,there is no evidence of an interaction effect between spending category and generation
D)Do not reject the null hypothesis,there is no evidence of an interaction effect between spending category and generation
Question
Exhibit 13.9 Psychology students want to determine if there are differences between the ability of business majors and science majors to solve various types of analytic problems.They conduct an experiment and record the amount of time it takes to complete each analytic problem.The following two-way ANOVA table summarizes their findings. <strong>Exhibit 13.9 Psychology students want to determine if there are differences between the ability of business majors and science majors to solve various types of analytic problems.They conduct an experiment and record the amount of time it takes to complete each analytic problem.The following two-way ANOVA table summarizes their findings.   Refer to Exhibit 13.9.The number of different analytic problems the students solved is:</strong> A)1 B)2 C)4 D)5 <div style=padding-top: 35px> Refer to Exhibit 13.9.The number of different analytic problems the students solved is:

A)1
B)2
C)4
D)5
Question
Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.The conclusion for the hypothesis test about the interaction term is:</strong> A)   B)   C)   D)   <div style=padding-top: 35px> <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.The conclusion for the hypothesis test about the interaction term is:</strong> A)   B)   C)   D)   <div style=padding-top: 35px> Refer to Exhibit 13.8.The conclusion for the hypothesis test about the interaction term is:

A) <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.The conclusion for the hypothesis test about the interaction term is:</strong> A)   B)   C)   D)   <div style=padding-top: 35px>
B) <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.The conclusion for the hypothesis test about the interaction term is:</strong> A)   B)   C)   D)   <div style=padding-top: 35px>
C) <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.The conclusion for the hypothesis test about the interaction term is:</strong> A)   B)   C)   D)   <div style=padding-top: 35px>
D) <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.The conclusion for the hypothesis test about the interaction term is:</strong> A)   B)   C)   D)   <div style=padding-top: 35px>
Question
Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below. <strong>Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below.   Refer to Exhibit 13.7.At the 5% significance level,the conclusion of the hypothesis test for Factor B is:</strong> A)Do not reject the null hypothesis,cannot conclude the average amount spent differs by generation B)Do not reject the null hypothesis,the average amount spent differs by generation C)Reject the null hypothesis,cannot conclude the average amount spent differs by generation D)Reject the null hypothesis,the average amount spent differs by generation <div style=padding-top: 35px> Refer to Exhibit 13.7.At the 5% significance level,the conclusion of the hypothesis test for Factor B is:

A)Do not reject the null hypothesis,cannot conclude the average amount spent differs by generation
B)Do not reject the null hypothesis,the average amount spent differs by generation
C)Reject the null hypothesis,cannot conclude the average amount spent differs by generation
D)Reject the null hypothesis,the average amount spent differs by generation
Question
Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below. <strong>Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below.   Refer to Exhibit 13.7.Which of the following is the value of SSB?</strong> A)46,869 B)159,860 C)1,115,101 D)1,321,831 <div style=padding-top: 35px> Refer to Exhibit 13.7.Which of the following is the value of SSB?

A)46,869
B)159,860
C)1,115,101
D)1,321,831
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Deck 13: Analysis of Variance
1
Fisher's 100(1 - α)% confidence interval for the difference between two population means μi - μj is:

A) <strong>Fisher's 100(1 - α)% confidence interval for the difference between two population means μ<sub>i</sub> - μ<sub>j</sub> is:</strong> A)   B)   C)   D)
B) <strong>Fisher's 100(1 - α)% confidence interval for the difference between two population means μ<sub>i</sub> - μ<sub>j</sub> is:</strong> A)   B)   C)   D)
C) <strong>Fisher's 100(1 - α)% confidence interval for the difference between two population means μ<sub>i</sub> - μ<sub>j</sub> is:</strong> A)   B)   C)   D)
D) <strong>Fisher's 100(1 - α)% confidence interval for the difference between two population means μ<sub>i</sub> - μ<sub>j</sub> is:</strong> A)   B)   C)   D)
2
If the amount of variability between treatments is significantly greater than the amount of variability within treatments,then:

A)reject the null hypothesis of equal population means
B)do not reject the null hypothesis of equal population means
C)conclude that the ratio of between-treatments variability to within-treatments variability is significantly less than 1
D)perform further analysis using the two-way ANOVA with interaction
reject the null hypothesis of equal population means
3
If units within each block are randomly assigned to each of the treatments,then the design of the experiment is referred to as a completely randomized design.
False
4
Identify the assumption that is not applicable for a one-way ANOVA test.

A)The populations are normally distributed
B)The population standard deviations are not all equal
C)The samples are selected independently
D)The sample is drawn at random from each population
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5
When using Fisher's least significant difference (LSD)method at some stated significance level,the probability of committing a Type I error increases as the number of:

A)pairwise comparisons decreases
B)pairwise comparisons increases
C)sample size increases
D)treatments decreases
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6
The interaction test is performed before making any conclusions based on the tests for the main effects.
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7
The between-treatments variability is the estimate of σ2 which is based on the variability due to chance.
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8
In general,blocks are the levels at which we hold an integral factor fixed,so that we can measure its contribution to the variation within the samples.
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9
When the null hypothesis is rejected in an ANOVA test,Fisher's least significant difference method is superior to Tukey's honestly significant differences method to determine which population means differ.
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10
Fisher's least difference (LSD)method is applied when the:

A)ANOVA test has not rejected the null hypothesis of equal population means
B)ANOVA test has rejected the null hypothesis of equal population means
C)Two-sample t test is not applicable
D)None of the above
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11
When two factors interact,the effect of one factor on the population mean depends upon the specific value or level present for the other factor.
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12
One-Way ANOVA analyzes the effect of one factor on the population mean.It is based on a:

A)randomized block design
B)completely randomized design
C)factorial design
D)balanced incomplete block design
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13
We use ANOVA to test for differences between population means by examining the amount of variability between the samples relative to the amount of variability within the samples.
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14
ANOVA is a statistical technique used to determine if differences exist between the means of two populations.
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15
One-way ANOVA analyzes the effect of one factor on the population mean and it is based on a completely randomized design.
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16
Which of the following is the correct interpretation of the Fisher's 100(1 - α)% confidence interval for μi - μj?

A)If the interval includes the value zero,the null hypothesis,that H0: μi - μj = 0,is rejected for at α level of significance
B)If the interval does not include the value zero,the null hypothesis,that H0: μi - μj = 0,is rejected at 100(1 - α)% level of significance
C)If the interval does not include the value zero,the null hypothesis,that H0: μi - μj = 0,is rejected at α level of significance
D)If the interval includes the value zero,the null hypothesis,that H0: μi - μj = 0,is rejected at 100(1 - α)% level of significance
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17
When using Fisher's LSD method at some stated significance level α,the probability of committing a Type I error increases as the number of pairwise comparisons increases.
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18
If there are five treatments under study,the number of pairwise comparisons is:

A)15
B)5
C)20
D)10
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19
The variability due to chance,also known as within-treatments variability,is the estimate of σ2 which is based on the:

A)variability of the data across different samples
B)consistency of the data within each sample
C)variability of the data within each sample
D)reliability of the data within each sample
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20
Between-treatments variability is based on a weighted sum of squared differences between the:

A)population variances and the overall mean of the data set
B)sample means and the overall mean of the data set
C)sample variances and the overall mean of the data set
D)population means and the overall mean of the data set
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21
Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625. <strong>Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625.   Refer to Exhibit 13.2.Which of the following is the sum of squared errors?</strong> A)264.29 B)5,285.83 C)18,567.63 D)13,281.79 Refer to Exhibit 13.2.Which of the following is the sum of squared errors?

A)264.29
B)5,285.83
C)18,567.63
D)13,281.79
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22
If units with each block are randomly assigned to each of the treatments,then the design of the experiment is referred to as a:

A)factorial design
B)completely randomized design
C)randomized block design
D)balanced incomplete block design
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23
Tukey's 100(1 - α)% confidence interval for the difference between two population means μi - μj for balanced data is given by ____.

A) <strong>Tukey's 100(1 - α)% confidence interval for the difference between two population means μ<sub>i</sub> - μ<sub>j</sub> for balanced data is given by ____.</strong> A)   B)   C)   D)
B) <strong>Tukey's 100(1 - α)% confidence interval for the difference between two population means μ<sub>i</sub> - μ<sub>j</sub> for balanced data is given by ____.</strong> A)   B)   C)   D)
C) <strong>Tukey's 100(1 - α)% confidence interval for the difference between two population means μ<sub>i</sub> - μ<sub>j</sub> for balanced data is given by ____.</strong> A)   B)   C)   D)
D) <strong>Tukey's 100(1 - α)% confidence interval for the difference between two population means μ<sub>i</sub> - μ<sub>j</sub> for balanced data is given by ____.</strong> A)   B)   C)   D)
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24
Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625. <strong>Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625.   Refer to Exhibit 13.2.At the 5% significance level,the critical value is:</strong> A)2.38 B)3.10 C)3.86 D)4.94 Refer to Exhibit 13.2.At the 5% significance level,the critical value is:

A)2.38
B)3.10
C)3.86
D)4.94
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25
One of the disadvantages of Fisher's least difference (LSD)method is that the probability of committing a:

A)Type II error increases as the number of pairwise comparisons increases.
B)Type I error increases as the number of pairwise comparisons decreases.
C)Type II error increases as the number of pairwise comparisons decreases.
D)Type I error increases as the number of pairwise comparisons increases.
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26
In a two-way ANOVA test,how many null hypotheses are tested?

A)1
B)1 or 2
C)2 or 3
D)More than 3
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27
If the interaction between two factors is not significant,the next tests to be done are:

A)None,the analysis is complete.
B)None,gather more data.
C)Tests about the population means of factor A or factor B using two-way ANOVA without interaction.
D)Tukey's confidence intervals.
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28
Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625. <strong>Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625.   Refer to Exhibit 13.2.The conclusion for the hypothesis test is:</strong> A)Reject the null hypothesis,cannot conclude that not all mean commute times are equal B)Do not reject the null hypothesis,cannot conclude that not all mean commute times are equal C)Reject the null hypothesis,not all mean commute times are equal D)Do not reject the null hypothesis,not all mean commute times are equal Refer to Exhibit 13.2.The conclusion for the hypothesis test is:

A)Reject the null hypothesis,cannot conclude that not all mean commute times are equal
B)Do not reject the null hypothesis,cannot conclude that not all mean commute times are equal
C)Reject the null hypothesis,not all mean commute times are equal
D)Do not reject the null hypothesis,not all mean commute times are equal
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Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625. <strong>Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625.   Refer to Exhibit 13.2.Which of the following is the sum of squares due to treatments?</strong> A)5,285.83 B)13,281.79 C)18,567.63 D)4,427.26 Refer to Exhibit 13.2.Which of the following is the sum of squares due to treatments?

A)5,285.83
B)13,281.79
C)18,567.63
D)4,427.26
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Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625. <strong>Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625.   Refer to Exhibit 13.2.The value of the test statistic is:</strong> A)0.06 B)0.40 C)2.51 D)16.75 Refer to Exhibit 13.2.The value of the test statistic is:

A)0.06
B)0.40
C)2.51
D)16.75
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Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625. <strong>Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625.   Refer to Exhibit 13.2.Based on the sample standard deviation,the one-way ANOVA assumption which is likely not met is:</strong> A)The populations are normally distributed B)The population standard deviations are assumed to be equal C)The samples are independent D)None of the above Refer to Exhibit 13.2.Based on the sample standard deviation,the one-way ANOVA assumption which is likely not met is:

A)The populations are normally distributed
B)The population standard deviations are assumed to be equal
C)The samples are independent
D)None of the above
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Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625. <strong>Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625.   Refer to Exhibit 13.2.The competing hypotheses about the mean commute times are:</strong> A)   B)   C)   D)   Refer to Exhibit 13.2.The competing hypotheses about the mean commute times are:

A) <strong>Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625.   Refer to Exhibit 13.2.The competing hypotheses about the mean commute times are:</strong> A)   B)   C)   D)
B) <strong>Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625.   Refer to Exhibit 13.2.The competing hypotheses about the mean commute times are:</strong> A)   B)   C)   D)
C) <strong>Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625.   Refer to Exhibit 13.2.The competing hypotheses about the mean commute times are:</strong> A)   B)   C)   D)
D) <strong>Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625.   Refer to Exhibit 13.2.The competing hypotheses about the mean commute times are:</strong> A)   B)   C)   D)
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Exhibit 13.1 The following is an incomplete ANOVA table. <strong>Exhibit 13.1 The following is an incomplete ANOVA table.   Refer to Exhibit 13.1.The value of the test statistic is:</strong> A)1.333 B)9.375 C)12.5 D)100 Refer to Exhibit 13.1.The value of the test statistic is:

A)1.333
B)9.375
C)12.5
D)100
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34
Tukey's honestly significant differences (HSD)method ensures that the probability of a Type I error remains fixed irrespective of the number of:

A)pairwise comparisons
B)treatments
C)replications within each treatment
D)replications for each combination of factor A and factor B
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35
Tukey's honestly significant differences (HSD)method uses _____ instead of _____ when compared to Fishers least differences (LSD)method for pairwise comparisons.

A)t values;studentized range values
B)studentized range values;F values
C)F values;t values
D)studentized range values;t values
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Exhibit 13.1 The following is an incomplete ANOVA table. <strong>Exhibit 13.1 The following is an incomplete ANOVA table.   Refer to Exhibit 13.1.The mean square error is:</strong> A)1.333 B)9.375 C)25 D)75 Refer to Exhibit 13.1.The mean square error is:

A)1.333
B)9.375
C)25
D)75
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Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625. <strong>Exhibit 13.2 A researcher with Ministry of Transportation is commissioned to study the drive times to work (one-way)for U.S.cities.The underlying hypothesis is that average commute times are different across cities.To test the hypothesis,the researcher randomly selects six people from each of the four cities and records their one-way commute times to work.Refer to the below data on one-way commute time (in minutes)to work.Note that the grand mean is 36.625.   Refer to Exhibit 13.2.Which of the following is the mean square for treatments?</strong> A)18,567.63 B)13,281.79 C)5,285.83 D)4,427.26 Refer to Exhibit 13.2.Which of the following is the mean square for treatments?

A)18,567.63
B)13,281.79
C)5,285.83
D)4,427.26
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Exhibit 13.1 The following is an incomplete ANOVA table. <strong>Exhibit 13.1 The following is an incomplete ANOVA table.   Refer to exhibit 13.1.The sum of squares due to treatments is:</strong> A)10 B)25 C)75 D)100 Refer to exhibit 13.1.The sum of squares due to treatments is:

A)10
B)25
C)75
D)100
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Exhibit 13.1 The following is an incomplete ANOVA table. <strong>Exhibit 13.1 The following is an incomplete ANOVA table.   Refer to Exhibit 13.1.For the within groups,the degrees of freedom are:</strong> A)6 B)7 C)8 D)9 Refer to Exhibit 13.1.For the within groups,the degrees of freedom are:

A)6
B)7
C)8
D)9
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Exhibit 13.1 The following is an incomplete ANOVA table. <strong>Exhibit 13.1 The following is an incomplete ANOVA table.   Refer to Exhibit 13.1.At the 5% significance level,the critical value is:</strong> A)3.11 B)4.46 C)6.06 D)8.65 Refer to Exhibit 13.1.At the 5% significance level,the critical value is:

A)3.11
B)4.46
C)6.06
D)8.65
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Exhibit 13.4 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Tukey 95% confidence intervals are shown below. <strong>Exhibit 13.4 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Tukey 95% confidence intervals are shown below.   Refer to Exhibit 13.4.How many pairs of cities show a significant difference in average commute times to work?</strong> A)2 B)3 C)4 D)6 Refer to Exhibit 13.4.How many pairs of cities show a significant difference in average commute times to work?

A)2
B)3
C)4
D)6
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Exhibit 13.3 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Fisher 95% confidence intervals are shown below. <strong>Exhibit 13.3 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Fisher 95% confidence intervals are shown below.   Refer to Exhibit 13.3.How many pairs of cities show a significant difference in average commute times to work?</strong> A)2 B)3 C)4 D)6 Refer to Exhibit 13.3.How many pairs of cities show a significant difference in average commute times to work?

A)2
B)3
C)4
D)6
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Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.How many degrees of freedom are there for factors A and B?</strong> A)2,3 B)3,4 C)3,6 D)2,4 <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.How many degrees of freedom are there for factors A and B?</strong> A)2,3 B)3,4 C)3,6 D)2,4 Refer to Exhibit 13.6.How many degrees of freedom are there for factors A and B?

A)2,3
B)3,4
C)3,6
D)2,4
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Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.At the 5% significance level,the conclusion for the hypothesis test about factor A is:</strong> A)Do not reject the null hypothesis,the average mortgage payments differ by income level B)Do not reject the null hypothesis,cannot conclude the average mortgage payments differ by income level C)Reject the null hypothesis,the average mortgage payments differ by income level D)Reject the null hypothesis,cannot conclude the average mortgage payments differ by income level <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.At the 5% significance level,the conclusion for the hypothesis test about factor A is:</strong> A)Do not reject the null hypothesis,the average mortgage payments differ by income level B)Do not reject the null hypothesis,cannot conclude the average mortgage payments differ by income level C)Reject the null hypothesis,the average mortgage payments differ by income level D)Reject the null hypothesis,cannot conclude the average mortgage payments differ by income level Refer to Exhibit 13.6.At the 5% significance level,the conclusion for the hypothesis test about factor A is:

A)Do not reject the null hypothesis,the average mortgage payments differ by income level
B)Do not reject the null hypothesis,cannot conclude the average mortgage payments differ by income level
C)Reject the null hypothesis,the average mortgage payments differ by income level
D)Reject the null hypothesis,cannot conclude the average mortgage payments differ by income level
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Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below. <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.At the 1% significance level,the conclusion for the hypothesis test is:</strong> A)Reject the null hypothesis,not all mean number of crimes are equal B)Do not reject the null hypothesis,not all mean number of crimes are equal C)Reject the null hypothesis,cannot conclude that not all mean number of crimes are equal D)Do not reject the null hypothesis,cannot conclude that not all mean number of crimes are equal <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.At the 1% significance level,the conclusion for the hypothesis test is:</strong> A)Reject the null hypothesis,not all mean number of crimes are equal B)Do not reject the null hypothesis,not all mean number of crimes are equal C)Reject the null hypothesis,cannot conclude that not all mean number of crimes are equal D)Do not reject the null hypothesis,cannot conclude that not all mean number of crimes are equal Refer to Exhibit 13.5.At the 1% significance level,the conclusion for the hypothesis test is:

A)Reject the null hypothesis,not all mean number of crimes are equal
B)Do not reject the null hypothesis,not all mean number of crimes are equal
C)Reject the null hypothesis,cannot conclude that not all mean number of crimes are equal
D)Do not reject the null hypothesis,cannot conclude that not all mean number of crimes are equal
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Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below. <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.The competing hypotheses about the mean crime rates are:</strong> A)   B)   C)   D)   <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.The competing hypotheses about the mean crime rates are:</strong> A)   B)   C)   D)   Refer to Exhibit 13.5.The competing hypotheses about the mean crime rates are:

A) <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.The competing hypotheses about the mean crime rates are:</strong> A)   B)   C)   D)
B) <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.The competing hypotheses about the mean crime rates are:</strong> A)   B)   C)   D)
C) <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.The competing hypotheses about the mean crime rates are:</strong> A)   B)   C)   D)
D) <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.The competing hypotheses about the mean crime rates are:</strong> A)   B)   C)   D)
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Exhibit 13.3 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Fisher 95% confidence intervals are shown below. <strong>Exhibit 13.3 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Fisher 95% confidence intervals are shown below.   Refer to Exhibit 13.3.Which of the following is the   value used to calculate the Fisher 95% confidence intervals?</strong> A)1.725 B)2.086 C)2.080 D)2.090 Refer to Exhibit 13.3.Which of the following is the <strong>Exhibit 13.3 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Fisher 95% confidence intervals are shown below.   Refer to Exhibit 13.3.Which of the following is the   value used to calculate the Fisher 95% confidence intervals?</strong> A)1.725 B)2.086 C)2.080 D)2.090 value used to calculate the Fisher 95% confidence intervals?

A)1.725
B)2.086
C)2.080
D)2.090
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Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.At the 1% significance level,the conclusion for the hypothesis test about factor B is:</strong> A)Do not reject the null hypothesis,the average mortgage payments differ by zonal location B)Do not reject the null hypothesis,cannot conclude the average mortgage payments differ by zonal location C)Reject the null hypothesis,the average mortgage payments differ by zonal location D)Reject the null hypothesis,cannot conclude the average mortgage payments differ by zonal location <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.At the 1% significance level,the conclusion for the hypothesis test about factor B is:</strong> A)Do not reject the null hypothesis,the average mortgage payments differ by zonal location B)Do not reject the null hypothesis,cannot conclude the average mortgage payments differ by zonal location C)Reject the null hypothesis,the average mortgage payments differ by zonal location D)Reject the null hypothesis,cannot conclude the average mortgage payments differ by zonal location Refer to Exhibit 13.6.At the 1% significance level,the conclusion for the hypothesis test about factor B is:

A)Do not reject the null hypothesis,the average mortgage payments differ by zonal location
B)Do not reject the null hypothesis,cannot conclude the average mortgage payments differ by zonal location
C)Reject the null hypothesis,the average mortgage payments differ by zonal location
D)Reject the null hypothesis,cannot conclude the average mortgage payments differ by zonal location
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Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.Which of the following is the value of the test statistic for factor A?</strong> A)4.76 B)5.14 C)9.41 D)32.86 <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.Which of the following is the value of the test statistic for factor A?</strong> A)4.76 B)5.14 C)9.41 D)32.86 Refer to Exhibit 13.6.Which of the following is the value of the test statistic for factor A?

A)4.76
B)5.14
C)9.41
D)32.86
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Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below. <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.At the 1% significance level,the conclusion from Tukey's confidence intervals is:</strong> A)Cannot conclude the mean number of crimes differs for West and East B)Cannot conclude the mean number of crimes differs for West and South C)Cannot conclude the mean number of crimes differs for South and North D)Cannot conclude the mean number of crimes differs for West and North <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.At the 1% significance level,the conclusion from Tukey's confidence intervals is:</strong> A)Cannot conclude the mean number of crimes differs for West and East B)Cannot conclude the mean number of crimes differs for West and South C)Cannot conclude the mean number of crimes differs for South and North D)Cannot conclude the mean number of crimes differs for West and North Refer to Exhibit 13.5.At the 1% significance level,the conclusion from Tukey's confidence intervals is:

A)Cannot conclude the mean number of crimes differs for West and East
B)Cannot conclude the mean number of crimes differs for West and South
C)Cannot conclude the mean number of crimes differs for South and North
D)Cannot conclude the mean number of crimes differs for West and North
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Exhibit 13.3 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Fisher 95% confidence intervals are shown below. <strong>Exhibit 13.3 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Fisher 95% confidence intervals are shown below.   Refer to Exhibit 13.3.Which of these pair of cities shows no significant difference in average commute times to work?</strong> A)Houston,Akron B)Charlotte,Akron C)Charlotte,Tucson D)Houston,Tucson Refer to Exhibit 13.3.Which of these pair of cities shows no significant difference in average commute times to work?

A)Houston,Akron
B)Charlotte,Akron
C)Charlotte,Tucson
D)Houston,Tucson
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Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below. <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.At the 1% significance level,the critical value is:</strong> A)2.38 B)3.10 C)3.86 D)4.94 <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.At the 1% significance level,the critical value is:</strong> A)2.38 B)3.10 C)3.86 D)4.94 Refer to Exhibit 13.5.At the 1% significance level,the critical value is:

A)2.38
B)3.10
C)3.86
D)4.94
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Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.At the 1% significance level,the critical value for the hypothesis test about factor B is:</strong> A)3.29 B)4.76 C)6.60 D)9.78 <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.At the 1% significance level,the critical value for the hypothesis test about factor B is:</strong> A)3.29 B)4.76 C)6.60 D)9.78 Refer to Exhibit 13.6.At the 1% significance level,the critical value for the hypothesis test about factor B is:

A)3.29
B)4.76
C)6.60
D)9.78
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Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.Which of the following is the value of the test statistic for factor B?</strong> A)4.76 B)5.14 C)9.41 D)32.86 <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.Which of the following is the value of the test statistic for factor B?</strong> A)4.76 B)5.14 C)9.41 D)32.86 Refer to Exhibit 13.6.Which of the following is the value of the test statistic for factor B?

A)4.76
B)5.14
C)9.41
D)32.86
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Exhibit 13.4 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Tukey 95% confidence intervals are shown below. <strong>Exhibit 13.4 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Tukey 95% confidence intervals are shown below.   Refer to Exhibit 13.4.Which of these pair of cities shows a significant difference in average commute times to work?</strong> A)Houston,Akron B)Charlotte,Akron C)Charlotte,Tucson D)Akron,Tucson Refer to Exhibit 13.4.Which of these pair of cities shows a significant difference in average commute times to work?

A)Houston,Akron
B)Charlotte,Akron
C)Charlotte,Tucson
D)Akron,Tucson
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Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below. <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.The degrees of freedom for the hypothesis test are:</strong> A)4,20 B)3,23 C)3,20 D)4,23 <strong>Exhibit 13.5 A police chief wants to determine if crime rates are different for four different areas of the city (East(1),West(2),North(3),and South(4)sides),and obtains data on the number of crimes per day in each area.The one-way ANOVA table and Tukey's confidence intervals are shown below.     Refer to Exhibit 13.5.The degrees of freedom for the hypothesis test are:</strong> A)4,20 B)3,23 C)3,20 D)4,23 Refer to Exhibit 13.5.The degrees of freedom for the hypothesis test are:

A)4,20
B)3,23
C)3,20
D)4,23
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Exhibit 13.4 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Tukey 95% confidence intervals are shown below. <strong>Exhibit 13.4 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Tukey 95% confidence intervals are shown below.   Refer to Exhibit 13.4.Which of the following is the studentized range value with α = 0.05 for Tukey's HSD method?</strong> A)5.02 B)3.58 C)3.96 D)4.64 Refer to Exhibit 13.4.Which of the following is the studentized range value with α = 0.05 for Tukey's HSD method?

A)5.02
B)3.58
C)3.96
D)4.64
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Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.At the 5% significance level,the critical value for the hypothesis test about factor A is:</strong> A)3.46 B)5.14 C)7.26 D)10.92 <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.At the 5% significance level,the critical value for the hypothesis test about factor A is:</strong> A)3.46 B)5.14 C)7.26 D)10.92 Refer to Exhibit 13.6.At the 5% significance level,the critical value for the hypothesis test about factor A is:

A)3.46
B)5.14
C)7.26
D)10.92
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Exhibit 13.4 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Tukey 95% confidence intervals are shown below. <strong>Exhibit 13.4 The ANOVA test performed for Exhibit 13.2 determined that not all mean commute times across the four cities are equal.However,it did not indicate which means differed.To find out which population means differ requires further analysis of the direction and the statistical significance of the difference between paired population means.Tukey 95% confidence intervals are shown below.   Refer to Exhibit 13.4.The conclusion of the Tukey confidence intervals is:</strong> A)The mean commute time in Houston is different from the mean commute time in Charlotte,Tucson,and Akron. B)The mean commute time in Charlotte is different from the mean commute time in Houston,Tucson,and Akron. C)The mean commute time in Tucson is different from the mean commute time in Houston,Charlotte,and Akron. D)The mean commute time in Akron is different from the mean time in Houston,Charlotte,and Tucson. Refer to Exhibit 13.4.The conclusion of the Tukey confidence intervals is:

A)The mean commute time in Houston is different from the mean commute time in Charlotte,Tucson,and Akron.
B)The mean commute time in Charlotte is different from the mean commute time in Houston,Tucson,and Akron.
C)The mean commute time in Tucson is different from the mean commute time in Houston,Charlotte,and Akron.
D)The mean commute time in Akron is different from the mean time in Houston,Charlotte,and Tucson.
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Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.Which of the following are the total degrees of freedom?</strong> A)10 B)11 C)12 D)6 <strong>Exhibit 13.6 A researcher wants to understand how annual mortgage payment (in dollars)depends on income level and zonal location using a two-way ANOVA without interaction.The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.6.Which of the following are the total degrees of freedom?</strong> A)10 B)11 C)12 D)6 Refer to Exhibit 13.6.Which of the following are the total degrees of freedom?

A)10
B)11
C)12
D)6
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Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.The value of SSE is:</strong> A)46,869 B)159,860 C)116,767 D)1,321,831 <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.The value of SSE is:</strong> A)46,869 B)159,860 C)116,767 D)1,321,831 Refer to Exhibit 13.8.The value of SSE is:

A)46,869
B)159,860
C)116,767
D)1,321,831
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Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below. <strong>Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below.   Refer to Exhibit 13.7.Which of the following is the value of MSE?</strong> A)15,623 B)79,930 C)37,170 D)1,321,831 Refer to Exhibit 13.7.Which of the following is the value of MSE?

A)15,623
B)79,930
C)37,170
D)1,321,831
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Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.The degrees of freedom for the interaction and the error are:</strong> A)6,24 B)6,30 C)24,6 D)30,6 <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.The degrees of freedom for the interaction and the error are:</strong> A)6,24 B)6,30 C)24,6 D)30,6 Refer to Exhibit 13.8.The degrees of freedom for the interaction and the error are:

A)6,24
B)6,30
C)24,6
D)30,6
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A farmer plants tomato seeds into four different plots.In each plot,there is a different fertilizer treatment that is applied to the soil.After three weeks,he measures the height of each tomato plant from each of the four plots.The data he collects is given shown below. <strong>A farmer plants tomato seeds into four different plots.In each plot,there is a different fertilizer treatment that is applied to the soil.After three weeks,he measures the height of each tomato plant from each of the four plots.The data he collects is given shown below.   </strong> A)Construct an ANOVA table. B)Set up the competing hypothesis to test whether there are some differences in the mean heights between the different plots/fertilizers. C)At the 5% significance level,what is the conclusion to the test? D)Consider the sample standard deviations for each plot.Which of the assumptions might be violated?

A)Construct an ANOVA table.
B)Set up the competing hypothesis to test whether there are some differences in the mean heights between the different plots/fertilizers.
C)At the 5% significance level,what is the conclusion to the test?
D)Consider the sample standard deviations for each plot.Which of the assumptions might be violated?
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Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below. <strong>Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below.   Refer to Exhibit 13.7.How many degrees of freedom are there for factors A and B?</strong> A)2,3 B)3,4 C)3,6 D)2,4 Refer to Exhibit 13.7.How many degrees of freedom are there for factors A and B?

A)2,3
B)3,4
C)3,6
D)2,4
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Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.At the 5% significance level,the critical value for the test about the interaction is:</strong> A)2.04 B)2.51 C)2.99 D)3.67 <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.At the 5% significance level,the critical value for the test about the interaction is:</strong> A)2.04 B)2.51 C)2.99 D)3.67 Refer to Exhibit 13.8.At the 5% significance level,the critical value for the test about the interaction is:

A)2.04
B)2.51
C)2.99
D)3.67
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Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.For the analysis with an interaction between spending category and generation,the first hypothesis test to conduct should be about the:</strong> A)Average spending across spending B)The interaction between spending and generation C)Average spending across generation D)Both the average spending across spending and generation <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.For the analysis with an interaction between spending category and generation,the first hypothesis test to conduct should be about the:</strong> A)Average spending across spending B)The interaction between spending and generation C)Average spending across generation D)Both the average spending across spending and generation Refer to Exhibit 13.8.For the analysis with an interaction between spending category and generation,the first hypothesis test to conduct should be about the:

A)Average spending across spending
B)The interaction between spending and generation
C)Average spending across generation
D)Both the average spending across spending and generation
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Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.The value of MSAB is:</strong> A)983,335 B)116,767 C)159,860 D)166,389 <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.The value of MSAB is:</strong> A)983,335 B)116,767 C)159,860 D)166,389 Refer to Exhibit 13.8.The value of MSAB is:

A)983,335
B)116,767
C)159,860
D)166,389
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Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below. <strong>Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below.   Refer to Exhibit 13.7.At the 5% significance level,the conclusion of the hypothesis test for factor A is:</strong> A)Do not reject the null hypothesis,cannot conclude the average amount spent differs by spending category B)Do not reject the null hypothesis,the average amount spent differs by spending category C)Reject the null hypothesis,cannot conclude the average amount spent differs by spending category D)Reject the null hypothesis,the average amount spent differs by spending category Refer to Exhibit 13.7.At the 5% significance level,the conclusion of the hypothesis test for factor A is:

A)Do not reject the null hypothesis,cannot conclude the average amount spent differs by spending category
B)Do not reject the null hypothesis,the average amount spent differs by spending category
C)Reject the null hypothesis,cannot conclude the average amount spent differs by spending category
D)Reject the null hypothesis,the average amount spent differs by spending category
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Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below. <strong>Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below.   Refer to Exhibit 13.7.At the 5% significance level,the critical value for the hypothesis test about factor B is:</strong> A)2.28 B)2.92 C)3.59 D)4.51 Refer to Exhibit 13.7.At the 5% significance level,the critical value for the hypothesis test about factor B is:

A)2.28
B)2.92
C)3.59
D)4.51
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Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below. <strong>Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below.   Refer to Exhibit 13.7.At the 5% significance level,the critical value for the hypothesis test about factor A is:</strong> A)2.49 B)3.32 C)4.18 D)5.39 Refer to Exhibit 13.7.At the 5% significance level,the critical value for the hypothesis test about factor A is:

A)2.49
B)3.32
C)4.18
D)5.39
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Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.For the interaction,the value of the test statistic is:</strong> A)34.20 B)16.43 C)3.21 D)24 <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.For the interaction,the value of the test statistic is:</strong> A)34.20 B)16.43 C)3.21 D)24 Refer to Exhibit 13.8.For the interaction,the value of the test statistic is:

A)34.20
B)16.43
C)3.21
D)24
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Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below. <strong>Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below.   Refer to Exhibit 13.7.For factor A,the value of the test statistic is:</strong> A)3.21 B)2 C)16.43 D)3 Refer to Exhibit 13.7.For factor A,the value of the test statistic is:

A)3.21
B)2
C)16.43
D)3
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Exhibit 13.9 Psychology students want to determine if there are differences between the ability of business majors and science majors to solve various types of analytic problems.They conduct an experiment and record the amount of time it takes to complete each analytic problem.The following two-way ANOVA table summarizes their findings. <strong>Exhibit 13.9 Psychology students want to determine if there are differences between the ability of business majors and science majors to solve various types of analytic problems.They conduct an experiment and record the amount of time it takes to complete each analytic problem.The following two-way ANOVA table summarizes their findings.   Refer to Exhibit 13.9.At the 5% significance level,the conclusion for the hypothesis test about the interaction term is:</strong> A)Do not reject the null hypothesis,there is no evidence of an interaction effect between major and problem type B)Reject the null hypothesis,there is no evidence of an interaction effect between major and problem type C)Reject the null hypothesis,there is evidence of an interaction effect between major and problem type D)Do not reject the null hypothesis,there is evidence of an interaction effect between major and problem type Refer to Exhibit 13.9.At the 5% significance level,the conclusion for the hypothesis test about the interaction term is:

A)Do not reject the null hypothesis,there is no evidence of an interaction effect between major and problem type
B)Reject the null hypothesis,there is no evidence of an interaction effect between major and problem type
C)Reject the null hypothesis,there is evidence of an interaction effect between major and problem type
D)Do not reject the null hypothesis,there is evidence of an interaction effect between major and problem type
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Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below. <strong>Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below.   Refer to Exhibit 13.7.Which of the following is the value of MSA?</strong> A)15,623 B)79,930 C)37,170 D)1,321,831 Refer to Exhibit 13.7.Which of the following is the value of MSA?

A)15,623
B)79,930
C)37,170
D)1,321,831
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Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.At the 5% significance level,the conclusion for the hypothesis test about the interaction term is:</strong> A)Do not reject the null hypothesis,there is evidence of an interaction effect between spending category and generation B)Reject the null hypothesis,there is evidence of an interaction effect between spending category and generation C)Reject the null hypothesis,there is no evidence of an interaction effect between spending category and generation D)Do not reject the null hypothesis,there is no evidence of an interaction effect between spending category and generation <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.At the 5% significance level,the conclusion for the hypothesis test about the interaction term is:</strong> A)Do not reject the null hypothesis,there is evidence of an interaction effect between spending category and generation B)Reject the null hypothesis,there is evidence of an interaction effect between spending category and generation C)Reject the null hypothesis,there is no evidence of an interaction effect between spending category and generation D)Do not reject the null hypothesis,there is no evidence of an interaction effect between spending category and generation Refer to Exhibit 13.8.At the 5% significance level,the conclusion for the hypothesis test about the interaction term is:

A)Do not reject the null hypothesis,there is evidence of an interaction effect between spending category and generation
B)Reject the null hypothesis,there is evidence of an interaction effect between spending category and generation
C)Reject the null hypothesis,there is no evidence of an interaction effect between spending category and generation
D)Do not reject the null hypothesis,there is no evidence of an interaction effect between spending category and generation
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Exhibit 13.9 Psychology students want to determine if there are differences between the ability of business majors and science majors to solve various types of analytic problems.They conduct an experiment and record the amount of time it takes to complete each analytic problem.The following two-way ANOVA table summarizes their findings. <strong>Exhibit 13.9 Psychology students want to determine if there are differences between the ability of business majors and science majors to solve various types of analytic problems.They conduct an experiment and record the amount of time it takes to complete each analytic problem.The following two-way ANOVA table summarizes their findings.   Refer to Exhibit 13.9.The number of different analytic problems the students solved is:</strong> A)1 B)2 C)4 D)5 Refer to Exhibit 13.9.The number of different analytic problems the students solved is:

A)1
B)2
C)4
D)5
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Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below. <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.The conclusion for the hypothesis test about the interaction term is:</strong> A)   B)   C)   D)   <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.The conclusion for the hypothesis test about the interaction term is:</strong> A)   B)   C)   D)   Refer to Exhibit 13.8.The conclusion for the hypothesis test about the interaction term is:

A) <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.The conclusion for the hypothesis test about the interaction term is:</strong> A)   B)   C)   D)
B) <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.The conclusion for the hypothesis test about the interaction term is:</strong> A)   B)   C)   D)
C) <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.The conclusion for the hypothesis test about the interaction term is:</strong> A)   B)   C)   D)
D) <strong>Exhibit 13.8 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).The data and an incomplete ANOVA table are shown below.     Refer to Exhibit 13.8.The conclusion for the hypothesis test about the interaction term is:</strong> A)   B)   C)   D)
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Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below. <strong>Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below.   Refer to Exhibit 13.7.At the 5% significance level,the conclusion of the hypothesis test for Factor B is:</strong> A)Do not reject the null hypothesis,cannot conclude the average amount spent differs by generation B)Do not reject the null hypothesis,the average amount spent differs by generation C)Reject the null hypothesis,cannot conclude the average amount spent differs by generation D)Reject the null hypothesis,the average amount spent differs by generation Refer to Exhibit 13.7.At the 5% significance level,the conclusion of the hypothesis test for Factor B is:

A)Do not reject the null hypothesis,cannot conclude the average amount spent differs by generation
B)Do not reject the null hypothesis,the average amount spent differs by generation
C)Reject the null hypothesis,cannot conclude the average amount spent differs by generation
D)Reject the null hypothesis,the average amount spent differs by generation
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Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below. <strong>Exhibit 13.7 A market researcher is studying the spending habits of people across age groups.The amount of money spent by each individual is classified by spending category (Dining out,Shopping or Electronics)and generation (Gen-X,Gen-Y,Gen-Z or Baby Boomers).An incomplete ANOVA table are shown below.   Refer to Exhibit 13.7.Which of the following is the value of SSB?</strong> A)46,869 B)159,860 C)1,115,101 D)1,321,831 Refer to Exhibit 13.7.Which of the following is the value of SSB?

A)46,869
B)159,860
C)1,115,101
D)1,321,831
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