TABLE 13-4 The managers of a brokerage firm are interested in finding out if the number of new clients a broker brings into the firm affects the sales generated by the broker. They sample 12 brokers and determine the number of new clients they have enrolled in the last year and their sales amounts in thousands of dollars. These data are presented in the table that follows. $\begin{array}{cccc} \underline {\text { Broker } } & \underline {\text { Clients } }& \underline {\text { Sales }} \\ 1 & 27 & 52 \\ 2 & 11 & 37 \\ 3 & 42 & 64 \\ 4 & 33 & 55 \\ 5 & 15 & 29 \\ 6 & 15 & 34 \\ 7 & 25 & 58 \\ 8 & 36 & 59 \\ 9 & 28 & 44 \\ 10 & 30 & 48 \\ 11 & 17 & 31 \\ 12 & 22 & 38 \end{array}$ -Referring to Table 13-4, the managers of the brokerage firm wanted to test the hypothesis that the population slope was equal to 0. The value of the test statistic is ________.

The answer of TABLE 13-4 The managers of a brokerage firm...

TABLE 13-13 In this era of tough economic conditions, voters increasingly ask the question: "Is the educational achievement level of students dependent on the amount of money the state in which they reside spends on education?" The partial computer output below is the result of using spending per student ($) as the independent variable and composite score which is the sum of the math, science and reading scores as the dependent variable on 35 states that participated in a study. The table includes only partial results. $\begin{array}{|lr|} \hline {\text { Regression Statistics }} \\ \hline \text { Multiple R } & 0.3122 \\ \hline \text { R Square } & 0.0975 \\ \hline \text { Adjusted R } & 0.0701 \\ \text { Square } & \\ \hline \text { Standard } & 26.9122 \\ \text { Error } & \\ \hline \text { Observations } & 35 \\ \hline \end{array}$ $\text { ANOVA }$ $\begin{array} { | l | r | r | r | r | } \hline & { \text { df } } & \text { SS } & M S & F \\ \hline \text { Regression } & 1 & 2581.5759 & & \\ \hline \text { Residual } & & & 724.2674 & \\ \hline \text { Total } & 34 & 26482.4000 & & \\ \hline \end{array}$ $\begin{array} { | l | r | r | r | r | } \hline & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & \text { P-value } \\ \hline \text { Intercept } & 595.540251 & 22.115176 & & \\ \hline \text { Spending per } & & & & \\ \hline \text { Student } ( \mathrm { S } ) & 0.007996 & 0.004235 & & \\ \hline \end{array}$ -Referring to Table 13-13, what is the standard deviation of the composite score around the regression line?

The answer of TABLE 13-13 In this era of tough economic...

TABLE 13-5 The managing partner of an advertising agency believes that his company's sales are related to the industry sales. He uses Microsoft Excel to analyze the last 4 years of quarterly data (i.e., n = 16) with the following results: Regression Statistics $\begin{array} { l r } \text { Multiple R } & 0.802 \\ \text { R Square } & 0.643 \\ \text { Adjusted R Square } & 0.618 \\ \text { Standard Error SYX } & 0.9224 \\ \text { Observations } & 16 \end{array}$ ANOVA $\begin{array}{lrcrcc} & \text { df } & \text { SS } &{\text { MS }} & \text { F } & \text { Sig.F } \\ \text { Regression } & 1 & 21.497 & 21.497 & 25.27 & 0.000 \\ \text { Error } & 14 & 11.912 & 0.851 & & \\ \text { Total } & 15 & 33.409 & & & \end{array}$ $\begin{array} { l r r r r } \underline { \text { Predictor } } & \underline { \text { Coef } } & \underline { \text { StdError } } & \underline { \text { t Stat } } & p \text {-value } \\ \text { Intercept } & 3.962 & 1.440 & 2.75 & 0.016 \\ \text { Industry } & 0.040451 & 0.008048 & 5.03 & 0.000 \end{array}$ Durbin-Watson Statistic $1.59$ -The coefficient of determination (r²)tells you

A) that the coefficient of correlation (r) is larger than 1. B) whether r has any significance. C) that you should not partition the total variation. D) the proportion of total variation that is explained. A) that the coefficient of correlation (r) is larger than 1. B) whether r has any significance. C) that you should not partition the total variation. D) the proportion of total variation that is explained.

TABLE 13-4 The managers of a brokerage firm are interested in finding out if the number of new clients a broker brings into the firm affects the sales generated by the broker. They sample 12 brokers and determine the number of new clients they have enrolled in the last year and their sales amounts in thousands of dollars. These data are presented in the table that follows. $\begin{array}{cccc} \underline {\text { Broker } } & \underline {\text { Clients } }& \underline {\text { Sales }} \\ 1 & 27 & 52 \\ 2 & 11 & 37 \\ 3 & 42 & 64 \\ 4 & 33 & 55 \\ 5 & 15 & 29 \\ 6 & 15 & 34 \\ 7 & 25 & 58 \\ 8 & 36 & 59 \\ 9 & 28 & 44 \\ 10 & 30 & 48 \\ 11 & 17 & 31 \\ 12 & 22 & 38 \end{array}$ -Referring to Table 13-4, the managers of the brokerage firm wanted to test the hypothesis that the number of new clients brought in had a positive impact on the amount of sales generated. For a test with a level of significance of 0.01, the null hypothesis should be rejected if the value of the test statistic is ________.

The answer of TABLE 13-4 The managers of a brokerage firm...

TABLE 13-13 In this era of tough economic conditions, voters increasingly ask the question: "Is the educational achievement level of students dependent on the amount of money the state in which they reside spends on education?" The partial computer output below is the result of using spending per student ($) as the independent variable and composite score which is the sum of the math, science and reading scores as the dependent variable on 35 states that participated in a study. The table includes only partial results. $\begin{array}{|lr|} \hline {\text { Regression Statistics }} \\ \hline \text { Multiple R } & 0.3122 \\ \hline \text { R Square } & 0.0975 \\ \hline \text { Adjusted R } & 0.0701 \\ \text { Square } & \\ \hline \text { Standard } & 26.9122 \\ \text { Error } & \\ \hline \text { Observations } & 35 \\ \hline \end{array}$ $\text { ANOVA }$ $\begin{array} { | l | r | r | r | r | } \hline & { \text { df } } & \text { SS } & M S & F \\ \hline \text { Regression } & 1 & 2581.5759 & & \\ \hline \text { Residual } & & & 724.2674 & \\ \hline \text { Total } & 34 & 26482.4000 & & \\ \hline \end{array}$ $\begin{array} { | l | r | r | r | r | } \hline & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & \text { P-value } \\ \hline \text { Intercept } & 595.540251 & 22.115176 & & \\ \hline \text { Spending per } & & & & \\ \hline \text { Student } ( \mathrm { S } ) & 0.007996 & 0.004235 & & \\ \hline \end{array}$ -Referring to Table 13-13, the value of the measured t test statistic to test whether composite score depends linearly on spending per student is ________.

The answer of TABLE 13-13 In this era of tough economic...

TABLE 13-8 It is believed that GPA (grade point average, based on a four point scale) should have a positive linear relationship with ACT scores. Given below is the Excel output for predicting GPA using ACT scores based a data set of 8 randomly chosen students from a Big-Ten university. \[\text { Regressing GPA on ACT }\] $\begin{array}{lr} \hline{\text { Regression Statistics }} \\ \hline \text { Multiple R } & 0.7598 \\ \hline \text { R Square } & 0.5774 \\ \hline \text { Adjusted R Square } & 0.5069 \\ \hline \text { Standard Error } & 0.2691 \\ \hline \text { Observations } & 8 \\ \hline \end{array}$ $\text { ANOVA }$ $\begin{array}{lllllllr} \hline & d f & {\text { SS }} & \text { MS } & F & {\text { Significance F }} \\ \hline \text { Regression } & 1 & 0.5940 & 0.5940 & 8.1986 & 0.0286 \\ \text { Residual } & 6 & 0.4347 & 0.0724 & & \\ \hline \text { Total } & 7 & 1.0287 & & & \\ \hline \end{array}$ $\begin{array}{|lrrrrrrr} \hline & \text { Coefficients } & \text { Standard Error } & \text { t Stat } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & 0.5681 & 0.9284 & 0.6119 & 0.5630 & -1.7036 & 2.8398 \\ \hline \text { ACT } & 0.1021 & 0.0356 & 2.8633 & 0.0286 & 0.0148 & 0.1895 \\ \hline \end{array}$ -Referring to Table 13-8, the value of the measured (observed)test statistic of the F-test for H?: ?? = 0 vs. H?: ?? ? 0

A) may be negative. B) is always positive. C) is always negative. D) has the same sign as the corresponding t test statistic. A) may be negative. B) is always positive. C) is always negative. D) has the same sign as the corresponding t test statistic.

Exam 13: Simple Linear Regression

TABLE 13-4 The managers of a brokerage firm are interested in finding out if the number of new clients a broker brings into the firm affects the sales generated by the broker. They sample 12 brokers and determine the number of new clients they have enrolled in the last year and their sales amounts in thousands of dollars. These data are presented in the table that follows. 1 27 52 2 11 37 3 42 64 4 33 55 5 15 29 6 15 34 7 25 58 8 36 59 9 28 44 10 30 48 11 17 31 12 22 38 -Referring to Table 13-4, the managers of the brokerage firm wanted to test the hypothesis that the population slope was equal to 0. The value of the test statistic is ________.

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Question 81

TABLE 13-13 In this era of tough economic conditions, voters increasingly ask the question: "Is the educational achievement level of students dependent on the amount of money the state in which they reside spends on education?" The partial computer output below is the result of using spending per student ($) as the independent variable and composite score which is the sum of the math, science and reading scores as the dependent variable on 35 states that participated in a study. The table includes only partial results. Regression Statistics Multiple R 0.3122 R Square 0.0975 Adjusted R 0.0701 Square Standard 26.9122 Error Observations 35 $\text { ANOVA }$ df SS MS F Regression 1 2581.5759 Residual 724.2674 Total 34 26482.4000 Coefficients Standard Error t Stat P-value Intercept 595.540251 22.115176 Spending per Student () 0.007996 0.004235 -Referring to Table 13-13, the degrees of freedom for the F test on whether spending per student affects composite score are ________.

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Question 82

TABLE 13-13 In this era of tough economic conditions, voters increasingly ask the question: "Is the educational achievement level of students dependent on the amount of money the state in which they reside spends on education?" The partial computer output below is the result of using spending per student ($) as the independent variable and composite score which is the sum of the math, science and reading scores as the dependent variable on 35 states that participated in a study. The table includes only partial results. Regression Statistics Multiple R 0.3122 R Square 0.0975 Adjusted R 0.0701 Square Standard 26.9122 Error Observations 35 $\text { ANOVA }$ df SS MS F Regression 1 2581.5759 Residual 724.2674 Total 34 26482.4000 Coefficients Standard Error t Stat P-value Intercept 595.540251 22.115176 Spending per Student () 0.007996 0.004235 -Referring to Table 13-13, what is the standard deviation of the composite score around the regression line?

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Question 83

TABLE 13-10 The management of a chain electronic store would like to develop a model for predicting the weekly sales (in thousands of dollars) for individual stores based on the number of customers who made purchases. A random sample of 12 stores yields the following results: Customers Sales (Thousands of Dollars) 907 11.20 926 11.05 713 8.21 741 9.21 780 9.42 898 10.08 510 6.73 529 7.02 460 6.12 872 9.52 650 7.53 603 7.25 -Referring to Table 13-10, the value of the F test statistic equals the square of the t test statistic when testing whether the number of customers who make purchases is a good predictor for weekly sales.

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Question 84

TABLE 13-3 The director of cooperative education at a state college wants to examine the effect of cooperative education job experience on marketability in the work place. She takes a random sample of 4 students. For these 4, she finds out how many times each had a cooperative education job and how many job offers they received upon graduation. These data are presented in the table below. Student CoopJobs JobOffer 1 1 4 2 2 6 3 1 3 4 0 1 -Referring to Table 13-3, the director of cooperative education wanted to test the hypothesis that the population slope was equal to 3.0. The p-value of the test is between and .

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Question 85

In a simple linear regression problem, r and b₁

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Question 86

Data that exhibit an autocorrelation effect violate the regression assumption of independence.

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Question 87

TABLE 13-3 The director of cooperative education at a state college wants to examine the effect of cooperative education job experience on marketability in the work place. She takes a random sample of 4 students. For these 4, she finds out how many times each had a cooperative education job and how many job offers they received upon graduation. These data are presented in the table below. Student CoopJobs JobOffer 1 1 4 2 2 6 3 1 3 4 0 1 -Referring to Table 13-3, the director of cooperative education wanted to test the hypothesis that the population slope was equal to 0. The p-value of the test is between and .

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Question 88

TABLE 13-11 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed: Regression Statistics Multiple R 0.8691 R Square 0.7554 Adjusted R Square 0.7467 Standard Error 44.4765 Observations 30.0000 $\text { ANOVA }$ df SS MS F Significance F Regression 1 171062.9193 171062.9193 86.4759 0.0000 Residual 28 55388.4309 1978.1582 Total 29 226451.3503 Coefficients Standard Error t Stat P-value Lower 95\% \multicolumn 1 r Upper 95\% Intercept -95.0614 26.9183 -3.5315 0.0015 -150.2009 -39.9218 Download 3.7297 0.4011 9.2992 0.0000 2.9082 4.5513 $TABLE 13-11 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed: \begin{array}{lr} \hline {\text { Regression Statistics }} \\ \hline \text { Multiple R } & 0.8691 \\ \hline \text { R Square } & 0.7554 \\ \hline \text { Adjusted R Square } & 0.7467 \\ \hline \text { Standard Error } & 44.4765 \\ \hline \text { Observations } & 30.0000 \\ \hline \end{array} \text { ANOVA } \begin{array}{lrrrrrr} \hline & \text { df } & {\text { SS }} & {\text { MS }} & \text { F } & \text { Significance } F \\ \hline \text { Regression } & 1 & 171062.9193 & 171062.9193 & 86.4759 & 0.0000 \\ \text { Residual } & 28 & 55388.4309 & 1978.1582 & & \\ \hline \text { Total } & 29 & 226451.3503 & & & \\ \hline \end{array} \begin{array}{lrrrrrr} \hline & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & \text { P-value } & \text { Lower 95\% } & \multicolumn{1}{r}{\text { Upper 95\% }} \\ \hline \text { Intercept } & -95.0614 & 26.9183 & -3.5315 & 0.0015 & -150.2009 & -39.9218 \\ \text { Download } & 3.7297 & 0.4011 & 9.2992 & 0.0000 & 2.9082 & 4.5513 \\ \hline \end{array} -Referring to table 13-11, which of the following is the correct interpretation for the slope coefficient?$ $TABLE 13-11 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed: \begin{array}{lr} \hline {\text { Regression Statistics }} \\ \hline \text { Multiple R } & 0.8691 \\ \hline \text { R Square } & 0.7554 \\ \hline \text { Adjusted R Square } & 0.7467 \\ \hline \text { Standard Error } & 44.4765 \\ \hline \text { Observations } & 30.0000 \\ \hline \end{array} \text { ANOVA } \begin{array}{lrrrrrr} \hline & \text { df } & {\text { SS }} & {\text { MS }} & \text { F } & \text { Significance } F \\ \hline \text { Regression } & 1 & 171062.9193 & 171062.9193 & 86.4759 & 0.0000 \\ \text { Residual } & 28 & 55388.4309 & 1978.1582 & & \\ \hline \text { Total } & 29 & 226451.3503 & & & \\ \hline \end{array} \begin{array}{lrrrrrr} \hline & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & \text { P-value } & \text { Lower 95\% } & \multicolumn{1}{r}{\text { Upper 95\% }} \\ \hline \text { Intercept } & -95.0614 & 26.9183 & -3.5315 & 0.0015 & -150.2009 & -39.9218 \\ \text { Download } & 3.7297 & 0.4011 & 9.2992 & 0.0000 & 2.9082 & 4.5513 \\ \hline \end{array} -Referring to table 13-11, which of the following is the correct interpretation for the slope coefficient?$ -Referring to table 13-11, which of the following is the correct interpretation for the slope coefficient?

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Question 89

TABLE 13-12 The manager of the purchasing department of a large saving and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output: Regression Statistics Multiple R 0.9447 R Square 0.8924 Adjusted R 0.8886 Square Standard 0.3342 Error Observations 30 $\text { ANOVA }$ df SS MS F Significance F Regression 1 25.9438 25.9438 232.2200 4.3946-15 Residual 28 3.1282 0.1117 Total 29 29.072 Coefficients Standard Error t Stat P -value Lower 95\% Upper 95\% Intercept 0.4024 0.1236 3.2559 0.0030 0.1492 0.6555 Applications Recorded 0.0126 0.0008 15.2388 4.3946-15 0.0109 0.0143 Note: 4.3946E-15 is 4.394610^-15 $TABLE 13-12 The manager of the purchasing department of a large saving and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output: \begin{array}{lr} \hline{\text { Regression Statistics }} \\ \hline \text { Multiple R } & 0.9447 \\ \hline \text { R Square } & 0.8924 \\ \hline \text { Adjusted R } & 0.8886 \\ \text { Square } & \\ \text { Standard } & 0.3342 \\ \text { Error } & \\ \text { Observations } & 30 \\ \hline \end{array} \text { ANOVA } \begin{array} { | l | r | r | r | r | r | r | } \hline & { \text { df } } & { \text { SS } } &MS&F& { \text { Significance } } \\ & & & & & F \\ \hline \text { Regression } & 1 & 25.9438 & 25.9438 & 232.2200 & 4.3946 \mathrm { E } - 15 \\ \hline \text { Residual } & 28 & 3.1282 & 0.1117 & & \\ \hline \text { Total } & 29 & 29.072 & & & & \\ \hline \end{array} \begin{array} { | l | r | r | r | r | r | r | } \hline & \text { Coefficients } & \begin{array} { c } \text { Standard } \\ \text { Error } \end{array} & t \text { Stat } & P \text {-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & 0.4024 & 0.1236 & 3.2559 & 0.0030 & 0.1492 & 0.6555 \\ \hline \begin{array} { l } \text { Applications } \\ \text { Recorded } \end{array} & 0.0126 & 0.0008 & 15.2388 & 4.3946 \mathrm { E } - 15 & 0.0109 & 0.0143 \\ \hline \end{array} Note: 4.3946E-15 is 4.394610^-15 -Referring to Table 13-12, the degrees of freedom for the t test on whether the number of loan applications recorded affects the amount of time are$ $TABLE 13-12 The manager of the purchasing department of a large saving and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output: \begin{array}{lr} \hline{\text { Regression Statistics }} \\ \hline \text { Multiple R } & 0.9447 \\ \hline \text { R Square } & 0.8924 \\ \hline \text { Adjusted R } & 0.8886 \\ \text { Square } & \\ \text { Standard } & 0.3342 \\ \text { Error } & \\ \text { Observations } & 30 \\ \hline \end{array} \text { ANOVA } \begin{array} { | l | r | r | r | r | r | r | } \hline & { \text { df } } & { \text { SS } } &MS&F& { \text { Significance } } \\ & & & & & F \\ \hline \text { Regression } & 1 & 25.9438 & 25.9438 & 232.2200 & 4.3946 \mathrm { E } - 15 \\ \hline \text { Residual } & 28 & 3.1282 & 0.1117 & & \\ \hline \text { Total } & 29 & 29.072 & & & & \\ \hline \end{array} \begin{array} { | l | r | r | r | r | r | r | } \hline & \text { Coefficients } & \begin{array} { c } \text { Standard } \\ \text { Error } \end{array} & t \text { Stat } & P \text {-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & 0.4024 & 0.1236 & 3.2559 & 0.0030 & 0.1492 & 0.6555 \\ \hline \begin{array} { l } \text { Applications } \\ \text { Recorded } \end{array} & 0.0126 & 0.0008 & 15.2388 & 4.3946 \mathrm { E } - 15 & 0.0109 & 0.0143 \\ \hline \end{array} Note: 4.3946E-15 is 4.3946*10^-15 -Referring to Table 13-12, the degrees of freedom for the t test on whether the number of loan applications recorded affects the amount of time are$ -Referring to Table 13-12, the degrees of freedom for the t test on whether the number of loan applications recorded affects the amount of time are

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Question 90

TABLE 13-4 The managers of a brokerage firm are interested in finding out if the number of new clients a broker brings into the firm affects the sales generated by the broker. They sample 12 brokers and determine the number of new clients they have enrolled in the last year and their sales amounts in thousands of dollars. These data are presented in the table that follows. 1 27 52 2 11 37 3 42 64 4 33 55 5 15 29 6 15 34 7 25 58 8 36 59 9 28 44 10 30 48 11 17 31 12 22 38 -Referring to Table 13-4, the least squares estimate of the Y-intercept is ________.

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Question 91

TABLE 13-12 The manager of the purchasing department of a large saving and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output: Regression Statistics Multiple R 0.9447 R Square 0.8924 Adjusted R 0.8886 Square Standard 0.3342 Error Observations 30 $\text { ANOVA }$ df SS MS F Significance F Regression 1 25.9438 25.9438 232.2200 4.3946-15 Residual 28 3.1282 0.1117 Total 29 29.072 Coefficients Standard Error t Stat P -value Lower 95\% Upper 95\% Intercept 0.4024 0.1236 3.2559 0.0030 0.1492 0.6555 Applications Recorded 0.0126 0.0008 15.2388 4.3946-15 0.0109 0.0143 Note: 4.3946E-15 is 4.394610^-15 $TABLE 13-12 The manager of the purchasing department of a large saving and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output: \begin{array}{lr} \hline{\text { Regression Statistics }} \\ \hline \text { Multiple R } & 0.9447 \\ \hline \text { R Square } & 0.8924 \\ \hline \text { Adjusted R } & 0.8886 \\ \text { Square } & \\ \text { Standard } & 0.3342 \\ \text { Error } & \\ \text { Observations } & 30 \\ \hline \end{array} \text { ANOVA } \begin{array} { | l | r | r | r | r | r | r | } \hline & { \text { df } } & { \text { SS } } &MS&F& { \text { Significance } } \\ & & & & & F \\ \hline \text { Regression } & 1 & 25.9438 & 25.9438 & 232.2200 & 4.3946 \mathrm { E } - 15 \\ \hline \text { Residual } & 28 & 3.1282 & 0.1117 & & \\ \hline \text { Total } & 29 & 29.072 & & & & \\ \hline \end{array} \begin{array} { | l | r | r | r | r | r | r | } \hline & \text { Coefficients } & \begin{array} { c } \text { Standard } \\ \text { Error } \end{array} & t \text { Stat } & P \text {-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & 0.4024 & 0.1236 & 3.2559 & 0.0030 & 0.1492 & 0.6555 \\ \hline \begin{array} { l } \text { Applications } \\ \text { Recorded } \end{array} & 0.0126 & 0.0008 & 15.2388 & 4.3946 \mathrm { E } - 15 & 0.0109 & 0.0143 \\ \hline \end{array} Note: 4.3946E-15 is 4.394610^-15 -Referring to Table 13-12, the degrees of freedom for the F test on whether the number of load applications recorded affects the amount of time are$ $TABLE 13-12 The manager of the purchasing department of a large saving and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output: \begin{array}{lr} \hline{\text { Regression Statistics }} \\ \hline \text { Multiple R } & 0.9447 \\ \hline \text { R Square } & 0.8924 \\ \hline \text { Adjusted R } & 0.8886 \\ \text { Square } & \\ \text { Standard } & 0.3342 \\ \text { Error } & \\ \text { Observations } & 30 \\ \hline \end{array} \text { ANOVA } \begin{array} { | l | r | r | r | r | r | r | } \hline & { \text { df } } & { \text { SS } } &MS&F& { \text { Significance } } \\ & & & & & F \\ \hline \text { Regression } & 1 & 25.9438 & 25.9438 & 232.2200 & 4.3946 \mathrm { E } - 15 \\ \hline \text { Residual } & 28 & 3.1282 & 0.1117 & & \\ \hline \text { Total } & 29 & 29.072 & & & & \\ \hline \end{array} \begin{array} { | l | r | r | r | r | r | r | } \hline & \text { Coefficients } & \begin{array} { c } \text { Standard } \\ \text { Error } \end{array} & t \text { Stat } & P \text {-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & 0.4024 & 0.1236 & 3.2559 & 0.0030 & 0.1492 & 0.6555 \\ \hline \begin{array} { l } \text { Applications } \\ \text { Recorded } \end{array} & 0.0126 & 0.0008 & 15.2388 & 4.3946 \mathrm { E } - 15 & 0.0109 & 0.0143 \\ \hline \end{array} Note: 4.3946E-15 is 4.3946*10^-15 -Referring to Table 13-12, the degrees of freedom for the F test on whether the number of load applications recorded affects the amount of time are$ -Referring to Table 13-12, the degrees of freedom for the F test on whether the number of load applications recorded affects the amount of time are

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Question 92

TABLE 13-3 The director of cooperative education at a state college wants to examine the effect of cooperative education job experience on marketability in the work place. She takes a random sample of 4 students. For these 4, she finds out how many times each had a cooperative education job and how many job offers they received upon graduation. These data are presented in the table below. Student CoopJobs JobOffer 1 1 4 2 2 6 3 1 3 4 0 1 -Referring to Table 13-3, the standard error of estimate is ________.

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Question 93

In performing a regression analysis involving two numerical variables, you are assuming

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Question 94

TABLE 13-5 The managing partner of an advertising agency believes that his company's sales are related to the industry sales. He uses Microsoft Excel to analyze the last 4 years of quarterly data (i.e., n = 16) with the following results: Regression Statistics Multiple R 0.802 R Square 0.643 Adjusted R Square 0.618 Standard Error SYX 0.9224 Observations 16 ANOVA df SS MS F Sig.F Regression 1 21.497 21.497 25.27 0.000 Error 14 11.912 0.851 Total 15 33.409 p -value Intercept 3.962 1.440 2.75 0.016 Industry 0.040451 0.008048 5.03 0.000 Durbin-Watson Statistic $1.59$ -The coefficient of determination (r²)tells you

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Question 95

TABLE 13-12 The manager of the purchasing department of a large saving and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output: Regression Statistics Multiple R 0.9447 R Square 0.8924 Adjusted R 0.8886 Square Standard 0.3342 Error Observations 30 $\text { ANOVA }$ df SS MS F Significance F Regression 1 25.9438 25.9438 232.2200 4.3946-15 Residual 28 3.1282 0.1117 Total 29 29.072 Coefficients Standard Error t Stat P -value Lower 95\% Upper 95\% Intercept 0.4024 0.1236 3.2559 0.0030 0.1492 0.6555 Applications Recorded 0.0126 0.0008 15.2388 4.3946-15 0.0109 0.0143 Note: 4.3946E-15 is 4.394610^-15 $TABLE 13-12 The manager of the purchasing department of a large saving and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output: \begin{array}{lr} \hline{\text { Regression Statistics }} \\ \hline \text { Multiple R } & 0.9447 \\ \hline \text { R Square } & 0.8924 \\ \hline \text { Adjusted R } & 0.8886 \\ \text { Square } & \\ \text { Standard } & 0.3342 \\ \text { Error } & \\ \text { Observations } & 30 \\ \hline \end{array} \text { ANOVA } \begin{array} { | l | r | r | r | r | r | r | } \hline & { \text { df } } & { \text { SS } } &MS&F& { \text { Significance } } \\ & & & & & F \\ \hline \text { Regression } & 1 & 25.9438 & 25.9438 & 232.2200 & 4.3946 \mathrm { E } - 15 \\ \hline \text { Residual } & 28 & 3.1282 & 0.1117 & & \\ \hline \text { Total } & 29 & 29.072 & & & & \\ \hline \end{array} \begin{array} { | l | r | r | r | r | r | r | } \hline & \text { Coefficients } & \begin{array} { c } \text { Standard } \\ \text { Error } \end{array} & t \text { Stat } & P \text {-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & 0.4024 & 0.1236 & 3.2559 & 0.0030 & 0.1492 & 0.6555 \\ \hline \begin{array} { l } \text { Applications } \\ \text { Recorded } \end{array} & 0.0126 & 0.0008 & 15.2388 & 4.3946 \mathrm { E } - 15 & 0.0109 & 0.0143 \\ \hline \end{array} Note: 4.3946E-15 is 4.394610^-15 -Referring to Table 13-12, the error sum of squares (SSE)of the above regression is$ $TABLE 13-12 The manager of the purchasing department of a large saving and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output: \begin{array}{lr} \hline{\text { Regression Statistics }} \\ \hline \text { Multiple R } & 0.9447 \\ \hline \text { R Square } & 0.8924 \\ \hline \text { Adjusted R } & 0.8886 \\ \text { Square } & \\ \text { Standard } & 0.3342 \\ \text { Error } & \\ \text { Observations } & 30 \\ \hline \end{array} \text { ANOVA } \begin{array} { | l | r | r | r | r | r | r | } \hline & { \text { df } } & { \text { SS } } &MS&F& { \text { Significance } } \\ & & & & & F \\ \hline \text { Regression } & 1 & 25.9438 & 25.9438 & 232.2200 & 4.3946 \mathrm { E } - 15 \\ \hline \text { Residual } & 28 & 3.1282 & 0.1117 & & \\ \hline \text { Total } & 29 & 29.072 & & & & \\ \hline \end{array} \begin{array} { | l | r | r | r | r | r | r | } \hline & \text { Coefficients } & \begin{array} { c } \text { Standard } \\ \text { Error } \end{array} & t \text { Stat } & P \text {-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & 0.4024 & 0.1236 & 3.2559 & 0.0030 & 0.1492 & 0.6555 \\ \hline \begin{array} { l } \text { Applications } \\ \text { Recorded } \end{array} & 0.0126 & 0.0008 & 15.2388 & 4.3946 \mathrm { E } - 15 & 0.0109 & 0.0143 \\ \hline \end{array} Note: 4.3946E-15 is 4.3946*10^-15 -Referring to Table 13-12, the error sum of squares (SSE)of the above regression is$ -Referring to Table 13-12, the error sum of squares (SSE)of the above regression is

(Multiple Choice)

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Question 96

TABLE 13-4 The managers of a brokerage firm are interested in finding out if the number of new clients a broker brings into the firm affects the sales generated by the broker. They sample 12 brokers and determine the number of new clients they have enrolled in the last year and their sales amounts in thousands of dollars. These data are presented in the table that follows. 1 27 52 2 11 37 3 42 64 4 33 55 5 15 29 6 15 34 7 25 58 8 36 59 9 28 44 10 30 48 11 17 31 12 22 38 -Referring to Table 13-4, the managers of the brokerage firm wanted to test the hypothesis that the number of new clients brought in had a positive impact on the amount of sales generated. For a test with a level of significance of 0.01, the null hypothesis should be rejected if the value of the test statistic is ________.

(Short Answer)

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Question 97

TABLE 13-13 In this era of tough economic conditions, voters increasingly ask the question: "Is the educational achievement level of students dependent on the amount of money the state in which they reside spends on education?" The partial computer output below is the result of using spending per student ($) as the independent variable and composite score which is the sum of the math, science and reading scores as the dependent variable on 35 states that participated in a study. The table includes only partial results. Regression Statistics Multiple R 0.3122 R Square 0.0975 Adjusted R 0.0701 Square Standard 26.9122 Error Observations 35 $\text { ANOVA }$ df SS MS F Regression 1 2581.5759 Residual 724.2674 Total 34 26482.4000 Coefficients Standard Error t Stat P-value Intercept 595.540251 22.115176 Spending per Student () 0.007996 0.004235 -Referring to Table 13-13, the value of the measured t test statistic to test whether composite score depends linearly on spending per student is ________.

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Question 98

Based on the residual plot below, you will conclude that there might be a violation of which of the following assumptions.

(Multiple Choice)

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Question 99

TABLE 13-8 It is believed that GPA (grade point average, based on a four point scale) should have a positive linear relationship with ACT scores. Given below is the Excel output for predicting GPA using ACT scores based a data set of 8 randomly chosen students from a Big-Ten university. $\text { Regressing GPA on ACT }$ Regression Statistics Multiple R 0.7598 R Square 0.5774 Adjusted R Square 0.5069 Standard Error 0.2691 Observations 8 $\text { ANOVA }$ df SS MS F Significance F Regression 1 0.5940 0.5940 8.1986 0.0286 Residual 6 0.4347 0.0724 Total 7 1.0287 Coefficients Standard Error t Stat P-value Lower 95\% Upper 95\% Intercept 0.5681 0.9284 0.6119 0.5630 -1.7036 2.8398 ACT 0.1021 0.0356 2.8633 0.0286 0.0148 0.1895 -Referring to Table 13-8, the value of the measured (observed)test statistic of the F-test for H?: ?? = 0 vs. H?: ?? ? 0

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Question 100

Showing 81 - 100 of 213

In a simple linear regression problem, r and b₁

Data that exhibit an autocorrelation effect violate the regression assumption of independence.

In performing a regression analysis involving two numerical variables, you are assuming

Based on the residual plot below, you will conclude that there might be a violation of which of the following assumptions.

Introduction

Organizing and Visualizing Data

Numerical Descriptive Measures

Basic Probability

Discrete Probability Distributions

The Normal Distribution and Other Continuous Distributions

Sampling and Sampling Distributions

Confidence Interval Estimation

Fundamentals of Hypothesis Testing: One-Sample Tests

Two-Sample Tests

Analysis of Variance

Chi-Square Tests and Nonparametric Tests

Introduction to Multiple Regression

Multiple Regression Model Building

Time-Series Forecasting

Statistical Applications in Quality Management

A Roadmap for Analyzing Data

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