Question 1

Consider Scatterplots 1 and 2 with fitted regression lines shown below. Which of the following statements is true?

Accepted Answer

A)  The standard error of the regression slope is smaller in scatterplot 1. 
B)  The relationship between x and y is stronger in scatterplot 1. 
C)  The standard error of the regression slope is smaller in scatterplot 2. 
D)  The estimated slope is negative in scatterplot 2. 
E)  The estimated intercept is negative in scatterplot 1. 
A)  The standard error of the regression slope is smaller in scatterplot 1. 
B)  The relationship between x and y is stronger in scatterplot 1. 
C)  The standard error of the regression slope is smaller in scatterplot 2. 
D)  The estimated slope is negative in scatterplot 2. 
E)  The estimated intercept is negative in scatterplot 1.

Question 2

When using a plot of residuals (y-axis) vs. fitted value of the dependent variable, a plot with no pattern indicates that the:

Accepted Answer

A)  nearly normal condition is satisfied 
B)  nearly normal condition is not satisfied 
C)  equal spread condition is satisfied 
D)  linearity condition is not satisfied 
E)  independence condition is not satisfied 
A)  nearly normal condition is satisfied 
B)  nearly normal condition is not satisfied 
C)  equal spread condition is satisfied 
D)  linearity condition is not satisfied 
E)  independence condition is not satisfied

Question 3

Use the following for questions 
A sales manager was interested in determining if there is a relationship between college GPA and sales performance among salespeople hired within the last year. A sample of recently hired salespeople was selected and college GPA and the number of units sold last month recorded. Below are the scatterplot, regression results, and residual plots for these data. The regression equation is
Units Sold $= - 0.48 + 7.42$ GPA
$\begin{array} { l r r r r } \text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \ \text { Constant } & - 0.484 & 3.256 & - 0.15 & 0.884 \ \text { GPA } & 7.423 & 1.044 & 7.11 & 0.000 \end{array}$
$\mathrm { S } = 1.57429 \quad \mathrm { R } - \mathrm { Sq } = 78.3 \% \mathrm { \circ } \quad \mathrm { R } - \mathrm { Sq } ( \mathrm { adj } ) = 76.8 \%$
Analysis of Variance
$\begin{array} { l r r r r r } \text { Source } & \text { DF } & \text { SS } & \text { MS } & \text { F } & \text { P } \ \text { Regression } & 1 & 125.30 & 125.30 & 50.56 & 0.000 \ \text { Residual Error } & 14 & 34.70 & 2.48 & & \ \text { Total } & 15 & 160.00 & & & \end{array}$ Answer:  
-Test the hypotheses about the slope of the regression line. Give the appropriate test
statistic, associated P-value, and conclusion in terms of the problem.

Accepted Answer

The test for regression slope results in

Question 4

A sample of recently trained line workers was selected to determine if there is a relationship between the number of hours of training time received by production line Workers and the time it took (in minutes) for them to trouble shoot their last process Problem were captured. Using the regression output is shown below, what conclusion Should be made at α = .05? The regression equation is Trouble Shooting $= 30.7 - 1.84$ Training
$\begin{array} { l r r r r } \text { Predictor } & \text { Coef } & \text { SE Coef } & T & \mathrm {~P} \ \text { Constant } & 30.729 & 1.023 & 30.03 & 0.000 \ \text { Training } & - 1.8360 & 0.1376 & - 13.35 & 0.000 \end{array}$
$$\mathrm { S } = 1.43588 \quad \mathrm { R } - \mathrm { Sq } = 93.2 \% \quad \mathrm { R } - \mathrm { Sq } ( \mathrm { adj } ) = 92.7 \%$$

Accepted Answer

A)  reject the null hypothesis, there is a significant relationship between amount of
Training received and troubleshooting time 
B)  do not reject the null hypothesis, , there is a significant relationship between
Amount of training received and troubleshooting time 
C)  reject the null hypothesis, there is no significant relationship between amount of
Training received and troubleshooting time 
D)  do not reject the null hypothesis, there is no significant relationship between
Amount of training received and troubleshooting time 
E)  None of these. 
A)  reject the null hypothesis, there is a significant relationship between amount of
Training received and troubleshooting time 
B)  do not reject the null hypothesis, , there is a significant relationship between
Amount of training received and troubleshooting time 
C)  reject the null hypothesis, there is no significant relationship between amount of
Training received and troubleshooting time 
D)  do not reject the null hypothesis, there is no significant relationship between
Amount of training received and troubleshooting time 
E)  None of these.

Question 5

The following plots show (1) world population (millions) plotted against 5-year intervals from 1950 through 2000 and (2) residual vs. fitted value for a linear regression model estimated to describe the trend in world population over time. Based on these plots, would you consider this model appropriate? Explain.

Accepted Answer

. @#IMG-DLM&

The scatterplot (1) appearg gtraight

Question 6

Use the following for questions 
A sales manager was interested in determining if there is a relationship between college GPA and sales performance among salespeople hired within the last year. A sample of recently hired salespeople was selected and college GPA and the number of units sold last month recorded. Below are the scatterplot, regression results, and residual plots for these data. The regression equation is
Units Sold $= - 0.48 + 7.42$ GPA
$\begin{array} { l r r r r } \text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \ \text { Constant } & - 0.484 & 3.256 & - 0.15 & 0.884 \ \text { GPA } & 7.423 & 1.044 & 7.11 & 0.000 \end{array}$
$\mathrm { S } = 1.57429 \quad \mathrm { R } - \mathrm { Sq } = 78.3 \% \mathrm { \circ } \quad \mathrm { R } - \mathrm { Sq } ( \mathrm { adj } ) = 76.8 \%$
Analysis of Variance
$\begin{array} { l r r r r r } \text { Source } & \text { DF } & \text { SS } & \text { MS } & \text { F } & \text { P } \ \text { Regression } & 1 & 125.30 & 125.30 & 50.56 & 0.000 \ \text { Residual Error } & 14 & 34.70 & 2.48 & & \ \text { Total } & 15 & 160.00 & & & \end{array}$ Answer:  
-What percentage of the variability in sales performance (units sold per month) can be
accounted for by college GPA?

Accepted Answer

GPA accounts for @#LAT-DLM& of

Question 7

Use the following for questions 
A sales manager was interested in determining if there is a relationship between college GPA and sales performance among salespeople hired within the last year. A sample of recently hired salespeople was selected and college GPA and the number of units sold last month recorded. Below are the scatterplot, regression results, and residual plots for these data. The regression equation is
Units Sold $= - 0.48 + 7.42$ GPA
$\begin{array} { l r r r r } \text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \ \text { Constant } & - 0.484 & 3.256 & - 0.15 & 0.884 \ \text { GPA } & 7.423 & 1.044 & 7.11 & 0.000 \end{array}$
$\mathrm { S } = 1.57429 \quad \mathrm { R } - \mathrm { Sq } = 78.3 \% \mathrm { \circ } \quad \mathrm { R } - \mathrm { Sq } ( \mathrm { adj } ) = 76.8 \%$
Analysis of Variance
$\begin{array} { l r r r r r } \text { Source } & \text { DF } & \text { SS } & \text { MS } & \text { F } & \text { P } \ \text { Regression } & 1 & 125.30 & 125.30 & 50.56 & 0.000 \ \text { Residual Error } & 14 & 34.70 & 2.48 & & \ \text { Total } & 15 & 160.00 & & & \end{array}$ Answer:  
-Predict the units sold per month for a new hire whose college GPA is 3.00.

Accepted Answer

The answer of Use the following for questions 
A sales...

Question 8

The number of hours of training time received by employees and the time it took (in minutes) for them to trouble shoot their last process problem was estimated using a
Regression equation. The 95% prediction interval for trouble shooting time with 8
Hours of training was determined to be 12.822 to 19.261. The correct interpretation is

Accepted Answer

A)  We can be 95% confident that the trouble shooting time by a particular line
Worker who received 8 hours of training will be between 12.822 and 19.261
Minutes. 
B)  We can be 95% confident that the average trouble shooting time by line workers
Receiving 8 hours of training is between 12.822 and 19.261 minutes. 
C)  The troubleshooting time by a line worker who received 8 hours of training will
Be between 12.822 and 19.261 minutes 95% of the time. 
D)  95% of the time the average troubleshooting time is between 12.822 and 19.261
Minutes. 
E)  We can be 95% confident that troubleshooting times will be between 12.822 and 19.261 minutes. 
A)  We can be 95% confident that the trouble shooting time by a particular line
Worker who received 8 hours of training will be between 12.822 and 19.261
Minutes. 
B)  We can be 95% confident that the average trouble shooting time by line workers
Receiving 8 hours of training is between 12.822 and 19.261 minutes. 
C)  The troubleshooting time by a line worker who received 8 hours of training will
Be between 12.822 and 19.261 minutes 95% of the time. 
D)  95% of the time the average troubleshooting time is between 12.822 and 19.261
Minutes. 
E)  We can be 95% confident that troubleshooting times will be between 12.822 and 19.261 minutes.

Question 9

As the carbon content in steel increases, its ductility tends to decrease. A researcher at a steel company measures carbon content and ductility for a sample of 15 types of
Steel. According to the output provided below, the standard error of the regression
Slope is The regression equation is
Ductility $= 7.67 - 3.30$ Carbon Content
$\begin{array} { l r r r r } \text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \ \text { Constant } & 7.671 & 1.507 & 5.09 & 0.000 \ \text { Carbon Content } & - 3.296 & 1.097 & - 3.01 & 0.010 \end{array}$
$$S = 2.36317 \quad R - S q = 41.0 \% \quad R - S q ( a d j ) = 36.5 \%$$

Accepted Answer

A)  -3.296 
B)  +2.363 
C)  +1.097 
D)  +1.507 
E)  +5.090 
A)  -3.296 
B)  +2.363 
C)  +1.097 
D)  +1.507 
E)  +5.090

Question 10

Data on labor productivity and unit labor costs were obtained for the retail industry from 1987 through 2006 (Bureau of Labor Statistics). A regression was estimated to describe the linear relationship between the two variables. Based on the plot of residuals versus predicted values, is the linear model appropriate? Explain.

Accepted Answer

@#IMG-DLM&
The residual plot shows a cle

Question 11

Use the following for questions 
An operations manager was interested in determining if there is a relationship between the amount of training received by production line workers and the time it takes for them to troubleshoot a process problem. A sample of recently trained line workers was selected. The number of hours of training time received and the time it took (in minutes) for them to troubleshoot their last process problem were captured. Below are the scatterplot, regression results, and residual plots for these data. The regression equation is
Trouble Shooting $= 30.7 - 1.84$ Training
$\begin{array} { l r r r r } \text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \ \text { Constant } & 30.729 & 1.023 & 30.03 & 0.000 \ \text { Training } & - 1.8360 & 0.1376 & - 13.35 & 0.000 \end{array}$
$$S = 1.43588 \quad \mathrm { R } - \mathrm { Sq } = 93.2 \text { \% } \quad \mathrm { R } - \mathrm { Sq } ( \mathrm { adj } ) = 92.7 \text { \% }$$
Analysis of Variance
$\begin{array} { l r r r r r } \text { Source } & \text { DF } & \text { SS } & \text { MS } & \text { F } & \text { P } \ \text { Regression } & 1 & 367.20 & 367.20 & 178.10 & 0.000 \ \text { Residual Error } & 13 & 26.80 & 2.06 & & \ \text { Total } & 14 & 394.00 & & & \end{array}$

-According to the data, a worker who received 8 hours of training had a
troubleshooting time of 15 minutes. What is the value of the residual for this worker?
Explain what the residual means.

Accepted Answer

The residual for this worker i

Question 12

A sales manager claims that there is a relationship between college GPA and sales performance (number of units sold) among salespeople hired within the last year.
Use the regression results are shown below and set α = .05 to test his claim. $$\begin{array} { l r r r r } 
\text { Predictor } & \text { Coef } & \text { SE Coef } & T & \mathrm {~P} \
\text { Constant } & - 0.484 & 3.256 & - 0.15 & 0.884 \
\text { GPA } & 7.423 & 1.044 & 7.11 & 0.000
\end{array}$$

Accepted Answer

A)  reject the null hypothesis and conclude that there is no significant relationship
Between GPA and sales performance 
B)  fail to reject the null hypothesis and conclude that there is no significant
Relationship between GPA and sales performance 
C)  reject the null hypothesis and conclude that there is a significant relationship
Between GPA and sales performance 
D)  fail to reject the null hypothesis and conclude that there is a significant
Relationship between GPA and sales performance 
E)  None of these 
A)  reject the null hypothesis and conclude that there is no significant relationship
Between GPA and sales performance 
B)  fail to reject the null hypothesis and conclude that there is no significant
Relationship between GPA and sales performance 
C)  reject the null hypothesis and conclude that there is a significant relationship
Between GPA and sales performance 
D)  fail to reject the null hypothesis and conclude that there is a significant
Relationship between GPA and sales performance 
E)  None of these

Question 13

A sales manager was interested in determining if there is a relationship between college GPA and sales performance (number of units sold) among salespeople hired
Within the last year. From the regression results shown below, identify the residual
Standard deviation. $$\begin{array} { l r r r r } 
\text { Predictor } & \text { Coef } & \text { SE Coef } & T & \mathrm {~P} \
\text { Constant } & - 0.484 & 3.256 & - 0.15 & 0.884 \
\text { GPA } & 7.423 & 1.044 & 7.11 & 0.000
\end{array}$$ S = 1.57429 R-Sq = 78.3% R-Sq(adj) = 76.8%

Accepted Answer

A)  3.256. 
B)  1.044. 
C)  1.574. 
D)  34.70. 
E)  None of the above. 
A)  3.256. 
B)  1.044. 
C)  1.574. 
D)  34.70. 
E)  None of the above.

Question 14

A sales manager was interested in determining if there is a relationship between college GPA and sales performance (number of units sold) among salespeople hired
Within the last year. The correct null hypothesis is

Accepted Answer

A)  There is no relationship between GPA and sales performance. 
B)  There is a relationship between GPA and sales performance. 
C)  β1 = 0 
D)  Both A and C. 
E)  None of these 
A)  There is no relationship between GPA and sales performance. 
B)  There is a relationship between GPA and sales performance. 
C)  β1 = 0 
D)  Both A and C. 
E)  None of these

Question 15

Use the following for questions 
An operations manager was interested in determining if there is a relationship between the amount of training received by production line workers and the time it takes for them to troubleshoot a process problem. A sample of recently trained line workers was selected. The number of hours of training time received and the time it took (in minutes) for them to troubleshoot their last process problem were captured. Below are the scatterplot, regression results, and residual plots for these data. The regression equation is
Trouble Shooting $= 30.7 - 1.84$ Training
$\begin{array} { l r r r r } \text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \ \text { Constant } & 30.729 & 1.023 & 30.03 & 0.000 \ \text { Training } & - 1.8360 & 0.1376 & - 13.35 & 0.000 \end{array}$
$$S = 1.43588 \quad \mathrm { R } - \mathrm { Sq } = 93.2 \text { \% } \quad \mathrm { R } - \mathrm { Sq } ( \mathrm { adj } ) = 92.7 \text { \% }$$
Analysis of Variance
$\begin{array} { l r r r r r } \text { Source } & \text { DF } & \text { SS } & \text { MS } & \text { F } & \text { P } \ \text { Regression } & 1 & 367.20 & 367.20 & 178.10 & 0.000 \ \text { Residual Error } & 13 & 26.80 & 2.06 & & \ \text { Total } & 14 & 394.00 & & & \end{array}$

-Predict the troubleshooting time for a line worker who received 8 hours of training.

Accepted Answer

For a training time

Question 16

Use the following for questions 
A sales manager was interested in determining if there is a relationship between college GPA and sales performance among salespeople hired within the last year. A sample of recently hired salespeople was selected and college GPA and the number of units sold last month recorded. Below are the scatterplot, regression results, and residual plots for these data. The regression equation is
Units Sold $= - 0.48 + 7.42$ GPA
$\begin{array} { l r r r r } \text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \ \text { Constant } & - 0.484 & 3.256 & - 0.15 & 0.884 \ \text { GPA } & 7.423 & 1.044 & 7.11 & 0.000 \end{array}$
$\mathrm { S } = 1.57429 \quad \mathrm { R } - \mathrm { Sq } = 78.3 \% \mathrm { \circ } \quad \mathrm { R } - \mathrm { Sq } ( \mathrm { adj } ) = 76.8 \%$
Analysis of Variance
$\begin{array} { l r r r r r } \text { Source } & \text { DF } & \text { SS } & \text { MS } & \text { F } & \text { P } \ \text { Regression } & 1 & 125.30 & 125.30 & 50.56 & 0.000 \ \text { Residual Error } & 14 & 34.70 & 2.48 & & \ \text { Total } & 15 & 160.00 & & & \end{array}$ Answer:  
-List each of the four conditions for regression and inference and describe whether or
not they are satisfied.

Accepted Answer

Conditions:
* Linearity condition: There

Question 17

Use the following for questions 
An operations manager was interested in determining if there is a relationship between the amount of training received by production line workers and the time it takes for them to troubleshoot a process problem. A sample of recently trained line workers was selected. The number of hours of training time received and the time it took (in minutes) for them to troubleshoot their last process problem were captured. Below are the scatterplot, regression results, and residual plots for these data. The regression equation is
Trouble Shooting $= 30.7 - 1.84$ Training
$\begin{array} { l r r r r } \text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \ \text { Constant } & 30.729 & 1.023 & 30.03 & 0.000 \ \text { Training } & - 1.8360 & 0.1376 & - 13.35 & 0.000 \end{array}$
$$S = 1.43588 \quad \mathrm { R } - \mathrm { Sq } = 93.2 \text { \% } \quad \mathrm { R } - \mathrm { Sq } ( \mathrm { adj } ) = 92.7 \text { \% }$$
Analysis of Variance
$\begin{array} { l r r r r r } \text { Source } & \text { DF } & \text { SS } & \text { MS } & \text { F } & \text { P } \ \text { Regression } & 1 & 367.20 & 367.20 & 178.10 & 0.000 \ \text { Residual Error } & 13 & 26.80 & 2.06 & & \ \text { Total } & 14 & 394.00 & & & \end{array}$

-From the output, write the equation of the regression equation that can be used to
predict troubleshooting time.

Accepted Answer

The answer of Use the following for questions 
An operations...

Question 18

According to the plot of residuals versus fitted values below, which of the following is true?

Accepted Answer

A)  equal spread condition is satisfied 
B)  equal spread condition is not satisfied 
C)  nearly normal condition is not satisfied 
D)  linearity condition is not satisfied 
E)  quantitative variables condition is not satisfied 
A)  equal spread condition is satisfied 
B)  equal spread condition is not satisfied 
C)  nearly normal condition is not satisfied 
D)  linearity condition is not satisfied 
E)  quantitative variables condition is not satisfied

Consider Scatterplots 1 and 2 with fitted regression lines shown below. Which of the following statements is true?

When using a plot of residuals (y-axis) vs. fitted value of the dependent variable, a plot with no pattern indicates that the:

A sales manager was interested in determining if there is a relationship between college GPA and sales performance (number of units sold) among salespeople hired Within the last year. The correct null hypothesis is

According to the plot of residuals versus fitted values below, which of the following is true?

Data and Decisions

Displaying and Describing Categorical Data

Displaying and Describing Quantitative Data

Correlation and Linear Regression

Randomness and Probability

Random Variables and Probability Models

The Normal and Other Continuous Distributions

Surveys and Sampling

Sampling Distributions and Confidence Intervals for Proportions

Testing Hypotheses About Proportions

Confidence Intervals and Hypothesis Tests for Means

Comparing Two Means

Inference for Counts: Chi-Square Tests

Multiple Regression

Introduction to Data Mining

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Exam 14: Inference for Regression

Consider Scatterplots 1 and 2 with fitted regression lines shown below. Which of the following statements is true?

When using a plot of residuals (y-axis) vs. fitted value of the dependent variable, a plot with no pattern indicates that the:

A sales manager was interested in determining if there is a relationship between college GPA and sales performance (number of units sold) among salespeople hired Within the last year. The correct null hypothesis is

According to the plot of residuals versus fitted values below, which of the following is true?

Data and Decisions

Displaying and Describing Categorical Data

Displaying and Describing Quantitative Data

Correlation and Linear Regression

Randomness and Probability

Random Variables and Probability Models

The Normal and Other Continuous Distributions

Surveys and Sampling

Sampling Distributions and Confidence Intervals for Proportions

Testing Hypotheses About Proportions

Confidence Intervals and Hypothesis Tests for Means

Comparing Two Means

Inference for Counts: Chi-Square Tests

Multiple Regression

Introduction to Data Mining

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