Question 1

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 is no obvious bend in the original scatterplot of the data or in the plot of the residuals against the predicted values.
* Independence: These data were not collected over time and there is no reason to think that one salesperson's performance affects another.
* Bqual spread condition: Neither the original scatterplot nor the residual plot shows any changes in the spread about the line.
* Nearly Normal condition: The normal probability plot of residuals does not show severe departure from normality.

Question 2

Use the following information for questions 
Nutritional information was collected for 77 breakfast cereals including the amount of fiber (in grams), potassium (in mg), and the number of calories per serving. The data resulted in the following scatterplots.

-Compare the two plots with respect to the aspects that would affect the standard error
of the regression slope?

Accepted Answer

Overall, the plot of potassium vs. fiber would result in a regression slope with a more consistent standard error because the plot shows a tighter spread around the line. The spread of the $x$ 's for this plot show a wider range while not showing any points that defer from the pattern. Sample size is the same in each plot so this would not account for any difference in consistency of the standard error.

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: 
-Circle the standard error of the slope and its components in the output shown. If the
information is not in the output, list components.

Accepted Answer

[standard error of the slope, $1.044$, and standard deviation of the
residuals, $\mathrm { S } = 1.57429$ should be circled]
[standard error of the slope, $1.044$, and standard deviation of the
residuals, $\mathrm { s } = 1.57429$ should be circled]
Other components not given in the output are the standard deviation of $x$ and
the sample size
Other components not given in the output are the standard deviation of $x$ and
the sample size

Question 4

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

The answer of Data on labor productivity and unit labor...

Question 5

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
$\begin{array} { l r r r r r } \text { Source } & \text { DF } & \text { SS } & \text { MS } & \text { F } & \text { P } \ \text { Regression } & 1 & 367.20 & 367.20 & 178.10 & 0.000 \ \text { Residual Error } & 13 & 26.80 & 2.06 & & \ \text { Total } & 14 & 394.00 & & & \end{array}$

-Is there a significant relationship between time it takes to troubleshoot the process(minutes) and training received (use α = .05)? Give the appropriate test statistic,associated P-value, and conclusion.

Accepted Answer

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

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: 
-The confidence interval and prediction interval for the number of units sold per
month when GPA = 3.00 are shown below. Write a sentence to interpret each
interval in this context.

Accepted Answer

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

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: 
-What percentage of the variability in sales performance (units sold per month) can be
accounted for by college GPA?

Accepted Answer

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

Question 8

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 9

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. $$\begin{array} { l r r r r } 
\text { Predictor } & \text { Coef } & \text { SE Coef } & T & \mathrm {~P} \
\text { Constant } & - 0.484 & 3.256 & - 0.15 & 0.884 \
\text { GPA } & 7.423 & 1.044 & 7.11 & 0.000
\end{array}$$
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

Accepted Answer

The coefficient for GPA is positive and significant at the 0.05 level, which means that there is a significant positive relationship between college GPA and sales performance. Therefore, we can reject the null hypothesis and conclude that there is a significant relationship between GPA and sales performance.

Question 10

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

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

Question 11

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

The answer of The following plots show (1) world population...

Question 12

Use the following information for questions 
Nutritional information was collected for 77 breakfast cereals including the amount of fiber (in grams), potassium (in mg), and the number of calories per serving. The data resulted in the following scatterplots.

-From which of these plots would you expect a more consistent regression slope estimate? Why?

Accepted Answer

The answer of Use the following information for questions 
Nutritional...

Question 13

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

Question 14

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 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
$\begin{array} { l r r r r r } \text { Source } & \text { DF } & \text { SS } & \text { MS } & \text { F } & \text { P } \ \text { Regression } & 1 & 367.20 & 367.20 & 178.10 & 0.000 \ \text { Residual Error } & 13 & 26.80 & 2.06 & & \ \text { Total } & 14 & 394.00 & & & \end{array}$

-Based on the scatterplot, what is the relationship between training and troubleshooting? Is a regression appropriate for this data? Why or why not?

Accepted Answer

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

Question 16

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
$\begin{array} { l r r r r r } \text { Source } & \text { DF } & \text { SS } & \text { MS } & \text { F } & \text { P } \ \text { Regression } & 1 & 367.20 & 367.20 & 178.10 & 0.000 \ \text { Residual Error } & 13 & 26.80 & 2.06 & & \ \text { Total } & 14 & 394.00 & & & \end{array}$

-The 95% confidence interval for troubleshooting time with 8 hours of training is
(15.180, 16.903). Interpret this interval with respect to the estimated troubleshooting
time.

Accepted Answer

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

Question 17

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 is the independent variable in this regression? Write the null and alternative
hypothesis to test the slope of this variable.

Accepted Answer

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

Question 18

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
$\begin{array} { l r r r r r } \text { Source } & \text { DF } & \text { SS } & \text { MS } & \text { F } & \text { P } \ \text { Regression } & 1 & 367.20 & 367.20 & 178.10 & 0.000 \ \text { Residual Error } & 13 & 26.80 & 2.06 & & \ \text { Total } & 14 & 394.00 & & & \end{array}$

-Write a sentence to interpret the coefficient of training in the regression equation.

Accepted Answer

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

Question 19

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
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

Accepted Answer

The null hypothesis is that there is no relationship between GPA and sales performance, which is option A. Option C, β1 = 0, is also the same as option A, as β1 refers to the coefficient of the GPA variable in the regression equation. Therefore, the correct null hypothesis is either A or C. Option D includes both A and C, which is also correct. Option B is the opposite of the null hypothesis and assumes there is a relationship, which is not the testable hypothesis in this case. Option E is not a valid choice as it does not provide an alternative hypothesis.

Question 20

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

Quiz 14: Inference for Regression

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