Exam 14: Introduction to Multiple Regression

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TABLE 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter,a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit (X1)and the amount of insulation in inches (X2).Given below is EXCEL output of the regression model. TABLE 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter,a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit (X<sub>1</sub>)and the amount of insulation in inches (X<sub>2</sub>).Given below is EXCEL output of the regression model.   Also SSR (X<sub>1</sub> ∣ X<sub>2</sub>)= 8343.3572 and SSR (X<sub>2</sub> ∣ X<sub>1</sub>)= 4199.2672 -True or False: In calculating the standard error of the estimate,S<sub>YX</sub> =   ,there are n - k - 1 degrees of freedom,where n is the sample size and k represents the number of independent variables in the model. Also SSR (X1 ∣ X2)= 8343.3572 and SSR (X2 ∣ X1)= 4199.2672 -True or False: In calculating the standard error of the estimate,SYX = TABLE 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter,a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit (X<sub>1</sub>)and the amount of insulation in inches (X<sub>2</sub>).Given below is EXCEL output of the regression model.   Also SSR (X<sub>1</sub> ∣ X<sub>2</sub>)= 8343.3572 and SSR (X<sub>2</sub> ∣ X<sub>1</sub>)= 4199.2672 -True or False: In calculating the standard error of the estimate,S<sub>YX</sub> =   ,there are n - k - 1 degrees of freedom,where n is the sample size and k represents the number of independent variables in the model. ,there are n - k - 1 degrees of freedom,where n is the sample size and k represents the number of independent variables in the model.

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TABLE 14-15 The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test.She obtained the data on percentage of students passing the proficiency test (% Passing),mean teacher salary in thousands of dollars (Salaries),and instructional spending per pupil in thousands of dollars (Spending)of 47 schools in the state. Following is the multiple regression output with Y = % Passing as the dependent variable,X1 = Salaries and X2 = Spending: TABLE 14-15 The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test.She obtained the data on percentage of students passing the proficiency test (% Passing),mean teacher salary in thousands of dollars (Salaries),and instructional spending per pupil in thousands of dollars (Spending)of 47 schools in the state. Following is the multiple regression output with Y = % Passing as the dependent variable,X<sub>1 </sub>= Salaries and X<sub>2</sub> = Spending:   -True or False: Referring to Table 14-15,the alternative hypothesis H<sub>1 </sub>: At least one of β<sub>j</sub> ≠ 0 for j = 1,2 implies that percentage of students passing the proficiency test is related to at least one of the explanatory variables. -True or False: Referring to Table 14-15,the alternative hypothesis H1 : At least one of βj ≠ 0 for j = 1,2 implies that percentage of students passing the proficiency test is related to at least one of the explanatory variables.

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TABLE 14-15 The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test.She obtained the data on percentage of students passing the proficiency test (% Passing),mean teacher salary in thousands of dollars (Salaries),and instructional spending per pupil in thousands of dollars (Spending)of 47 schools in the state. Following is the multiple regression output with Y = % Passing as the dependent variable,X1 = Salaries and X2 = Spending: TABLE 14-15 The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test.She obtained the data on percentage of students passing the proficiency test (% Passing),mean teacher salary in thousands of dollars (Salaries),and instructional spending per pupil in thousands of dollars (Spending)of 47 schools in the state. Following is the multiple regression output with Y = % Passing as the dependent variable,X<sub>1 </sub>= Salaries and X<sub>2</sub> = Spending:   -Referring to Table 14-15,which of the following is the correct null hypothesis to test whether instructional spending per pupil has any effect on percentage of students passing the proficiency test,taking into account the effect of mean teacher salary? -Referring to Table 14-15,which of the following is the correct null hypothesis to test whether instructional spending per pupil has any effect on percentage of students passing the proficiency test,taking into account the effect of mean teacher salary?

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TABLE 14-19 The marketing manager for a nationally franchised lawn service company would like to study the characteristics that differentiate home owners who do and do not have a lawn service.A random sample of 30 home owners located in a suburban area near a large city was selected; 11 did not have a lawn service (code 0)and 19 had a lawn service (code 1).Additional information available concerning these 30 home owners includes family income (Income,in thousands of dollars)and lawn size (Lawn Size,in thousands of square feet). The PHStat output is given below: TABLE 14-19 The marketing manager for a nationally franchised lawn service company would like to study the characteristics that differentiate home owners who do and do not have a lawn service.A random sample of 30 home owners located in a suburban area near a large city was selected; 11 did not have a lawn service (code 0)and 19 had a lawn service (code 1).Additional information available concerning these 30 home owners includes family income (Income,in thousands of dollars)and lawn size (Lawn Size,in thousands of square feet). The PHStat output is given below:   -Referring to Table 14-19,what is the estimated odds ratio for a home owner with a family income of $50,000 and a lawn size of 2,000 square feet? -Referring to Table 14-19,what is the estimated odds ratio for a home owner with a family income of $50,000 and a lawn size of 2,000 square feet?

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TABLE 14-16 What are the factors that determine the acceleration time (in sec.)from 0 to 60 miles per hour of a car? Data on the following variables for 30 different vehicle models were collected: Y (Accel Time): Acceleration time in sec. X1 (Engine Size): c.c. X2 (Sedan): 1 if the vehicle model is a sedan and 0 otherwise The regression results using acceleration time as the dependent variable and the remaining variables as the independent variables are presented below. TABLE 14-16 What are the factors that determine the acceleration time (in sec.)from 0 to 60 miles per hour of a car? Data on the following variables for 30 different vehicle models were collected: Y (Accel Time): Acceleration time in sec. X<sub>1</sub><sub> </sub>(Engine Size): c.c. X<sub>2 </sub>(Sedan): 1 if the vehicle model is a sedan and 0 otherwise The regression results using acceleration time as the dependent variable and the remaining variables as the independent variables are presented below.   The various residual plots are as shown below.         The coefficient of partial determinations   and   are 0.3301,and 0.0594,respectively. The coefficient of determination for the regression model using each of the 2 independent variables as the dependent variable and the other independent variable as independent variables (   )are,respectively 0.0077,and 0.0077. -True or False: Referring to Table 14-16,the error appears to be left-skewed. The various residual plots are as shown below. TABLE 14-16 What are the factors that determine the acceleration time (in sec.)from 0 to 60 miles per hour of a car? Data on the following variables for 30 different vehicle models were collected: Y (Accel Time): Acceleration time in sec. X<sub>1</sub><sub> </sub>(Engine Size): c.c. X<sub>2 </sub>(Sedan): 1 if the vehicle model is a sedan and 0 otherwise The regression results using acceleration time as the dependent variable and the remaining variables as the independent variables are presented below.   The various residual plots are as shown below.         The coefficient of partial determinations   and   are 0.3301,and 0.0594,respectively. The coefficient of determination for the regression model using each of the 2 independent variables as the dependent variable and the other independent variable as independent variables (   )are,respectively 0.0077,and 0.0077. -True or False: Referring to Table 14-16,the error appears to be left-skewed. TABLE 14-16 What are the factors that determine the acceleration time (in sec.)from 0 to 60 miles per hour of a car? Data on the following variables for 30 different vehicle models were collected: Y (Accel Time): Acceleration time in sec. X<sub>1</sub><sub> </sub>(Engine Size): c.c. X<sub>2 </sub>(Sedan): 1 if the vehicle model is a sedan and 0 otherwise The regression results using acceleration time as the dependent variable and the remaining variables as the independent variables are presented below.   The various residual plots are as shown below.         The coefficient of partial determinations   and   are 0.3301,and 0.0594,respectively. The coefficient of determination for the regression model using each of the 2 independent variables as the dependent variable and the other independent variable as independent variables (   )are,respectively 0.0077,and 0.0077. -True or False: Referring to Table 14-16,the error appears to be left-skewed. TABLE 14-16 What are the factors that determine the acceleration time (in sec.)from 0 to 60 miles per hour of a car? Data on the following variables for 30 different vehicle models were collected: Y (Accel Time): Acceleration time in sec. X<sub>1</sub><sub> </sub>(Engine Size): c.c. X<sub>2 </sub>(Sedan): 1 if the vehicle model is a sedan and 0 otherwise The regression results using acceleration time as the dependent variable and the remaining variables as the independent variables are presented below.   The various residual plots are as shown below.         The coefficient of partial determinations   and   are 0.3301,and 0.0594,respectively. The coefficient of determination for the regression model using each of the 2 independent variables as the dependent variable and the other independent variable as independent variables (   )are,respectively 0.0077,and 0.0077. -True or False: Referring to Table 14-16,the error appears to be left-skewed. TABLE 14-16 What are the factors that determine the acceleration time (in sec.)from 0 to 60 miles per hour of a car? Data on the following variables for 30 different vehicle models were collected: Y (Accel Time): Acceleration time in sec. X<sub>1</sub><sub> </sub>(Engine Size): c.c. X<sub>2 </sub>(Sedan): 1 if the vehicle model is a sedan and 0 otherwise The regression results using acceleration time as the dependent variable and the remaining variables as the independent variables are presented below.   The various residual plots are as shown below.         The coefficient of partial determinations   and   are 0.3301,and 0.0594,respectively. The coefficient of determination for the regression model using each of the 2 independent variables as the dependent variable and the other independent variable as independent variables (   )are,respectively 0.0077,and 0.0077. -True or False: Referring to Table 14-16,the error appears to be left-skewed. The coefficient of partial determinations TABLE 14-16 What are the factors that determine the acceleration time (in sec.)from 0 to 60 miles per hour of a car? Data on the following variables for 30 different vehicle models were collected: Y (Accel Time): Acceleration time in sec. X<sub>1</sub><sub> </sub>(Engine Size): c.c. X<sub>2 </sub>(Sedan): 1 if the vehicle model is a sedan and 0 otherwise The regression results using acceleration time as the dependent variable and the remaining variables as the independent variables are presented below.   The various residual plots are as shown below.         The coefficient of partial determinations   and   are 0.3301,and 0.0594,respectively. The coefficient of determination for the regression model using each of the 2 independent variables as the dependent variable and the other independent variable as independent variables (   )are,respectively 0.0077,and 0.0077. -True or False: Referring to Table 14-16,the error appears to be left-skewed. and TABLE 14-16 What are the factors that determine the acceleration time (in sec.)from 0 to 60 miles per hour of a car? Data on the following variables for 30 different vehicle models were collected: Y (Accel Time): Acceleration time in sec. X<sub>1</sub><sub> </sub>(Engine Size): c.c. X<sub>2 </sub>(Sedan): 1 if the vehicle model is a sedan and 0 otherwise The regression results using acceleration time as the dependent variable and the remaining variables as the independent variables are presented below.   The various residual plots are as shown below.         The coefficient of partial determinations   and   are 0.3301,and 0.0594,respectively. The coefficient of determination for the regression model using each of the 2 independent variables as the dependent variable and the other independent variable as independent variables (   )are,respectively 0.0077,and 0.0077. -True or False: Referring to Table 14-16,the error appears to be left-skewed. are 0.3301,and 0.0594,respectively. The coefficient of determination for the regression model using each of the 2 independent variables as the dependent variable and the other independent variable as independent variables ( TABLE 14-16 What are the factors that determine the acceleration time (in sec.)from 0 to 60 miles per hour of a car? Data on the following variables for 30 different vehicle models were collected: Y (Accel Time): Acceleration time in sec. X<sub>1</sub><sub> </sub>(Engine Size): c.c. X<sub>2 </sub>(Sedan): 1 if the vehicle model is a sedan and 0 otherwise The regression results using acceleration time as the dependent variable and the remaining variables as the independent variables are presented below.   The various residual plots are as shown below.         The coefficient of partial determinations   and   are 0.3301,and 0.0594,respectively. The coefficient of determination for the regression model using each of the 2 independent variables as the dependent variable and the other independent variable as independent variables (   )are,respectively 0.0077,and 0.0077. -True or False: Referring to Table 14-16,the error appears to be left-skewed. )are,respectively 0.0077,and 0.0077. -True or False: Referring to Table 14-16,the error appears to be left-skewed.

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TABLE 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating (X1)and the number of economics courses the employee successfully completed in college (X2).The professor randomly selects 6 workers and collects the following information: TABLE 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating (X<sub>1</sub>)and the number of economics courses the employee successfully completed in college (X<sub>2</sub>).The professor randomly selects 6 workers and collects the following information:   -Referring to Table 14-2,for these data,what is the estimated coefficient for performance rating,b<sub>1</sub>? -Referring to Table 14-2,for these data,what is the estimated coefficient for performance rating,b1?

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TABLE 14-15 The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test.She obtained the data on percentage of students passing the proficiency test (% Passing),mean teacher salary in thousands of dollars (Salaries),and instructional spending per pupil in thousands of dollars (Spending)of 47 schools in the state. Following is the multiple regression output with Y = % Passing as the dependent variable,X1 = Salaries and X2 = Spending: TABLE 14-15 The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test.She obtained the data on percentage of students passing the proficiency test (% Passing),mean teacher salary in thousands of dollars (Salaries),and instructional spending per pupil in thousands of dollars (Spending)of 47 schools in the state. Following is the multiple regression output with Y = % Passing as the dependent variable,X<sub>1 </sub>= Salaries and X<sub>2</sub> = Spending:   -Referring to Table 14-15,what is the value of the test statistic when testing whether instructional spending per pupil has any effect on percentage of students passing the proficiency test,taking into account the effect of mean teacher salary? -Referring to Table 14-15,what is the value of the test statistic when testing whether instructional spending per pupil has any effect on percentage of students passing the proficiency test,taking into account the effect of mean teacher salary?

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TABLE 14-17 Given below are results from the regression analysis where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Unemploy)and the independent variables are the age of the worker (Age)and a dummy variable for management position (Manager: 1 = yes,0 = no). The results of the regression analysis are given below: TABLE 14-17 Given below are results from the regression analysis where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Unemploy)and the independent variables are the age of the worker (Age)and a dummy variable for management position (Manager: 1 = yes,0 = no). The results of the regression analysis are given below:   -True or False: Referring to Table 14-17,there is sufficient evidence that all of the explanatory variables are related to the number of weeks a worker is unemployed due to a layoff at a 10% level of significance. -True or False: Referring to Table 14-17,there is sufficient evidence that all of the explanatory variables are related to the number of weeks a worker is unemployed due to a layoff at a 10% level of significance.

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TABLE 14-17 Given below are results from the regression analysis where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Unemploy)and the independent variables are the age of the worker (Age)and a dummy variable for management position (Manager: 1 = yes,0 = no). The results of the regression analysis are given below: TABLE 14-17 Given below are results from the regression analysis where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Unemploy)and the independent variables are the age of the worker (Age)and a dummy variable for management position (Manager: 1 = yes,0 = no). The results of the regression analysis are given below:   -Referring to Table 14-17,which of the following is the correct alternative hypothesis to test whether age has any effect on the number of weeks a worker is unemployed due to a layoff while holding constant the effect of the other independent variable? -Referring to Table 14-17,which of the following is the correct alternative hypothesis to test whether age has any effect on the number of weeks a worker is unemployed due to a layoff while holding constant the effect of the other independent variable?

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TABLE 14-15 The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test.She obtained the data on percentage of students passing the proficiency test (% Passing),mean teacher salary in thousands of dollars (Salaries),and instructional spending per pupil in thousands of dollars (Spending)of 47 schools in the state. Following is the multiple regression output with Y = % Passing as the dependent variable,X1 = Salaries and X2 = Spending: TABLE 14-15 The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test.She obtained the data on percentage of students passing the proficiency test (% Passing),mean teacher salary in thousands of dollars (Salaries),and instructional spending per pupil in thousands of dollars (Spending)of 47 schools in the state. Following is the multiple regression output with Y = % Passing as the dependent variable,X<sub>1 </sub>= Salaries and X<sub>2</sub> = Spending:   -Referring to Table 14-15,which of the following is the correct null hypothesis to test whether mean teacher salary has any effect on percentage of students passing the proficiency test,taking into account the effect of instructional spending per pupil? -Referring to Table 14-15,which of the following is the correct null hypothesis to test whether mean teacher salary has any effect on percentage of students passing the proficiency test,taking into account the effect of instructional spending per pupil?

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TABLE 14-8 A financial analyst wanted to examine the relationship between salary (in $1,000)and 2 variables: age (X1 = Age)and experience in the field (X2 = Exper).He took a sample of 20 employees and obtained the following Microsoft Excel output: TABLE 14-8 A financial analyst wanted to examine the relationship between salary (in $1,000)and 2 variables: age (X<sub>1</sub> = Age)and experience in the field (X<sub>2</sub> = Exper).He took a sample of 20 employees and obtained the following Microsoft Excel output:   Also,the sum of squares due to the regression for the model that includes only Age is 5022.0654 while the sum of squares due to the regression for the model that includes only Exper is 125.9848. -Referring to Table 14-8,the analyst wants to use a t test to test for the significance of the coefficient of X<sub>2</sub>.The p-value of the test is ________. Also,the sum of squares due to the regression for the model that includes only Age is 5022.0654 while the sum of squares due to the regression for the model that includes only Exper is 125.9848. -Referring to Table 14-8,the analyst wants to use a t test to test for the significance of the coefficient of X2.The p-value of the test is ________.

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TABLE 14-17 Given below are results from the regression analysis where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Unemploy)and the independent variables are the age of the worker (Age)and a dummy variable for management position (Manager: 1 = yes,0 = no). The results of the regression analysis are given below: TABLE 14-17 Given below are results from the regression analysis where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Unemploy)and the independent variables are the age of the worker (Age)and a dummy variable for management position (Manager: 1 = yes,0 = no). The results of the regression analysis are given below:   -Referring to Table 14-17,which of the following is a correct statement? -Referring to Table 14-17,which of the following is a correct statement?

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TABLE 14-17 Given below are results from the regression analysis where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Unemploy)and the independent variables are the age of the worker (Age)and a dummy variable for management position (Manager: 1 = yes,0 = no). The results of the regression analysis are given below: TABLE 14-17 Given below are results from the regression analysis where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Unemploy)and the independent variables are the age of the worker (Age)and a dummy variable for management position (Manager: 1 = yes,0 = no). The results of the regression analysis are given below:   -True or False: Referring to Table 14-17,the alternative hypothesis H<sub>1</sub> : At least one of β<sub>j</sub> ≠ 0 for j = 1,2 implies that the number of weeks a worker is unemployed due to a layoff is related to all of the explanatory variables. -True or False: Referring to Table 14-17,the alternative hypothesis H1 : At least one of βj ≠ 0 for j = 1,2 implies that the number of weeks a worker is unemployed due to a layoff is related to all of the explanatory variables.

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TABLE 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating (X1)and the number of economics courses the employee successfully completed in college (X2).The professor randomly selects 6 workers and collects the following information: TABLE 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating (X<sub>1</sub>)and the number of economics courses the employee successfully completed in college (X<sub>2</sub>).The professor randomly selects 6 workers and collects the following information:   -Referring to Table 14-2,for these data,what is the estimated coefficient for the number of economics courses taken,b<sub>2</sub>? -Referring to Table 14-2,for these data,what is the estimated coefficient for the number of economics courses taken,b2?

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TABLE 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter,a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit (X1)and the amount of insulation in inches (X2).Given below is EXCEL output of the regression model. TABLE 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter,a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit (X<sub>1</sub>)and the amount of insulation in inches (X<sub>2</sub>).Given below is EXCEL output of the regression model.   Also SSR (X<sub>1</sub> ∣ X<sub>2</sub>)= 8343.3572 and SSR (X<sub>2</sub> ∣ X<sub>1</sub>)= 4199.2672 -Referring to Table 14-6,what is your decision and conclusion for the test H<sub>0</sub> : β<sub>2 </sub>= 0 vs.H<sub>1</sub> : β<sub>2 </sub>≠ 0 at the α = 0.01 level of significance? Also SSR (X1 ∣ X2)= 8343.3572 and SSR (X2 ∣ X1)= 4199.2672 -Referring to Table 14-6,what is your decision and conclusion for the test H0 : β2 = 0 vs.H1 : β2 ≠ 0 at the α = 0.01 level of significance?

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TABLE 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. TABLE 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   -Referring to Table 14-5,what are the predicted sales (in millions of dollars)for a company spending $100 million on capital and $100 million on wages? -Referring to Table 14-5,what are the predicted sales (in millions of dollars)for a company spending $100 million on capital and $100 million on wages?

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TABLE 14-7 The department head of the accounting department wanted to see if she could predict the GPA of students using the number of course units (credits)and total SAT scores of each.She takes a sample of students and generates the following Microsoft Excel output: TABLE 14-7 The department head of the accounting department wanted to see if she could predict the GPA of students using the number of course units (credits)and total SAT scores of each.She takes a sample of students and generates the following Microsoft Excel output:   -Referring to Table 14-7,the estimate of the unit change in the mean of Y per unit change in X<sub>1</sub>,holding X<sub>2</sub> constant,is ________. -Referring to Table 14-7,the estimate of the unit change in the mean of Y per unit change in X1,holding X2 constant,is ________.

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TABLE 14-7 The department head of the accounting department wanted to see if she could predict the GPA of students using the number of course units (credits)and total SAT scores of each.She takes a sample of students and generates the following Microsoft Excel output: TABLE 14-7 The department head of the accounting department wanted to see if she could predict the GPA of students using the number of course units (credits)and total SAT scores of each.She takes a sample of students and generates the following Microsoft Excel output:   -Referring to Table 14-7,the predicted GPA for a student carrying 15 course units and who has a total SAT of 1,100 is ________. -Referring to Table 14-7,the predicted GPA for a student carrying 15 course units and who has a total SAT of 1,100 is ________.

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TABLE 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. TABLE 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   -Referring to Table 14-5,which of the independent variables in the model are significant at the 5% level? -Referring to Table 14-5,which of the independent variables in the model are significant at the 5% level?

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TABLE 14-19 The marketing manager for a nationally franchised lawn service company would like to study the characteristics that differentiate home owners who do and do not have a lawn service.A random sample of 30 home owners located in a suburban area near a large city was selected; 11 did not have a lawn service (code 0)and 19 had a lawn service (code 1).Additional information available concerning these 30 home owners includes family income (Income,in thousands of dollars)and lawn size (Lawn Size,in thousands of square feet). The PHStat output is given below: TABLE 14-19 The marketing manager for a nationally franchised lawn service company would like to study the characteristics that differentiate home owners who do and do not have a lawn service.A random sample of 30 home owners located in a suburban area near a large city was selected; 11 did not have a lawn service (code 0)and 19 had a lawn service (code 1).Additional information available concerning these 30 home owners includes family income (Income,in thousands of dollars)and lawn size (Lawn Size,in thousands of square feet). The PHStat output is given below:   -Referring to Table 14-19,which of the following is the correct expression for the estimated model? -Referring to Table 14-19,which of the following is the correct expression for the estimated model?

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