Exam 14: Introduction to Multiple Regression

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TABLE 14-18 A logistic regression model was estimated in order to predict the probability that a randomly chosen university or college would be a private university using information on mean total Scholastic Aptitude Test score (SAT)at the university or college and whether the TOEFL criterion is at least 90 (Toefl90 = 1 if yes,0 otherwise).The dependent variable,Y,is school type (Type = 1 if private and 0 otherwise). The PHStat output is given below: TABLE 14-18 A logistic regression model was estimated in order to predict the probability that a randomly chosen university or college would be a private university using information on mean total Scholastic Aptitude Test score (SAT)at the university or college and whether the TOEFL criterion is at least 90 (Toefl90 = 1 if yes,0 otherwise).The dependent variable,Y,is school type (Type = 1 if private and 0 otherwise). The PHStat output is given below:   -Referring to Table 14-18,what is the p-value of the test statistic when testing whether Toefl90 makes a significant contribution to the model in the presence of SAT? -Referring to Table 14-18,what is the p-value of the test statistic when testing whether Toefl90 makes a significant contribution to the model in the presence of SAT?

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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 null hypothesis H<sub>0</sub> : β<sub>1 </sub>= β<sub>2 </sub>= 0 implies that the number of weeks a worker is unemployed due to a layoff is not affected by some of the explanatory variables. -True or False: Referring to Table 14-17,the null hypothesis H0 : β1 = β2 = 0 implies that the number of weeks a worker is unemployed due to a layoff is not affected by some 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,what is the p-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 p-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 14-16 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. -Referring to Table 14-16,________ of the variation in Accel Time can be explained by engine size while controlling for the other independent variable. 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. -Referring to Table 14-16,________ of the variation in Accel Time can be explained by engine size while controlling for the other independent variable. 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. -Referring to Table 14-16,________ of the variation in Accel Time can be explained by engine size while controlling for the other independent variable. 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. -Referring to Table 14-16,________ of the variation in Accel Time can be explained by engine size while controlling for the other independent variable. 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. -Referring to Table 14-16,________ of the variation in Accel Time can be explained by engine size while controlling for the other independent variable. 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. -Referring to Table 14-16,________ of the variation in Accel Time can be explained by engine size while controlling for the other independent variable. 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. -Referring to Table 14-16,________ of the variation in Accel Time can be explained by engine size while controlling for the other independent variable. 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. -Referring to Table 14-16,________ of the variation in Accel Time can be explained by engine size while controlling for the other independent variable. )are,respectively 0.0077,and 0.0077. -Referring to Table 14-16,________ of the variation in Accel Time can be explained by engine size while controlling for the other independent variable.

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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 are the degrees of freedom for the chi-square distribution when testing whether the model is a good-fitting model? -Referring to Table 14-19,what are the degrees of freedom for the chi-square distribution when testing whether the model is a good-fitting model?

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TABLE 14-18 A logistic regression model was estimated in order to predict the probability that a randomly chosen university or college would be a private university using information on mean total Scholastic Aptitude Test score (SAT)at the university or college and whether the TOEFL criterion is at least 90 (Toefl90 = 1 if yes,0 otherwise).The dependent variable,Y,is school type (Type = 1 if private and 0 otherwise). The PHStat output is given below: TABLE 14-18 A logistic regression model was estimated in order to predict the probability that a randomly chosen university or college would be a private university using information on mean total Scholastic Aptitude Test score (SAT)at the university or college and whether the TOEFL criterion is at least 90 (Toefl90 = 1 if yes,0 otherwise).The dependent variable,Y,is school type (Type = 1 if private and 0 otherwise). The PHStat output is given below:   -Referring to Table 14-18,what should be the decision ('reject' or 'do not reject')on the null hypothesis when testing whether SAT makes a significant contribution to the model in the presence of Toefl90 at a 0.05 level of significance? -Referring to Table 14-18,what should be the decision ('reject' or 'do not reject')on the null hypothesis when testing whether SAT makes a significant contribution to the model in the presence of Toefl90 at a 0.05 level of significance?

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TABLE 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: TABLE 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Also SSR (X<sub>1</sub> ∣ X<sub>2</sub>)= 36400.6326 and SSR (X<sub>2</sub> ∣ X<sub>1</sub>)= 3297.7917 -Referring to Table 14-4,the coefficient of partial determination   is ________. Also SSR (X1 ∣ X2)= 36400.6326 and SSR (X2 ∣ X1)= 3297.7917 -Referring to Table 14-4,the coefficient of partial determination TABLE 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Also SSR (X<sub>1</sub> ∣ X<sub>2</sub>)= 36400.6326 and SSR (X<sub>2</sub> ∣ X<sub>1</sub>)= 3297.7917 -Referring to Table 14-4,the coefficient of partial determination   is ________. is ________.

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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,the coefficient of partial determination   is ________. Also SSR (X1 ∣ X2)= 8343.3572 and SSR (X2 ∣ X1)= 4199.2672 -Referring to Table 14-6,the coefficient of partial determination 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,the coefficient of partial determination   is ________. is ________.

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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 p-value of the F test for the significance of the entire regression 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 p-value of the F test for the significance of the entire regression is ________.

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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 p-value of the test statistic when testing whether the model is a good-fitting model? -Referring to Table 14-19,what is the p-value of the test statistic when testing whether the model is a good-fitting model?

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TABLE 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: TABLE 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Also SSR (X<sub>1</sub> ∣ X<sub>2</sub>)= 36400.6326 and SSR (X<sub>2</sub> ∣ X<sub>1</sub>)= 3297.7917 -Referring to Table 14-4,what is the value of the calculated F test statistic that is missing from the output for testing whether the whole regression model is significant? Also SSR (X1 ∣ X2)= 36400.6326 and SSR (X2 ∣ X1)= 3297.7917 -Referring to Table 14-4,what is the value of the calculated F test statistic that is missing from the output for testing whether the whole regression model is significant?

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TABLE 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: TABLE 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Also SSR (X<sub>1</sub> ∣ X<sub>2</sub>)= 36400.6326 and SSR (X<sub>2</sub> ∣ X<sub>1</sub>)= 3297.7917 -Referring to Table 14-4,________% of the variation in the house size can be explained by the variation in the family income while holding the family size constant. Also SSR (X1 ∣ X2)= 36400.6326 and SSR (X2 ∣ X1)= 3297.7917 -Referring to Table 14-4,________% of the variation in the house size can be explained by the variation in the family income while holding the family size constant.

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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 department head wants to test H<sub>0</sub> : β<sub>1 </sub>= β<sub>2</sub> = 0.The p-value of the test is ________. -Referring to Table 14-7,the department head wants to test H0 : β1 = β2 = 0.The p-value of the test is ________.

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TABLE 14-14 An automotive engineer would like to be able to predict automobile mileages.She believes that the two most important characteristics that affect mileage are horsepower and the number of cylinders (4 or 6)of a car.She believes that the appropriate model is Y = 40 - 0.05X1 + 20X2 - 0.1X1 X2 where X1 = horsepower X2 = 1 if 4 cylinders,0 if 6 cylinders Y = mileage -Referring to Table 14-14,the fitted model for predicting mileages for 6-cylinder cars is ________.

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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,the estimated value of the regression parameter β<sub>1</sub> means that Also SSR (X1 ∣ X2)= 8343.3572 and SSR (X2 ∣ X1)= 4199.2672 -Referring to Table 14-6,the estimated value of the regression parameter β1 means that

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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 the number of weeks a worker is unemployed due to a layoff depends on at least one of the explanatory variables at a 10% level of significance. -True or False: Referring to Table 14-17,there is sufficient evidence that the number of weeks a worker is unemployed due to a layoff depends on at least one of the explanatory variables at a 10% level of significance.

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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 null hypothesis should be rejected at a 5% level of significance when testing 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. -True or False: Referring to Table 14-15,the null hypothesis should be rejected at a 5% level of significance when testing 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 14-5 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,one company in the sample had sales of $20 billion (Sales = 20,000).This company spent $300 million on capital and $700 million on wages.What is the residual (in millions of dollars)for this data point? -Referring to Table 14-5,one company in the sample had sales of $20 billion (Sales = 20,000).This company spent $300 million on capital and $700 million on wages.What is the residual (in millions of dollars)for this data point?

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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,you can conclude definitively that instructional spending per pupil individually has no impact on the mean percentage of students passing the proficiency test,taking into account the effect of mean teacher salary,at a 10% level of significance based solely on but not actually computing the 90% confidence interval estimate for β<sub>2</sub>. -True or False: Referring to Table 14-15,you can conclude definitively that instructional spending per pupil individually has no impact on the mean percentage of students passing the proficiency test,taking into account the effect of mean teacher salary,at a 10% level of significance based solely on but not actually computing the 90% confidence interval estimate for β2.

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TABLE 14-10 You worked as an intern at We Always Win Car Insurance Company last summer.You notice that individual car insurance premiums depend very much on the age of the individual and the number of traffic tickets received by the individual.You performed a regression analysis in EXCEL and obtained the following partial information: TABLE 14-10 You worked as an intern at We Always Win Car Insurance Company last summer.You notice that individual car insurance premiums depend very much on the age of the individual and the number of traffic tickets received by the individual.You performed a regression analysis in EXCEL and obtained the following partial information:   -Referring to Table 14-10,the proportion of the total variability in insurance premiums that can be explained by AGE and TICKETS is ________. -Referring to Table 14-10,the proportion of the total variability in insurance premiums that can be explained by AGE and TICKETS is ________.

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