Exam 14: Introduction to Multiple Regression

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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 alternative hypothesis to determine whether there is a significant relationship between percentage of students passing the proficiency test and the entire set of explanatory variables? -Referring to Table 14-15,which of the following is the correct alternative hypothesis to determine whether there is a significant relationship between percentage of students passing the proficiency test and the entire set of explanatory variables?

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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 value of the F-statistic for testing 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 value of the F-statistic for testing the significance of the entire regression is ________.

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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 the two independent variables after taking into consideration the number of independent variables and the number of observations. 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 the two independent variables after taking into consideration the number of independent variables and the number of observations. 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 the two independent variables after taking into consideration the number of independent variables and the number of observations. 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 the two independent variables after taking into consideration the number of independent variables and the number of observations. 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 the two independent variables after taking into consideration the number of independent variables and the number of observations. 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 the two independent variables after taking into consideration the number of independent variables and the number of observations. 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 the two independent variables after taking into consideration the number of independent variables and the number of observations. 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 the two independent variables after taking into consideration the number of independent variables and the number of observations. )are,respectively 0.0077,and 0.0077. -Referring to Table 14-16,________ of the variation in Accel Time can be explained by the two independent variables after taking into consideration the number of independent variables and the number of observations.

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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,what are the lower and upper limits of the 95% confidence interval estimate for the difference in the mean number of weeks a worker is unemployed due to a layoff between a worker who is in a management position and one who is not after taking into consideration the effect of all the other independent variables? -Referring to Table 14-17,what are the lower and upper limits of the 95% confidence interval estimate for the difference in the mean number of weeks a worker is unemployed due to a layoff between a worker who is in a management position and one who is not after taking into consideration the effect of all the other independent variables?

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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 is the p-value for testing whether Capital has a negative influence on corporate sales? -Referring to Table 14-5,what is the p-value for testing whether Capital has a negative influence on corporate sales?

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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 total degrees of freedom that are missing in the ANOVA table should be ________. -Referring to Table 14-10,the total degrees of freedom that are missing in the ANOVA table should be ________.

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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 value of the coefficient of multiple determination,   ,is ________. -Referring to Table 14-7,the value of the coefficient of multiple determination, 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 value of the coefficient of multiple determination,   ,is ________. ,is ________.

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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,which of the following assumptions is most likely violated based on the residual plot of the residuals versus predicted Y? 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,which of the following assumptions is most likely violated based on the residual plot of the residuals versus predicted Y? 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,which of the following assumptions is most likely violated based on the residual plot of the residuals versus predicted Y? 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,which of the following assumptions is most likely violated based on the residual plot of the residuals versus predicted Y? 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,which of the following assumptions is most likely violated based on the residual plot of the residuals versus predicted Y? 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,which of the following assumptions is most likely violated based on the residual plot of the residuals versus predicted Y? 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,which of the following assumptions is most likely violated based on the residual plot of the residuals versus predicted Y? 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,which of the following assumptions is most likely violated based on the residual plot of the residuals versus predicted Y? )are,respectively 0.0077,and 0.0077. -Referring to Table 14-16,which of the following assumptions is most likely violated based on the residual plot of the residuals versus predicted Y?

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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,which of the following is the correct expression for the estimated model? -Referring to Table 14-18,which of the following is the correct expression for the estimated 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 are the degrees of freedom for the chi-square distribution when testing whether the model is a good-fitting model? -Referring to Table 14-18,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 14-17 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 null hypothesis to determine whether there is a significant relationship between the number of weeks a worker is unemployed due to a layoff and the entire set of explanatory variables? -Referring to Table 14-17,which of the following is the correct null hypothesis to determine whether there is a significant relationship between the number of weeks a worker is unemployed due to a layoff and the entire set of 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 determine whether there is a significant relationship between percentage of students passing the proficiency test and the entire set of explanatory variables? -Referring to Table 14-15,which of the following is the correct null hypothesis to determine whether there is a significant relationship between percentage of students passing the proficiency test and the entire set of explanatory variables?

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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 LawnSize makes a significant contribution to the model in the presence of Income? -Referring to Table 14-19,what is the p-value of the test statistic when testing whether LawnSize makes a significant contribution to the model in the presence of Income?

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TABLE 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. TABLE 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   -Referring to Table 14-3,to test whether aggregate price index has a positive impact on consumption,the p-value is -Referring to Table 14-3,to test whether aggregate price index has a positive impact on consumption,the p-value 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,what is the value of the test statistic when testing 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,what is the value of the test statistic when testing 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 14-19 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 interpretation for the Lawn Size slope coefficient? -Referring to Table 14-19,which of the following is the correct interpretation for the Lawn Size slope coefficient?

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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,estimate the mean number of weeks being unemployed due to a layoff for a worker who is a thirty-year-old and is a manager. -Referring to Table 14-17,estimate the mean number of weeks being unemployed due to a layoff for a worker who is a thirty-year-old and is a manager.

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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 a particular model,the sum of the squared residuals was 847.If the model had 5 independent variables,and the data set contained 40 points,the value of the standard error of the estimate is 24.911. Also SSR (X1 ∣ X2)= 8343.3572 and SSR (X2 ∣ X1)= 4199.2672 -True or False: In a particular model,the sum of the squared residuals was 847.If the model had 5 independent variables,and the data set contained 40 points,the value of the standard error of the estimate is 24.911.

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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:   -A dummy variable is used as an independent variable in a regression model when -A dummy variable is used as an independent variable in a regression model when

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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 determine whether there is a significant relationship between percentage of students passing the proficiency test and the entire set of explanatory variables? -Referring to Table 14-17,which of the following is the correct alternative hypothesis to determine whether there is a significant relationship between percentage of students passing the proficiency test and the entire set of explanatory variables?

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