Exam 14: Introduction to Multiple Regression

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SCENARIO 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: SCENARIO 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 Scenario 14-17, what is the p-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 Scenario 14-17, what is the p-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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SCENARIO 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: SCENARIO 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 Scenario 14-17, what is the value of the test statistic 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 Scenario 14-17, what is the value of the test statistic 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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SCENARIO 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, SCENARIO 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,   = Salaries and   Spending:     -Referring to Scenario 14-15, which of the following is the correct alternative hypothesis to test whether instructional spending per pupil has any effect on percentage of students passing the proficiency test, considering the effect of mean teacher salary? = Salaries and SCENARIO 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,   = Salaries and   Spending:     -Referring to Scenario 14-15, which of the following is the correct alternative hypothesis to test whether instructional spending per pupil has any effect on percentage of students passing the proficiency test, considering the effect of mean teacher salary? Spending: SCENARIO 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,   = Salaries and   Spending:     -Referring to Scenario 14-15, which of the following is the correct alternative hypothesis to test whether instructional spending per pupil has any effect on percentage of students passing the proficiency test, considering the effect of mean teacher salary? SCENARIO 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,   = Salaries and   Spending:     -Referring to Scenario 14-15, which of the following is the correct alternative hypothesis to test whether instructional spending per pupil has any effect on percentage of students passing the proficiency test, considering the effect of mean teacher salary? -Referring to Scenario 14-15, which of the following is the correct alternative hypothesis to test whether instructional spending per pupil has any effect on percentage of students passing the proficiency test, considering the effect of mean teacher salary?

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SCENARIO 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 and total SAT scores of each.She takes a sample of students and generates the following Microsoft Excel output: SCENARIO 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 and total SAT scores of each.She takes a sample of students and generates the following Microsoft Excel output:   -Referring to Scenario 14-7, the department head wants to test   The appropriate alternative hypothesis is ________. -Referring to Scenario 14-7, the department head wants to test SCENARIO 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 and total SAT scores of each.She takes a sample of students and generates the following Microsoft Excel output:   -Referring to Scenario 14-7, the department head wants to test   The appropriate alternative hypothesis is ________. The appropriate alternative hypothesis is ________.

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SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   -Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing scores on the aptitude test,  and how he/she scored on a business aptitude test SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   -Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing scores on the aptitude test,  A random sample of 8 employees provides the following: SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   -Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing scores on the aptitude test,  -Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing scores on the aptitude test, SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   -Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing scores on the aptitude test,

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SCENARIO 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).There are 80 universities in the sample. The PHStat output is given below: SCENARIO 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).There are 80 universities in the sample. The PHStat output is given below:   -Referring to Scenario 14-18, what is the estimated probability that a school with a mean SAT score of 1100 and a TOEFL criterion that is not at least 90? -Referring to Scenario 14-18, what is the estimated probability that a school with a mean SAT score of 1100 and a TOEFL criterion that is not at least 90?

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SCENARIO 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. SCENARIO 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 Scenario 14-3, the p-value for the aggregated price index is -Referring to Scenario 14-3, the p-value for the aggregated price index is

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SCENARIO 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: SCENARIO 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:   -Referring to Scenario 14-4, the coefficient of partial determination   is ____. -Referring to Scenario 14-4, the coefficient of partial determination SCENARIO 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:   -Referring to Scenario 14-4, the coefficient of partial determination   is ____. is ____.

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SCENARIO 14-11 A weight-loss clinic wants to use regression analysis to build a model for weight loss of a client (measured in pounds).Two variables thought to affect weight loss are client's length of time on the weight-loss program and time of session.These variables are described below: Y SCENARIO 14-11 A weight-loss clinic wants to use regression analysis to build a model for weight loss of a client (measured in pounds).Two variables thought to affect weight loss are client's length of time on the weight-loss program and time of session.These variables are described below: Y   Weight loss (in pounds)   Length of time in weight-loss program (in months)   1 if morning session, 0 if not Data for 25 clients on a weight-loss program at the clinic were collected and used to fit the interaction model: Y   Output from Microsoft Excel follows:     -Referring to Scenario 14-11, the overall model for predicting weight loss (Y)is statistically significant at the 0.05 level. Weight loss (in pounds) SCENARIO 14-11 A weight-loss clinic wants to use regression analysis to build a model for weight loss of a client (measured in pounds).Two variables thought to affect weight loss are client's length of time on the weight-loss program and time of session.These variables are described below: Y   Weight loss (in pounds)   Length of time in weight-loss program (in months)   1 if morning session, 0 if not Data for 25 clients on a weight-loss program at the clinic were collected and used to fit the interaction model: Y   Output from Microsoft Excel follows:     -Referring to Scenario 14-11, the overall model for predicting weight loss (Y)is statistically significant at the 0.05 level. Length of time in weight-loss program (in months) SCENARIO 14-11 A weight-loss clinic wants to use regression analysis to build a model for weight loss of a client (measured in pounds).Two variables thought to affect weight loss are client's length of time on the weight-loss program and time of session.These variables are described below: Y   Weight loss (in pounds)   Length of time in weight-loss program (in months)   1 if morning session, 0 if not Data for 25 clients on a weight-loss program at the clinic were collected and used to fit the interaction model: Y   Output from Microsoft Excel follows:     -Referring to Scenario 14-11, the overall model for predicting weight loss (Y)is statistically significant at the 0.05 level. 1 if morning session, 0 if not Data for 25 clients on a weight-loss program at the clinic were collected and used to fit the interaction model: Y SCENARIO 14-11 A weight-loss clinic wants to use regression analysis to build a model for weight loss of a client (measured in pounds).Two variables thought to affect weight loss are client's length of time on the weight-loss program and time of session.These variables are described below: Y   Weight loss (in pounds)   Length of time in weight-loss program (in months)   1 if morning session, 0 if not Data for 25 clients on a weight-loss program at the clinic were collected and used to fit the interaction model: Y   Output from Microsoft Excel follows:     -Referring to Scenario 14-11, the overall model for predicting weight loss (Y)is statistically significant at the 0.05 level. Output from Microsoft Excel follows: SCENARIO 14-11 A weight-loss clinic wants to use regression analysis to build a model for weight loss of a client (measured in pounds).Two variables thought to affect weight loss are client's length of time on the weight-loss program and time of session.These variables are described below: Y   Weight loss (in pounds)   Length of time in weight-loss program (in months)   1 if morning session, 0 if not Data for 25 clients on a weight-loss program at the clinic were collected and used to fit the interaction model: Y   Output from Microsoft Excel follows:     -Referring to Scenario 14-11, the overall model for predicting weight loss (Y)is statistically significant at the 0.05 level. SCENARIO 14-11 A weight-loss clinic wants to use regression analysis to build a model for weight loss of a client (measured in pounds).Two variables thought to affect weight loss are client's length of time on the weight-loss program and time of session.These variables are described below: Y   Weight loss (in pounds)   Length of time in weight-loss program (in months)   1 if morning session, 0 if not Data for 25 clients on a weight-loss program at the clinic were collected and used to fit the interaction model: Y   Output from Microsoft Excel follows:     -Referring to Scenario 14-11, the overall model for predicting weight loss (Y)is statistically significant at the 0.05 level. -Referring to Scenario 14-11, the overall model for predicting weight loss (Y)is statistically significant at the 0.05 level.

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SCENARIO 14-13 An econometrician is interested in evaluating the relationship of demand for building materials to mortgage rates in Los Angeles and San Francisco.He believes that the appropriate model is SCENARIO 14-13 An econometrician is interested in evaluating the relationship of demand for building materials to mortgage rates in Los Angeles and San Francisco.He believes that the appropriate model is   where   -Referring to Scenario 14-13, the fitted model for predicting demand in Los Angeles is ________. where SCENARIO 14-13 An econometrician is interested in evaluating the relationship of demand for building materials to mortgage rates in Los Angeles and San Francisco.He believes that the appropriate model is   where   -Referring to Scenario 14-13, the fitted model for predicting demand in Los Angeles is ________. -Referring to Scenario 14-13, the fitted model for predicting demand in Los Angeles is ________.

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An interaction term in a multiple regression model may be used when the relationship between An interaction term in a multiple regression model may be used when the relationship between   and Y changes for differing values of  and Y changes for differing values of An interaction term in a multiple regression model may be used when the relationship between   and Y changes for differing values of

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SCENARIO 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, SCENARIO 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,   = Salaries and   Spending:     -Referring to Scenario 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? = Salaries and SCENARIO 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,   = Salaries and   Spending:     -Referring to Scenario 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? Spending: SCENARIO 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,   = Salaries and   Spending:     -Referring to Scenario 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? SCENARIO 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,   = Salaries and   Spending:     -Referring to Scenario 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 Scenario 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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SCENARIO 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).There are 80 universities in the sample. The PHStat output is given below: SCENARIO 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).There are 80 universities in the sample. The PHStat output is given below:   -Referring to Scenario 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 Scenario 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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SCENARIO 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, SCENARIO 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,   = Salaries and   Spending:     -Referring to Scenario 14-15, there is sufficient evidence that at least one of the explanatory variables is related to the percentage of students passing the proficiency test at a 5% level of significance. = Salaries and SCENARIO 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,   = Salaries and   Spending:     -Referring to Scenario 14-15, there is sufficient evidence that at least one of the explanatory variables is related to the percentage of students passing the proficiency test at a 5% level of significance. Spending: SCENARIO 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,   = Salaries and   Spending:     -Referring to Scenario 14-15, there is sufficient evidence that at least one of the explanatory variables is related to the percentage of students passing the proficiency test at a 5% level of significance. SCENARIO 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,   = Salaries and   Spending:     -Referring to Scenario 14-15, there is sufficient evidence that at least one of the explanatory variables is related to the percentage of students passing the proficiency test at a 5% level of significance. -Referring to Scenario 14-15, there is sufficient evidence that at least one of the explanatory variables is related to the percentage of students passing the proficiency test at a 5% level of significance.

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SCENARIO 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, SCENARIO 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,   = Salaries and   Spending:     -Referring to Scenario 14-15, which of the following is a correct statement? = Salaries and SCENARIO 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,   = Salaries and   Spending:     -Referring to Scenario 14-15, which of the following is a correct statement? Spending: SCENARIO 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,   = Salaries and   Spending:     -Referring to Scenario 14-15, which of the following is a correct statement? SCENARIO 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,   = Salaries and   Spending:     -Referring to Scenario 14-15, which of the following is a correct statement? -Referring to Scenario 14-15, which of the following is a correct statement?

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SCENARIO 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).There are 80 universities in the sample. The PHStat output is given below: SCENARIO 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).There are 80 universities in the sample. The PHStat output is given below:   -Referring to Scenario 14-18, what should be the decision ('reject' or 'do not reject')on the null hypothesis when testing whether Toefl90 makes a significant contribution to the model in the presence of SAT at a 0.05 level of significance? -Referring to Scenario 14-18, what should be the decision ('reject' or 'do not reject')on the null hypothesis when testing whether Toefl90 makes a significant contribution to the model in the presence of SAT at a 0.05 level of significance?

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SCENARIO 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: SCENARIO 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 Scenario 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 Scenario 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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SCENARIO 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).There are 80 universities in the sample. The PHStat output is given below: SCENARIO 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).There are 80 universities in the sample. The PHStat output is given below:   -Referring to Scenario 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 Scenario 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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SCENARIO 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 SCENARIO 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   where   = horsepower   = 1 if 4 cylinders, 0 if 6 cylinders Y = mileage. -Referring to Scenario 14-14, the predicted mileage for a 200 horsepower, 4-cylinder car is ________. where SCENARIO 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   where   = horsepower   = 1 if 4 cylinders, 0 if 6 cylinders Y = mileage. -Referring to Scenario 14-14, the predicted mileage for a 200 horsepower, 4-cylinder car is ________. = horsepower SCENARIO 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   where   = horsepower   = 1 if 4 cylinders, 0 if 6 cylinders Y = mileage. -Referring to Scenario 14-14, the predicted mileage for a 200 horsepower, 4-cylinder car is ________. = 1 if 4 cylinders, 0 if 6 cylinders Y = mileage. -Referring to Scenario 14-14, the predicted mileage for a 200 horsepower, 4-cylinder car is ________.

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SCENARIO 14-8 A financial analyst wanted to examine the relationship between salary (in $1,000)and 2 variables: age SCENARIO 14-8 A financial analyst wanted to examine the relationship between salary (in $1,000)and 2 variables: age   = Age)and experience in the field   = 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 Scenario 14-8, the analyst wants to use a t test to test for the significance of the coefficient of   At a level of significance of 0.01, the department head would decide that  = Age)and experience in the field SCENARIO 14-8 A financial analyst wanted to examine the relationship between salary (in $1,000)and 2 variables: age   = Age)and experience in the field   = 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 Scenario 14-8, the analyst wants to use a t test to test for the significance of the coefficient of   At a level of significance of 0.01, the department head would decide that  = Exper).He took a sample of 20 employees and obtained the following Microsoft Excel output: SCENARIO 14-8 A financial analyst wanted to examine the relationship between salary (in $1,000)and 2 variables: age   = Age)and experience in the field   = 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 Scenario 14-8, the analyst wants to use a t test to test for the significance of the coefficient of   At a level of significance of 0.01, the department head would decide that  SCENARIO 14-8 A financial analyst wanted to examine the relationship between salary (in $1,000)and 2 variables: age   = Age)and experience in the field   = 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 Scenario 14-8, the analyst wants to use a t test to test for the significance of the coefficient of   At a level of significance of 0.01, the department head would decide that  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 Scenario 14-8, the analyst wants to use a t test to test for the significance of the coefficient of SCENARIO 14-8 A financial analyst wanted to examine the relationship between salary (in $1,000)and 2 variables: age   = Age)and experience in the field   = 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 Scenario 14-8, the analyst wants to use a t test to test for the significance of the coefficient of   At a level of significance of 0.01, the department head would decide that  At a level of significance of 0.01, the department head would decide that SCENARIO 14-8 A financial analyst wanted to examine the relationship between salary (in $1,000)and 2 variables: age   = Age)and experience in the field   = 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 Scenario 14-8, the analyst wants to use a t test to test for the significance of the coefficient of   At a level of significance of 0.01, the department head would decide that

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