SCENARIO 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 ( XS1U1B11S1U1B0 ) and the amount of insulation in inches ( X S1U1B12S1U1B0 ). Given below is EXCEL output of the regression model. Also SSR (XS1U1B11S1U1B0 | XS1U1B12S1U1B0) = 8343.3572 and SSR (XS1U1B12S1U1B0 | XS1U1B11S1U1B0) = 4199.2672 -Referring to Scenario 14-5,what is the p-value for testing whether Capital has a positive influence on corporate sales?

A) 0.025 B) 0.05 C) 0.2743 D) 0.5485 A) 0.025 B) 0.05 C) 0.2743 D) 0.5485

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 (XS1U1B11S1U1B0)and how he/she scored on a business aptitude test (XS1U1B12S1U1B0).A random sample of 8 employees provides the following: -Referring to Scenario 14-1,for these data,what is the value for the regression constant,b0?

A) 0.998 B) 3.103 C) 4.698 D) 21.293 A) 0.998 B) 3.103 C) 4.698 D) 21.293

SCENARIO 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 ( XS1U1B11S1U1B0 ) and the amount of insulation in inches ( X S1U1B12S1U1B0 ). Given below is EXCEL output of the regression model. Also SSR (XS1U1B11S1U1B0 | XS1U1B12S1U1B0) = 8343.3572 and SSR (XS1U1B12S1U1B0 | XS1U1B11S1U1B0) = 4199.2672 -Referring to Scenario 14-5,what are the predicted sales (in millions of dollars)for a company spending $100 million on capital and $100 million on wages?

A) 15,800.00 B) 16,520.07 C) 17,277.49 D) 20,455.98 A) 15,800.00 B) 16,520.07 C) 17,277.49 D) 20,455.98

SCENARIO 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 ( XS1U1B11S1U1B0 ) and the amount of insulation in inches ( X S1U1B12S1U1B0 ). Given below is EXCEL output of the regression model. Also SSR (XS1U1B11S1U1B0 | XS1U1B12S1U1B0) = 8343.3572 and SSR (XS1U1B12S1U1B0 | XS1U1B11S1U1B0) = 4199.2672 -Referring to Scenario 14-6,the estimated value of the regression parameter $\beta$1 in means that

A) holding the effect of the amount of insulation constant,an estimated expected $1 increase in heating costs is associated with a decrease in the daily minimum outside temperature by 2.76 degrees. B) holding the effect of the amount of insulation constant,a 1 degree increase in the daily minimum outside temperature results in a decrease in heating costs by $2.76. C) holding the effect of the amount of insulation constant,a 1 degree increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs by $2.76. D) holding the effect of the amount of insulation constant,a 1% increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs by 2.76%. A) holding the effect of the amount of insulation constant,an estimated expected $1 increase in heating costs is associated with a decrease in the daily minimum outside temperature by 2.76 degrees. B) holding the effect of the amount of insulation constant,a 1 degree increase in the daily minimum outside temperature results in a decrease in heating costs by $2.76. C) holding the effect of the amount of insulation constant,a 1 degree increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs by $2.76. D) holding the effect of the amount of insulation constant,a 1% increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs by 2.76%.

Exam 14: Introduction to Multiple Regression

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,we can conclude definitively that,holding constant the effect of the other independent variable,age has no impact on the mean number of weeks a worker is unemployed due to a layoff at a 1% level of significance if all we have is the information of the 95% confidence interval estimate for the effect of a one year increase in age on the mean number of weeks a worker is unemployed due to a layoff.

(True/False)

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Question 201

If a categorical independent variable contains 2 categories,then _____ dummy variable(s)will be needed to uniquely represent these categories.

(Multiple Choice)

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Question 202

SCENARIO 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 ( X ₂ ). Given below is EXCEL output of the regression model. Also SSR (X₁ | X₂) = 8343.3572 and SSR (X₂ | X₁) = 4199.2672 -Referring to Scenario 14-5,what is the p-value for testing whether Capital has a positive influence on corporate sales?

(Multiple Choice)

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Question 203

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 (X₁)and how he/she scored on a business aptitude test (X₂).A random sample of 8 employees provides the following: -Referring to Scenario 14-1,for these data,what is the value for the regression constant,b₀?

(Multiple Choice)

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Question 204

SCENARIO 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: $SCENARIO 14-8 A financial analyst wanted to examine the relationship between salary (in $1,000) and 2 variables: age (X1 = Age) and experience in the field (X2 = Exper). He took a sample of 20 employees and obtained the following Microsoft Excel output: Also, the sum of squares due to the regression for the model that includes only Age is 5022.0654 while the sum of squares due to the regression for the model that includes only Exper is 125.9848. -Referring to Scenario 14-7,the department head wants to use a t test to test for the significance of the coefficient of X1.At a level of significance of 0.05,the department head would decide that \beta 1 \neq 0.$ $SCENARIO 14-8 A financial analyst wanted to examine the relationship between salary (in $1,000) and 2 variables: age (X1 = Age) and experience in the field (X2 = Exper). He took a sample of 20 employees and obtained the following Microsoft Excel output: Also, the sum of squares due to the regression for the model that includes only Age is 5022.0654 while the sum of squares due to the regression for the model that includes only Exper is 125.9848. -Referring to Scenario 14-7,the department head wants to use a t test to test for the significance of the coefficient of X1.At a level of significance of 0.05,the department head would decide that \beta 1 \neq 0.$ 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 14-7,the department head wants to use a t test to test for the significance of the coefficient of X₁.At a level of significance of 0.05,the department head would decide that $\beta$ ₁ $\neq$ 0.

(True/False)

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Question 205

SCENARIO 14-15 The superintendent of a school district wanted to predict the percentage of students passing a sixthgrade 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 X ₂ = Spending: $SCENARIO 14-15 The superintendent of a school district wanted to predict the percentage of students passing a sixthgrade proficiency test. She obtained the data on percentage of students passing the proficiency test (% Passing), mean teacher salary in thousands of dollars (Salaries), and instructional spending per pupil in thousands of dollars (Spending) of 47 schools in the state. Following is the multiple regression output with Y = % Passing as the dependent variable, X1 = Salaries and X 2 = Spending: -Referring to Scenario 14-15,the null hypothesis H1: \beta 1= \beta 2=0implies that percentage of students passing the proficiency test is not affected by either of the explanatory variables.$ $SCENARIO 14-15 The superintendent of a school district wanted to predict the percentage of students passing a sixthgrade proficiency test. She obtained the data on percentage of students passing the proficiency test (% Passing), mean teacher salary in thousands of dollars (Salaries), and instructional spending per pupil in thousands of dollars (Spending) of 47 schools in the state. Following is the multiple regression output with Y = % Passing as the dependent variable, X1 = Salaries and X 2 = Spending: -Referring to Scenario 14-15,the null hypothesis H1: \beta 1= \beta 2=0implies that percentage of students passing the proficiency test is not affected by either of the explanatory variables.$ -Referring to Scenario 14-15,the null hypothesis H₁: $\beta$ ₁= $\beta$ ₂=0implies that percentage of students passing the proficiency test is not affected by either of the explanatory variables.

(True/False)

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Question 206

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: $SCENARIO 14-17 Given below are results from the regression analysis where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Unemploy)and the independent variables are the age of the worker (Age)and a dummy variable for management position (Manager: 1 = yes,0 = no). The results of the regression analysis are given below: -Referring to Scenario 14-17,the null hypothesis H0: \beta 1= \beta 2=0implies that the number of weeks a worker is unemployed due to a layoff is not affected by any of the explanatory variables.$ $SCENARIO 14-17 Given below are results from the regression analysis where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Unemploy)and the independent variables are the age of the worker (Age)and a dummy variable for management position (Manager: 1 = yes,0 = no). The results of the regression analysis are given below: -Referring to Scenario 14-17,the null hypothesis H0: \beta 1= \beta 2=0implies that the number of weeks a worker is unemployed due to a layoff is not affected by any of the explanatory variables.$ -Referring to Scenario 14-17,the null hypothesis H₀: $\beta$ ₁= $\beta$ ₂=0implies that the number of weeks a worker is unemployed due to a layoff is not affected by any of the explanatory variables.

(True/False)

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Question 207

SCENARIO 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 ( X ₂ ). Given below is EXCEL output of the regression model. Also SSR (X₁ | X₂) = 8343.3572 and SSR (X₂ | X₁) = 4199.2672 -Referring to Scenario 14-5,what are the predicted sales (in millions of dollars)for a company spending $100 million on capital and $100 million on wages?

(Multiple Choice)

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Question 208

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,there is sufficient evidence that the number of weeks a worker is unemployed due to a layoff depends on all of the explanatory variables at a 10% level of significance.

(True/False)

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Question 209

SCENARIO 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 ( X ₂ ). Given below is EXCEL output of the regression model. Also SSR (X₁ | X₂) = 8343.3572 and SSR (X₂ | X₁) = 4199.2672 -Referring to Scenario 14-6,the value of the partial F test statistic is _____ for H₀ : Variable X₂ does not significantly improve the model after variable X₁ has been included H₁ : Variable X₂ significantly improves the model after variable X₁ has been included

(Short Answer)

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Question 210

SCENARIO 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 ( X ₂ ). Given below is EXCEL output of the regression model. $SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y). To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit ( X1 ) and the amount of insulation in inches ( X 2 ). Given below is EXCEL output of the regression model. Also SSR (X1 | X2) = 8343.3572 and SSR (X2 | X1) = 4199.2672 -Referring to Scenario 14-6,the estimated value of the regression parameter \beta 1 in means that$ $SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y). To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit ( X1 ) and the amount of insulation in inches ( X 2 ). Given below is EXCEL output of the regression model. Also SSR (X1 | X2) = 8343.3572 and SSR (X2 | X1) = 4199.2672 -Referring to Scenario 14-6,the estimated value of the regression parameter \beta 1 in means that$ $SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y). To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit ( X1 ) and the amount of insulation in inches ( X 2 ). Given below is EXCEL output of the regression model. Also SSR (X1 | X2) = 8343.3572 and SSR (X2 | X1) = 4199.2672 -Referring to Scenario 14-6,the estimated value of the regression parameter \beta 1 in means that$ Also SSR (X₁ | X₂) = 8343.3572 and SSR (X₂ | X₁) = 4199.2672 -Referring to Scenario 14-6,the estimated value of the regression parameter $\beta$ ₁in means that

(Multiple Choice)

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Question 211

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 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?

(Short Answer)

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Question 212

The variation attributable to factors other than the relationship between the independent variables and the explained variable in a regression analysis is represented by

(Multiple Choice)

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Question 213

Multiple regression is the process of using several independent variables to predict a number of dependent variables.

(True/False)

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Question 214

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,we can conclude definitively that,holding constant the effect of the other independent variables,there is not a 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 at a 1% level of significance if all we have is the information 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.

(True/False)

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Question 215

SCENARIO 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: 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 partial F test for H₀ : Variable X₁ does not significantly improve the model after variable X₂ has been included H₁ : Variable X₁ significantly improves the model after variable X₂ has been included has _ and _ degrees of freedom.

(Short Answer)

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Question 216

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 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?

(Short Answer)

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Question 217

When an additional explanatory variable is introduced into a multiple regression model,the adjusted r² can never decrease.

(True/False)

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Question 218

SCENARIO 14-15 The superintendent of a school district wanted to predict the percentage of students passing a sixthgrade 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 X ₂ = Spending: -Referring to Scenario 14-15,the null hypothesis should be rejected at a 5% level of significance when testing whether instructional spending per pupil has any effect on percentage of students passing the proficiency test,considering the effect of mean teacher salary.

(True/False)

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Question 219

A regression had the following results: SST = 102.55,SSE = 82.04.It can be said that 90.0% of the variation in the dependent variable is explained by the independent variables in the regression.

(True/False)

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Question 220

Showing 201 - 220 of 256

If a categorical independent variable contains 2 categories,then _____ dummy variable(s)will be needed to uniquely represent these categories.

The variation attributable to factors other than the relationship between the independent variables and the explained variable in a regression analysis is represented by

Multiple regression is the process of using several independent variables to predict a number of dependent variables.

When an additional explanatory variable is introduced into a multiple regression model,the adjusted r² can never decrease.

A regression had the following results: SST = 102.55,SSE = 82.04.It can be said that 90.0% of the variation in the dependent variable is explained by the independent variables in the regression.

Defining and Collecting Data

Organizing and Visualizing Variables

Numerical Descriptive Measures

Basic Probability

Discrete Probability Distributions

The Normal Distribution and Other Continuous Distributions

Sampling Distributions

Confidence Interval Estimation

Fundamentals of Hypothesis Testing: One-Sample Tests

Two-Sample Tests

Analysis of Variance

Chi-Square and Nonparametric Tests

Simple Linear Regression

Multiple Regression Model Building

Time-Series Forecasting

Business Analytics

A Roadmap for Analyzing Data

Statistical Applications in Quality Management

Decision Making

Getting Started: Important Things to Learn First

Binomial Distribution and Normal Approximation

Filters

Exam 14: Introduction to Multiple Regression

If a categorical independent variable contains 2 categories,then _____ dummy variable(s)will be needed to uniquely represent these categories.

The variation attributable to factors other than the relationship between the independent variables and the explained variable in a regression analysis is represented by

Multiple regression is the process of using several independent variables to predict a number of dependent variables.

When an additional explanatory variable is introduced into a multiple regression model,the adjusted r2 can never decrease.

A regression had the following results: SST = 102.55,SSE = 82.04.It can be said that 90.0% of the variation in the dependent variable is explained by the independent variables in the regression.

Defining and Collecting Data

Organizing and Visualizing Variables

Numerical Descriptive Measures

Basic Probability

Discrete Probability Distributions

The Normal Distribution and Other Continuous Distributions

Sampling Distributions

Confidence Interval Estimation

Fundamentals of Hypothesis Testing: One-Sample Tests

Two-Sample Tests

Analysis of Variance

Chi-Square and Nonparametric Tests

Simple Linear Regression

Multiple Regression Model Building

Time-Series Forecasting

Business Analytics

A Roadmap for Analyzing Data

Statistical Applications in Quality Management

Decision Making

Getting Started: Important Things to Learn First

Binomial Distribution and Normal Approximation

Filters

When an additional explanatory variable is introduced into a multiple regression model,the adjusted r² can never decrease.