Question 1

The variance of the error variable   is required to be constant. When this requirement is violated, the condition is called heteroscedasticity.

Accepted Answer

A) True 
 B)False

Question 2

In a multiple regression model, the standard deviation of the error variable e is assumed to be:

Accepted Answer

A) 0. 
B) 1.0. 
C) constant. 
D) None of these choices. 
A) 0. 
B) 1.0. 
C) constant. 
D) None of these choices.

Question 3

Marc Anthony Concert At a recent Marc Anthony concert, a survey was conducted that asked a random sample of 20 people their age and how many concerts they have attended since the first of the year. The following data were collected:   An Excel output follows:   {Marc Anthony Concert Narrative} Draw a histogram of the residuals.

Accepted Answer

The answer of Marc Anthony Concert At a recent Marc...

Question 4

A multiple regression model is assessed to be poor if the error sum of squares SSE and the standard error of estimate s e  are both large, the coefficient of determination R 2 is close to 0, and the value of the test statistic F is large.

Accepted Answer

A) True 
 B)False

Question 5

Life Expectancy An actuary wanted to develop a model to predict how long individuals will live. After consulting a number of physicians, she collected the age at death ( y ), the average number of hours of exercise per week ( x 1), the cholesterol level ( x 2), and the number of points that the individual's blood pressure exceeded the recommended value ( x 3). A random sample of 40 individuals was selected. The computer output of the multiple regression model is shown below. THE REGRESSION EQUATION IS y = 55.8 + 1.79 x 1 - 0.021 x 2 - 0.061 x 3   
 S = 9.47 R - Sq = 22.5% 
 ANALYSIS OF VARIANCE   {Life Expectancy Narrative} Interpret the coefficient b 2.

Accepted Answer

b ₂ = - 0.021. This tells us that for eac

Question 6

There are several clues to the presence of multicollinearity. One clue is when a regression coefficient exhibits the wrong ____________________.

Accepted Answer

The answer of There are several clues to the presence...

Question 7

The coefficient of determination R 2 measures the proportion of variation in y that is explained by the explanatory variables included in the model.

Accepted Answer

A) True 
 B)False

Question 8

A multiple regression model has the form   . The coefficient b 1 is interpreted as the average change in y per unit change in x 1.

Accepted Answer

A) True 
 B)False

Question 9

The parameter estimates are biased when multicollinearity is present in a multiple regression equation.

Accepted Answer

A) True 
 B)False

Question 10

In a multiple regression model, the mean of the probability distribution of the error variable e is assumed to be:

Accepted Answer

A) k , where k is the number of independent variables included in the model. 
B) 1.0 
C) 0.0 
D) None of these choices. 
A) k , where k is the number of independent variables included in the model. 
B) 1.0 
C) 0.0 
D) None of these choices.

Question 11

Multicollinearity is present when there is a high degree of correlation between the independent variables included in the regression model.

Accepted Answer

A) True 
 B)False

Question 12

One of the consequences of multicollinearity in multiple regression is inflated standard errors in some or all of the estimated slope coefficients.

Accepted Answer

A) True 
 B)False

Question 13

Real Estate Builder A real estate builder wishes to determine how house size is influenced by family income, family size, and education of the head of household. House size is measured in hundreds of square feet, income is measured in thousands of dollars, and education is measured in years. A partial computer output is shown below. SUMMARY OUTPUT   ANOVA 
       {Real Estate Builder Narrative} Suppose the builder wants to test whether the coefficient on income is significantly different from 0. What is the value of the relevant t -statistic?

Accepted Answer

The answer of Real Estate Builder A real estate builder...

Question 14

Life Expectancy An actuary wanted to develop a model to predict how long individuals will live. After consulting a number of physicians, she collected the age at death ( y ), the average number of hours of exercise per week ( x 1), the cholesterol level ( x 2), and the number of points that the individual's blood pressure exceeded the recommended value ( x 3). A random sample of 40 individuals was selected. The computer output of the multiple regression model is shown below. THE REGRESSION EQUATION IS y = 55.8 + 1.79 x 1 - 0.021 x 2 - 0.061 x 3   
 S = 9.47 R - Sq = 22.5% 
 ANALYSIS OF VARIANCE   {Life Expectancy Narrative} Is there enough evidence at the 5% significance level to infer that the cholesterol level and the age at death are negatively linearly related?

Accepted Answer

@#IMG-DLM& vs. @#IMG-DLM& Rejection region: t - t _0.05,36 » - 1.69

Question 15

Because of multicollinearity, the t -tests of the individual coefficients may indicate that some independent variables are not linearly related to the dependent variable, when in fact they are.

Accepted Answer

A) True 
 B)False

Question 16

Real Estate Builder A real estate builder wishes to determine how house size is influenced by family income, family size, and education of the head of household. House size is measured in hundreds of square feet, income is measured in thousands of dollars, and education is measured in years. A partial computer output is shown below. SUMMARY OUTPUT 
   ANOVA 
       {Real Estate Builder Narrative} At the 0.01 level of significance, what conclusion should the builder draw regarding the inclusion of education in the regression model?

Accepted Answer

Education is not significant i

Question 17

Consider the following statistics of a multiple regression model: Total variation in y = 1000, SSE = 300, n = 50, and k = 4. 
 a. Determine the standard error of estimate. 
 b. Determine the coefficient of determination. 
 c. Determine the F -statistic.

Accepted Answer

a. s_e = 2.582 b. R

Question 18

Which of the following statements regarding multicollinearity is not true?

Accepted Answer

A) It exists in virtually all multiple regression models. 
B) It is also called collinearity and intercorrelation. 
C) It is a condition that exists when the independent variables are highly correlated with the dependent variable. 
D) All of these choices are true. 
A) It exists in virtually all multiple regression models. 
B) It is also called collinearity and intercorrelation. 
C) It is a condition that exists when the independent variables are highly correlated with the dependent variable. 
D) All of these choices are true.

Question 19

Life Expectancy An actuary wanted to develop a model to predict how long individuals will live. After consulting a number of physicians, she collected the age at death ( y ), the average number of hours of exercise per week ( x 1), the cholesterol level ( x 2), and the number of points that the individual's blood pressure exceeded the recommended value ( x 3). A random sample of 40 individuals was selected. The computer output of the multiple regression model is shown below. THE REGRESSION EQUATION IS y = 55.8 + 1.79 x 1 - 0.021 x 2 - 0.061 x 3   S = 9.47 R - Sq = 22.5%   {Life Expectancy Narrative} What is the coefficient of determination? What does this statistic tell you?

Accepted Answer

R ² = 0.225. This means that 22.5% of the

Question 20

If multicollinearity exists among the independent variables included in a multiple regression model, then:

Accepted Answer

A) the regression coefficients will be difficult to interpret. 
B) the standard errors of the regression coefficients for the correlated independent variables will increase. 
C) one or more of the coefficients may have the wrong sign. 
D) All of these choices are true. 
A) the regression coefficients will be difficult to interpret. 
B) the standard errors of the regression coefficients for the correlated independent variables will increase. 
C) one or more of the coefficients may have the wrong sign. 
D) All of these choices are true.

The variance of the error variable is required to be constant. When this requirement is violated, the condition is called heteroscedasticity.

In a multiple regression model, the standard deviation of the error variable e is assumed to be:

A multiple regression model is assessed to be poor if the error sum of squares SSE and the standard error of estimate s_e are both large, the coefficient of determination R ² is close to 0, and the value of the test statistic F is large.

There are several clues to the presence of multicollinearity. One clue is when a regression coefficient exhibits the wrong ____________________.

The coefficient of determination R ² measures the proportion of variation in y that is explained by the explanatory variables included in the model.

A multiple regression model has the form . The coefficient b ₁ is interpreted as the average change in y per unit change in x ₁.

The parameter estimates are biased when multicollinearity is present in a multiple regression equation.

In a multiple regression model, the mean of the probability distribution of the error variable e is assumed to be:

Multicollinearity is present when there is a high degree of correlation between the independent variables included in the regression model.

One of the consequences of multicollinearity in multiple regression is inflated standard errors in some or all of the estimated slope coefficients.

Because of multicollinearity, the t -tests of the individual coefficients may indicate that some independent variables are not linearly related to the dependent variable, when in fact they are.

Consider the following statistics of a multiple regression model: Total variation in y = 1000, SSE = 300, n = 50, and k = 4. a. Determine the standard error of estimate. b. Determine the coefficient of determination. c. Determine the F -statistic.

Which of the following statements regarding multicollinearity is not true?

If multicollinearity exists among the independent variables included in a multiple regression model, then:

What Is Statistics

Graphical and Tabular Descriptive Techniques

Numerical Descriptive Techniques

Data Collection and Sampling

Probability

Random Variables and Discrete Probability Distributions

Continuous Probability Distributions

Sampling Distributions

Introduction to Estimation

Introduction to Hypothesis Testing

Inference About a Population

Inference About Comparing Two Populat

Inference About Comparing Two Populat

Analysis of Variance

Chi-Squared Tests

Simple Linear Regression and Correlat

Review of Statistical Inference

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Exam 17: Multiple Regression

The variance of the error variable is required to be constant. When this requirement is violated, the condition is called heteroscedasticity.

In a multiple regression model, the standard deviation of the error variable e is assumed to be:

A multiple regression model is assessed to be poor if the error sum of squares SSE and the standard error of estimate s e are both large, the coefficient of determination R 2 is close to 0, and the value of the test statistic F is large.

There are several clues to the presence of multicollinearity. One clue is when a regression coefficient exhibits the wrong ____________________.

The coefficient of determination R 2 measures the proportion of variation in y that is explained by the explanatory variables included in the model.

A multiple regression model has the form . The coefficient b 1 is interpreted as the average change in y per unit change in x 1.

The parameter estimates are biased when multicollinearity is present in a multiple regression equation.

In a multiple regression model, the mean of the probability distribution of the error variable e is assumed to be:

Multicollinearity is present when there is a high degree of correlation between the independent variables included in the regression model.

One of the consequences of multicollinearity in multiple regression is inflated standard errors in some or all of the estimated slope coefficients.

Because of multicollinearity, the t -tests of the individual coefficients may indicate that some independent variables are not linearly related to the dependent variable, when in fact they are.

Consider the following statistics of a multiple regression model: Total variation in y = 1000, SSE = 300, n = 50, and k = 4. a. Determine the standard error of estimate. b. Determine the coefficient of determination. c. Determine the F -statistic.

Which of the following statements regarding multicollinearity is not true?

If multicollinearity exists among the independent variables included in a multiple regression model, then:

What Is Statistics

Graphical and Tabular Descriptive Techniques

Numerical Descriptive Techniques

Data Collection and Sampling

Probability

Random Variables and Discrete Probability Distributions

Continuous Probability Distributions

Sampling Distributions

Introduction to Estimation

Introduction to Hypothesis Testing

Inference About a Population

Inference About Comparing Two Populat

Inference About Comparing Two Populat

Analysis of Variance

Chi-Squared Tests

Simple Linear Regression and Correlat

Review of Statistical Inference

Filters

A multiple regression model is assessed to be poor if the error sum of squares SSE and the standard error of estimate s_e are both large, the coefficient of determination R ² is close to 0, and the value of the test statistic F is large.

The coefficient of determination R ² measures the proportion of variation in y that is explained by the explanatory variables included in the model.

A multiple regression model has the form . The coefficient b ₁ is interpreted as the average change in y per unit change in x ₁.