Question 1

If the Durbin-Watson statistic has a value close to 4, which assumption is violated?

Accepted Answer

A)  Homoscedasticity 
B)  Normality of the errors 
C)  Independence of errors 
D)  None of these choices. 
A)  Homoscedasticity 
B)  Normality of the errors 
C)  Independence of errors 
D)  None of these choices.

Question 2

A(n) ____________________ value of the F-test statistic indicates that the multiple regression model is valid.

Accepted Answer

The answer of A(n) ____________________ value of the F-test statistic...

Question 3

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 (xS1U1B11S1U1B0), the cholesterol level (xS1U1B12S1U1B0), and the number of points that the individual's blood pressure exceeded the recommended value (xS1U1B13S1U1B0).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 + 1.79xS1U1B11S1U1B0 - 0.021xS1U1B12S1U1B0 -0.061xS1U1B13S1U1B0
     ANALYSIS OF VARIANCE
  
-{Life Expectancy Narrative} Is there sufficient evidence at the 5% significance level to infer that the number of points that the individual's blood pressure exceeded the recommended value and the age at death are negatively linearly related?

Accepted Answer

@#IMG-DLM& vs. @#IMG-DLM& Rejection region: t < -t_0.05,36 @#LAT-DLM& -1.69 Te

Question 4

Test the hypotheses: H0: There is no first-order autocorrelation vs.H1: There is negative first-order autocorrelation, given that: Durbin-Watson Statistic d = 1.75, n = 20, k = 2, and$\alpha$ = 0.01.

Accepted Answer

d_L = 0.86 and d_U = 1.27 The decision is ma

Question 5

One clue to the presence of multicollinearity is an independent variable known to be an important predictor that ends up having a regression coefficient that is not ____________________.

Accepted Answer

The answer of One clue to the presence of multicollinearity...

Question 6

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} What are the residual degrees of freedom that are missing from the output?

Accepted Answer

df = 1214.

Question 7

The total variation in y in a regression model will never exceed the regression sum of squares (SSR).

Accepted Answer

A) True 
 B)False

Question 8

Test the hypotheses H0: There is no first-order autocorrelation vs.H1: There is positive first-order autocorrelation, given that: Durbin-Watson Statistic d = 1.12, n = 45, k = 5, and $\alpha$= 0.05.

Accepted Answer

d_L = 1.29 and d_U = 1.78 The decision is ma

Question 9

When an additional explanatory variable is introduced into a multiple regression model, coefficient of determination adjusted for degrees of freedom can never decrease.

Accepted Answer

A) True 
 B)False

Question 10

Discuss two indicators that can be found in an analysis that suggest multicollinearity is present.

Accepted Answer

A good indicator of multicollinearity is

Question 11

For a multiple regression model, the total variation in y can be expressed as:

Accepted Answer

A)  SSE - SSR. 
B)  SSR - SSE. 
C)  SSR + SSE. 
D)  SSR / SSE. 
A)  SSE - SSR. 
B)  SSR - SSE. 
C)  SSR + SSE. 
D)  SSR / SSE.

Question 12

In a multiple regression model, the mean of the probability distribution of the error variable  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 13

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 14

A multiple regression is called "multiple" because it has several explanatory variables.

Accepted Answer

A) True 
 B)False

Question 15

Student's Final Grade
A statistics professor investigated some of the factors that affect an individual student's final grade in her course.She proposed the multiple regression model   , where y is the final grade (out of 100 points), xS1U1B11S1U1B0 is the number of lectures skipped, xS1U1B12S1U1B0 is the number of late assignments, and xS1U1B13S1U1B0 is the midterm exam score (out of 100).The professor recorded the data for 50 randomly selected students.The computer output is shown below.
THE REGRESSION EQUATION IS       ANALYSIS OF VARIANCE
  
-{Student's Final Grade Narrative} Interpret the coefficient b2.

Accepted Answer

b₂ = -1.17.This tells us that for each ad

Question 16

One of the consequences of multicollinearity in multiple regression is biased estimates on the slope coefficients.

Accepted Answer

A) True 
 B)False

Question 17

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.

Question 18

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 (xS1U1B11S1U1B0), the cholesterol level (xS1U1B12S1U1B0), and the number of points that the individual's blood pressure exceeded the recommended value (xS1U1B13S1U1B0).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 + 1.79xS1U1B11S1U1B0 - 0.021xS1U1B12S1U1B0 -0.061xS1U1B13S1U1B0
     ANALYSIS OF VARIANCE
  
-{Life Expectancy Narrative} Interpret the coefficient b2.

Accepted Answer

b₂ = -0.021.This tells us that for each a

Question 19

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} What minimum annual income would an individual with a family size of 9 and 10 years of education need to attain a predicted 5,000 square foot home?

Accepted Answer

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

Question 20

If the Durbin-Watson statistic, d, has values greater than 2, this indicates

Accepted Answer

A)  a positive first-order autocorrelation 
B)  a negative first-order autocorrelation 
C)  no first-order autocorrelation at all 
D)  None of these choices. 
A)  a positive first-order autocorrelation 
B)  a negative first-order autocorrelation 
C)  no first-order autocorrelation at all 
D)  None of these choices.

If the Durbin-Watson statistic has a value close to 4, which assumption is violated?

A(n) ____________________ value of the F-test statistic indicates that the multiple regression model is valid.

Test the hypotheses: H₀: There is no first-order autocorrelation vs.H₁: There is negative first-order autocorrelation, given that: Durbin-Watson Statistic d = 1.75, n = 20, k = 2, and $\alpha$ = 0.01.

One clue to the presence of multicollinearity is an independent variable known to be an important predictor that ends up having a regression coefficient that is not ____________________.

The total variation in y in a regression model will never exceed the regression sum of squares (SSR).

Test the hypotheses H₀: There is no first-order autocorrelation vs.H₁: There is positive first-order autocorrelation, given that: Durbin-Watson Statistic d = 1.12, n = 45, k = 5, and $\alpha$ = 0.05.

When an additional explanatory variable is introduced into a multiple regression model, coefficient of determination adjusted for degrees of freedom can never decrease.

Discuss two indicators that can be found in an analysis that suggest multicollinearity is present.

For a multiple regression model, the total variation in y can be expressed as:

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

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

A multiple regression is called "multiple" because it has several explanatory variables.

One of the consequences of multicollinearity in multiple regression is biased estimates on the slope coefficients.

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

If the Durbin-Watson statistic, d, has values greater than 2, this indicates

What Is Statistics

Graphical Descriptive Techniques I

Graphical Descriptive Techniques II

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 Populations

Analysis of Variance

Chi-Squared Tests

Simple Linear Regression and Correlation

Filters

Exam 17: Multiple Regression

If the Durbin-Watson statistic has a value close to 4, which assumption is violated?

A(n) ____________________ value of the F-test statistic indicates that the multiple regression model is valid.

Test the hypotheses: H0: There is no first-order autocorrelation vs.H1: There is negative first-order autocorrelation, given that: Durbin-Watson Statistic d = 1.75, n = 20, k = 2, and α\alphaα = 0.01.

One clue to the presence of multicollinearity is an independent variable known to be an important predictor that ends up having a regression coefficient that is not ____________________.

The total variation in y in a regression model will never exceed the regression sum of squares (SSR).

Test the hypotheses H0: There is no first-order autocorrelation vs.H1: There is positive first-order autocorrelation, given that: Durbin-Watson Statistic d = 1.12, n = 45, k = 5, and α\alphaα = 0.05.

When an additional explanatory variable is introduced into a multiple regression model, coefficient of determination adjusted for degrees of freedom can never decrease.

Discuss two indicators that can be found in an analysis that suggest multicollinearity is present.

For a multiple regression model, the total variation in y can be expressed as:

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

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

A multiple regression is called "multiple" because it has several explanatory variables.

One of the consequences of multicollinearity in multiple regression is biased estimates on the slope coefficients.

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

If the Durbin-Watson statistic, d, has values greater than 2, this indicates

What Is Statistics

Graphical Descriptive Techniques I

Graphical Descriptive Techniques II

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 Populations

Analysis of Variance

Chi-Squared Tests

Simple Linear Regression and Correlation

Filters

Test the hypotheses: H₀: There is no first-order autocorrelation vs.H₁: There is negative first-order autocorrelation, given that: Durbin-Watson Statistic d = 1.75, n = 20, k = 2, and $\alpha$ = 0.01.

Test the hypotheses H₀: There is no first-order autocorrelation vs.H₁: There is positive first-order autocorrelation, given that: Durbin-Watson Statistic d = 1.12, n = 45, k = 5, and $\alpha$ = 0.05.