Question 1

Regression analysis with two dependent variables and two or more independent variables is called multiple regression.

Accepted Answer

A) True 
 B)False

Question 2

The model y = $\beta$ 0 + $\beta$ 1x1 + $\beta$ 2x2 + $\beta$ 3x3 + $\varepsilon$ is a first-order regression model.

Accepted Answer

A) True 
 B)False

Question 3

A multiple regression analysis produced the following output from Minitab. Regression Analysis: Y versus x1 and x2 
 
S = 0.179449   R-Sq = 89.0%   R-Sq(adj) = 87.8%
Analysis of Variance
 
These results indicate that ____________.

Accepted Answer

A)  none of the predictor variables are significant at the 5% level 
B)  each predictor variable is significant at the 5% level 
C)  x1 is the only predictor variable significant at the 5% level 
D)  x2 is the only predictor variable significant at the 5% level 
E)  at least one of the variable is significant at 5% level 
A)  none of the predictor variables are significant at the 5% level 
B)  each predictor variable is significant at the 5% level 
C)  x1 is the only predictor variable significant at the 5% level 
D)  x2 is the only predictor variable significant at the 5% level 
E)  at least one of the variable is significant at 5% level

Question 4

The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables.   The observed F value is __________.

Accepted Answer

A) 17.50 
B) 2.33 
C) 0.70 
D) 0.43 
E) 0.50 
A) 17.50 
B) 2.33 
C) 0.70 
D) 0.43 
E) 0.50

Question 5

The mean square error (MSerr)is calculated by dividing the sum of squares error (SSerr)by the number of error degrees of freedom (dferr).

Accepted Answer

A) True 
 B)False

Question 6

The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables.   The R2 value is __________.

Accepted Answer

A) 0.80 
B) 0.70 
C) 0.66 
D) 0.76 
E) 0.30 
A) 0.80 
B) 0.70 
C) 0.66 
D) 0.76 
E) 0.30

Question 7

The following ANOVA table is from a multiple regression analysis.   The MSR value is __________.

Accepted Answer

A) 1500 
B) 50 
C) 2300 
D) 500 
E) 31 
A) 1500 
B) 50 
C) 2300 
D) 500 
E) 31

Question 8

A multiple regression analysis produced the following tables.   These results indicate that ____________.

Accepted Answer

A) none of the predictor variables are significant at the 10% level 
B) each predictor variable is significant at the 10% level 
C) x1 is significant at the 10% level 
D) x2 is significant at the 10% level 
E) the intercept is not significant at 10% level 
A) none of the predictor variables are significant at the 10% level 
B) each predictor variable is significant at the 10% level 
C) x1 is significant at the 10% level 
D) x2 is significant at the 10% level 
E) the intercept is not significant at 10% level

Question 9

The model y = $\beta$0 + $\beta$ 1x1 + $\beta$ 2x2 + $\varepsilon$ is a second-order regression model.

Accepted Answer

A) True 
 B)False

Question 10

In a multiple regression analysis with N observations and k independent variables,the degrees of freedom for the residual error is given by (N - k).

Accepted Answer

A) True 
 B)False

Question 11

The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables.   The value of the standard error of the estimate se is __________.

Accepted Answer

A) 13.23 
B) 3.16 
C) 17.32 
D) 26.46 
E) 10.00 
A) 13.23 
B) 3.16 
C) 17.32 
D) 26.46 
E) 10.00

Question 12

The following ANOVA table is from a multiple regression analysis.   The SSE value is __________.

Accepted Answer

A) 30 
B) 1500 
C) 500 
D) 800 
E) 2300 
A) 30 
B) 1500 
C) 500 
D) 800 
E) 2300

Question 13

The mean square error (MSerr)is calculated by dividing the sum of squares error (SSerr)by the number of observations in the data set (N).

Accepted Answer

A) True 
 B)False

Question 14

A multiple regression analysis produced the following tables.  
 Using $\alpha$ = 0.05 to test the null hypothesis H0: $\beta$2 = 0,the correct decision is ____.

Accepted Answer

A) fail to reject the null hypothesis 
B) reject the null hypothesis 
C) fail to reject the alternative hypothesis 
D) reject the alternative hypothesis 
E) there is not enought information provided to make a decision 
A) fail to reject the null hypothesis 
B) reject the null hypothesis 
C) fail to reject the alternative hypothesis 
D) reject the alternative hypothesis 
E) there is not enought information provided to make a decision

Question 15

A real estate appraiser is developing a regression model to predict the market value of single family residential houses as a function of heated area,number of bedrooms,number of bathrooms,age of the house,and central heating (yes,no).The response variable in this model is _______.

Accepted Answer

A) heated area 
B) number of bedrooms 
C) market value 
D) central heating 
E) residential houses 
A) heated area 
B) number of bedrooms 
C) market value 
D) central heating 
E) residential houses

Question 16

A multiple regression analysis produced the following tables.     The sample size for this analysis is ____________.

Accepted Answer

A) 17 
B) 13 
C) 16 
D) 11 
E) 15 
A) 17 
B) 13 
C) 16 
D) 11 
E) 15

Question 17

In regression analysis,outliers may be identified by examining the ________.

Accepted Answer

A) coefficient of determination 
B) coefficient of correlation 
C) p-values for the partial coefficients 
D) residuals 
E) R-squared value 
A) coefficient of determination 
B) coefficient of correlation 
C) p-values for the partial coefficients 
D) residuals 
E) R-squared value

Question 18

The following ANOVA table is from a multiple regression analysis.   The sample size for the analysis is __________.

Accepted Answer

A) 30 
B) 26 
C) 3 
D) 29 
E) 31 
A) 30 
B) 26 
C) 3 
D) 29 
E) 31

Question 19

A multiple regression analysis produced the following tables.     These results indicate that ____________.

Accepted Answer

A) none of the predictor variables are significant at the 5% level 
B) each predictor variable is significant at the 5% level 
C) x1 is the only predictor variable significant at the 5% level 
D) x2 is the only predictor variable significant at the 5% level 
E) the intercept is not significant at 5% level 
A) none of the predictor variables are significant at the 5% level 
B) each predictor variable is significant at the 5% level 
C) x1 is the only predictor variable significant at the 5% level 
D) x2 is the only predictor variable significant at the 5% level 
E) the intercept is not significant at 5% level

Question 20

If we reject H0: β1= β2=0 using the F-test,then we should conclude that both slopes are different from zero.

Accepted Answer

A) True 
 B)False

Regression analysis with two dependent variables and two or more independent variables is called multiple regression.

The model y = $\beta$ 0 + $\beta$ 1x1 + $\beta$ 2x2 + $\beta$ 3x3 + $\varepsilon$ is a first-order regression model.

A multiple regression analysis produced the following output from Minitab. Regression Analysis: Y versus x₁ and x₂ S = 0.179449 R-Sq = 89.0% R-Sq(adj) = 87.8% Analysis of Variance These results indicate that ____________.

The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables. The observed F value is __________.

The mean square error (MS_err)is calculated by dividing the sum of squares error (SS_err)by the number of error degrees of freedom (df_err).

The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables. The R2 value is __________.

The following ANOVA table is from a multiple regression analysis. The MSR value is __________.

A multiple regression analysis produced the following tables. These results indicate that ____________.

The model y = $\beta$ 0 + $\beta$ 1x1 + $\beta$ 2x2 + $\varepsilon$ is a second-order regression model.

In a multiple regression analysis with N observations and k independent variables,the degrees of freedom for the residual error is given by (N - k).

The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables. The value of the standard error of the estimate se is __________.

The following ANOVA table is from a multiple regression analysis. The SSE value is __________.

The mean square error (MS_err)is calculated by dividing the sum of squares error (SS_err)by the number of observations in the data set (N).

A real estate appraiser is developing a regression model to predict the market value of single family residential houses as a function of heated area,number of bedrooms,number of bathrooms,age of the house,and central heating (yes,no).The response variable in this model is _______.

A multiple regression analysis produced the following tables. The sample size for this analysis is ____________.

In regression analysis,outliers may be identified by examining the ________.

The following ANOVA table is from a multiple regression analysis. The sample size for the analysis is __________.

A multiple regression analysis produced the following tables. These results indicate that ____________.

If we reject H₀: β₁= β₂=0 using the F-test,then we should conclude that both slopes are different from zero.

Introduction to Statistics

Charts and Graphs

Descriptive Statistics

Probability

Discrete Distributions

Continuous Distributions

Sampling and Sampling Distributions

Statistical Inference: Estimation for Single Populations

Statistical Inference: Hypothesis Testing for Single Populations

Statistical Inferences About Two Populations

Analysis of Variance and Design of Experiments

Simple Regression Analysis and Correlation

Building Multiple Regression Models

Time-Series Forecasting and Index Numbers

Analysis of Categorical Data

Nonparametric Statistics

Statistical Quality Control

Decision Analysis

Filters

Exam 13: Multiple Regression Analysis

Regression analysis with two dependent variables and two or more independent variables is called multiple regression.

The model y = β\betaβ 0 + β\betaβ 1x1 + β\betaβ 2x2 + β\betaβ 3x3 + ε\varepsilonε is a first-order regression model.

A multiple regression analysis produced the following output from Minitab. Regression Analysis: Y versus x1 and x2 S = 0.179449 R-Sq = 89.0% R-Sq(adj) = 87.8% Analysis of Variance These results indicate that ____________.

The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables. The observed F value is __________.

The mean square error (MSerr)is calculated by dividing the sum of squares error (SSerr)by the number of error degrees of freedom (dferr).

The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables. The R2 value is __________.

The following ANOVA table is from a multiple regression analysis. The MSR value is __________.

A multiple regression analysis produced the following tables. These results indicate that ____________.

The model y = β\betaβ 0 + β\betaβ 1x1 + β\betaβ 2x2 + ε\varepsilonε is a second-order regression model.

In a multiple regression analysis with N observations and k independent variables,the degrees of freedom for the residual error is given by (N - k).

The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables. The value of the standard error of the estimate se is __________.

The following ANOVA table is from a multiple regression analysis. The SSE value is __________.

The mean square error (MSerr)is calculated by dividing the sum of squares error (SSerr)by the number of observations in the data set (N).

A multiple regression analysis produced the following tables. Using α\alphaα = 0.05 to test the null hypothesis H0: β\betaβ 2 = 0,the correct decision is ____.

A real estate appraiser is developing a regression model to predict the market value of single family residential houses as a function of heated area,number of bedrooms,number of bathrooms,age of the house,and central heating (yes,no).The response variable in this model is _______.

A multiple regression analysis produced the following tables. The sample size for this analysis is ____________.

In regression analysis,outliers may be identified by examining the ________.

The following ANOVA table is from a multiple regression analysis. The sample size for the analysis is __________.

A multiple regression analysis produced the following tables. These results indicate that ____________.

If we reject H0: β1= β2=0 using the F-test,then we should conclude that both slopes are different from zero.

Introduction to Statistics

Charts and Graphs

Descriptive Statistics

Probability

Discrete Distributions

Continuous Distributions

Sampling and Sampling Distributions

Statistical Inference: Estimation for Single Populations

Statistical Inference: Hypothesis Testing for Single Populations

Statistical Inferences About Two Populations

Analysis of Variance and Design of Experiments

Simple Regression Analysis and Correlation

Building Multiple Regression Models

Time-Series Forecasting and Index Numbers

Analysis of Categorical Data

Nonparametric Statistics

Statistical Quality Control

Decision Analysis

Filters

The model y = $\beta$ 0 + $\beta$ 1x1 + $\beta$ 2x2 + $\beta$ 3x3 + $\varepsilon$ is a first-order regression model.

A multiple regression analysis produced the following output from Minitab. Regression Analysis: Y versus x₁ and x₂ S = 0.179449 R-Sq = 89.0% R-Sq(adj) = 87.8% Analysis of Variance These results indicate that ____________.

The mean square error (MS_err)is calculated by dividing the sum of squares error (SS_err)by the number of error degrees of freedom (df_err).

The model y = $\beta$ 0 + $\beta$ 1x1 + $\beta$ 2x2 + $\varepsilon$ is a second-order regression model.

The mean square error (MS_err)is calculated by dividing the sum of squares error (SS_err)by the number of observations in the data set (N).

If we reject H₀: β₁= β₂=0 using the F-test,then we should conclude that both slopes are different from zero.