Question 1

The following ANOVA table is from a multiple regression analysis:   The adjusted R2 value is ___.

Accepted Answer

A) 0.80 
B) 0.70 
C) 0.66 
D) 0.86 
E) 0.76 
A) 0.80 
B) 0.70 
C) 0.66 
D) 0.86 
E) 0.76

Question 2

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 3

The following ANOVA table is from a multiple regression analysis:   The number of independent variables in the analysis is ___.

Accepted Answer

A) 30 
B) 25 
C) 1 
D) 5 
E) 2 
A) 30 
B) 25 
C) 1 
D) 5 
E) 2

Question 4

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 the 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 the 5% level

Question 5

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 6

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 7

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 - 1).

Accepted Answer

A) True 
 B)False

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 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 9

A real estate agent 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 10

A multiple regression analysis produced the following tables:     Using $\alpha$ = 0.10 to test the null hypothesis H0: $\beta$2 = 0, the critical t value is ___.

Accepted Answer

A) ±1.345 
B) ±1.356 
C) ±1.761 
D) ±2.782 
E) ±1.782 
A) ±1.345 
B) ±1.356 
C) ±1.761 
D) ±2.782 
E) ±1.782

Question 11

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) all variables are 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) all variables are significant at 5% level

Question 12

A market research company is developing a regression model to predict monthly household expenditures on groceries as a function of family size, household income, and household neighbourhood (urban, suburban, and rural).The response variable in this model is ___.

Accepted Answer

A) family size 
B) expenditures on groceries 
C) household income 
D) suburban 
E) household neighbourhood 
A) family size 
B) expenditures on groceries 
C) household income 
D) suburban 
E) household neighbourhood

Question 13

In a multiple regression model, the proportion of the variation of the dependent variable, y, accounted for the independent variables in the regression model is given by the coefficient of multiple correlation.

Accepted Answer

A) True 
 B)False

Question 14

A multiple regression analysis produced the following tables:     For x1= 60 and x2 = 200, the predicted value of y is ___.

Accepted Answer

A) 1,173.00 
B) 772.40 
C) 460.97 
D) 615.13 
E) 987.78 
A) 1,173.00 
B) 772.40 
C) 460.97 
D) 615.13 
E) 987.78

Question 15

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

Accepted Answer

A) True 
 B)False

Question 16

A real estate agent 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 "central heating" variable in this model is ___.

Accepted Answer

A) a response variable 
B) an independent variable 
C) a quantitative variable 
D) a dependent variable 
E) a constant 
A) a response variable 
B) an independent variable 
C) a quantitative variable 
D) a dependent variable 
E) a constant

Question 17

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

Accepted Answer

A) 12 
B) 15 
C) 14 
D) 28 
E) 24 
A) 12 
B) 15 
C) 14 
D) 28 
E) 24

Question 18

In the estimated multiple regression model y = b0 + b1x1 + b2 x2, if the values of x1 and x2 are both increased by one unit, the value of y will increase by (b1+ b2)units.

Accepted Answer

A) True 
 B)False

Question 19

A multiple regression analysis produced the following tables:     Using $\alpha$= 0.01 to test the null hypothesis H0: $\beta$1 = $\beta$2 = 0, the critical F value is ___.

Accepted Answer

A) 8.68 
B) 6.36 
C) 8.40 
D) 6.11 
E) 3.36 
A) 8.68 
B) 6.36 
C) 8.40 
D) 6.11 
E) 3.36

Question 20

In the model y = $\beta$ 0 + $\beta$ 1x1 + $\beta$ 2x2 + $\beta$ 3x3 + $\varepsilon$y is the independent variable.

Accepted Answer

A) True 
 B)False

The following ANOVA table is from a multiple regression analysis: The adjusted R²value 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).

The following ANOVA table is from a multiple regression analysis: The number of independent variables in the analysis is ___.

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

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 s_e 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).

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 - 1).

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

A real estate agent 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: These results indicate that ___.

A market research company is developing a regression model to predict monthly household expenditures on groceries as a function of family size, household income, and household neighbourhood (urban, suburban, and rural).The response variable in this model is ___.

In a multiple regression model, the proportion of the variation of the dependent variable, y, accounted for the independent variables in the regression model is given by the coefficient of multiple correlation.

A multiple regression analysis produced the following tables: For x₁= 60 and x₂ = 200, the predicted value of y is ___.

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

A real estate agent 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 "central heating" variable in this model is ___.

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

In the estimated multiple regression model y = b₀ + b₁x₁ + b₂x₂, if the values of x₁ and x₂ are both increased by one unit, the value of y will increase by (b₁+ b₂)units.

In the model y = $\beta$ ₀ + $\beta$ ₁x₁ + $\beta$ ₂x₂ + $\beta$ ₃x₃ + $\varepsilon$ y is the independent variable.

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

The following ANOVA table is from a multiple regression analysis: The adjusted R2 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).

The following ANOVA table is from a multiple regression analysis: The number of independent variables in the analysis is ___.

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

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

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 - 1).

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

A real estate agent 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: Using α\alphaα = 0.10 to test the null hypothesis H0: β\betaβ 2 = 0, the critical t value is ___.

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

A market research company is developing a regression model to predict monthly household expenditures on groceries as a function of family size, household income, and household neighbourhood (urban, suburban, and rural).The response variable in this model is ___.

In a multiple regression model, the proportion of the variation of the dependent variable, y, accounted for the independent variables in the regression model is given by the coefficient of multiple correlation.

A multiple regression analysis produced the following tables: For x1= 60 and x2 = 200, the predicted value of y is ___.

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

A real estate agent 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 "central heating" variable in this model is ___.

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

In the estimated multiple regression model y = b0 + b1x1 + b2 x2, if the values of x1 and x2 are both increased by one unit, the value of y will increase by (b1+ b2)units.

A multiple regression analysis produced the following tables: Using α\alphaα = 0.01 to test the null hypothesis H0: β\betaβ 1 = β\betaβ 2 = 0, the critical F value is ___.

In the model y = β\betaβ 0 + β\betaβ 1x1 + β\betaβ 2x2 + β\betaβ 3x3 + ε\varepsilonε y is the independent variable.

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 following ANOVA table is from a multiple regression analysis: The adjusted R²value 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).

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 s_e 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).

A multiple regression analysis produced the following tables: For x₁= 60 and x₂ = 200, the predicted value of y is ___.

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

In the estimated multiple regression model y = b₀ + b₁x₁ + b₂x₂, if the values of x₁ and x₂ are both increased by one unit, the value of y will increase by (b₁+ b₂)units.

In the model y = $\beta$ ₀ + $\beta$ ₁x₁ + $\beta$ ₂x₂ + $\beta$ ₃x₃ + $\varepsilon$ y is the independent variable.