A multiple regression analysis produced the following tables. \[\begin{array} { | c | c | c | c | c | } \hline \text { Predictor } & \text { Coefficients } & \text { Stardard Error } & \boldsymbol { t } \text { Statistic } & p \text {-value } \\ \hline \text { Irtercept } & - 139.609 & 2548.989 & - 0.05477 & 0.957154 \\ \hline \boldsymbol { x } _ { 1 } & 24.24619 & 22.25267 & 1.089586 & \mathbf { 0 . 2 9 5 6 8 2 } \\ \hline \mathbf { x } _ { 2 } & 32.10171 & 17.44559 & 1.840105 & 0.08869 \\ \hline \end{array}\] \[\begin{array} { | c | c | c | c | c | c | } \hline \text { Source } & \mathrm { df } & \text { SS } & \text { MS } & F & p \text {-value } \\ \hline \text { Repression } & 2 & 302689 & 151344.5 & 1.705942 & 0.219838 \\ \hline \text { Residual } & 13 & 1153309 & 88716.07 & & \\ \hline \text { Total } & 15 & 1455998 & & & \\ \hline \end{array}\] The regression equation for this analysis is ____________.

A) = 302689 + 1153309 x1 + 1455998 x2 B) = -139.609 + 24.24619 x1 + 32.10171 x2 C) = 2548.989 + 22.25267 x1 + 17.44559 x2 D) = -0.05477 + 1.089586 x1 + 1.840105 x2 E) = 0.05477 + 1.089586 x1 + 1.840105 x2 A) = 302689 + 1153309 x1 + 1455998 x2 B) = -139.609 + 24.24619 x1 + 32.10171 x2 C) = 2548.989 + 22.25267 x1 + 17.44559 x2 D) = -0.05477 + 1.089586 x1 + 1.840105 x2 E) = 0.05477 + 1.089586 x1 + 1.840105 x2

A multiple regression analysis produced the following tables. \[\begin{array} { | c | c | c | c | c | } \hline \text { Predictor } & \text { Coefficients } & \text { Stardard Error } & t \text { Statistic } & p \text {-value } \\ \hline \text { Irtercept } & 624.5369 & 78.49712 & 7.956176 & 6.88 E - 06 \\ \hline \boldsymbol { x } _ { 1 } & 8.569122 & 1.652255 & 5.186319 & \mathbf { 0 . 0 0 0 3 0 1 } \\ \hline \mathbf { x } _ { 2 } & 4.736515 & 0.699194 & 6.774248 & \mathbf { 3 . 0 6 E - 0 5 } \\ \hline \end{array}\] \[\begin{array} { | c | c | c | c | c | c | } \hline \text { Source } & \mathrm { df } & \text { SS } & \text { MS } & F & p \text {-value } \\ \hline \text { Repression } & 2 & 1660914 & \mathbf { 8 3 0 4 5 7 . 1 } & 58.31956 & 1.4 \mathrm { E } - 06 \\ \hline \text { Residual } & 11 & 156637.5 & 14239.77 & & \\ \hline \text { Total } & 13 & 1817552 & & & \\ \hline \end{array}\] The coefficient of multiple determination is ____________.

A) 0.0592 B) 0.9138 C) 0.1149 D) 0.9559 E) 1.0000 A) 0.0592 B) 0.9138 C) 0.1149 D) 0.9559 E) 1.0000

A multiple regression analysis produced the following output from Minitab. Regression Analysis: Y versus x1 and x2 Predictor Coef SE Coef T P Constant -0.0626 0.2034 -0.31 0.762 X1 1.1003 0.5441 2.02 0.058 X2 -0.8960 0.5548 -1.61 0.124 S = 0.179449 R-Sq = 89.0% R-Sq(adj)= 87.8% Analysis of Variance Source DF SS MS F P Regression 2 4.7013 2.3506 73.00 0.000 Residual Error 18 0.5796 0.0322 Total 20 5.2809 These results indicate that ____________.

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 variables 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 variables is significant at 5% level

The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables. \[\begin{array} { | c | c | c | c | c | c | } \hline \text { Source } & \mathrm { df } & \mathrm { SS } & \mathrm { MS } & F & p \\ \hline \text { Repression } & & 700 & & & \\ \hline \text { Error } & & & & & \\ \hline \text { Total } & & 1000 & & & \\ \hline \end{array}\] The observed F value is __________.

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

The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables. \[\begin{array} { | c | c | c | c | c | c | } \hline \text { Source } & \mathrm { df } & \mathrm { SS } & \mathrm { MS } & F & p \\ \hline \text { Repression } & & 700 & & & \\ \hline \text { Error } & & & & & \\ \hline \text { Total } & & 1000 & & & \\ \hline \end{array}\] The MSE value is __________.

A) 8.57 B) 8.82 C) 10.00 D) 75.00 E) 20.00 A) 8.57 B) 8.82 C) 10.00 D) 75.00 E) 20.00

A multiple regression analysis produced the following tables. \[\begin{array} { | c | c | c | c | c | } \hline \text { Predictor } & \text { Coefficients } & \text { Stardard Eror } & \boldsymbol { t } \text { Statistic } & p \text {-value } \\ \hline \text { Irtercept } & 616.6849 & 154.5534 & 3.990108 & 0.000947 \\ \hline \boldsymbol { x } _ { 1 } & - 3.33833 & \mathbf { 2 . 3 3 3 5 4 8 } & - 1.43058 & \mathbf { 0 . 1 7 0 6 7 5 } \\ \hline \mathbf { x } _ { 2 } & 1.780075 & \mathbf { 0 . 3 3 5 6 0 5 } & 5.30407 & 5.83 \mathrm { E } - 05 \\ \hline \end{array}\] \[\begin{array} { | c | c | c | c | c | c | } \hline \text { Source } & \mathrm { df } & \text { SS } & \text { MS } & F & p \text {-value } \\ \hline \text { Repression } & 2 & 121783 & 60891.48 & 14.76117 & 0.000286 \\ \hline \text { Residual } & 15 & 61876.68 & 4125.112 & & \\ \hline \text { Total } & 17 & 183659.6 & & & \\ \hline \end{array}\] Using $\alpha$ = 0.01 to test the null hypothesis H0: $\beta$ 1 = $\beta$ 2 = 0, the critical F value is ____.

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

The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables. \[\begin{array} { | c | c | c | c | c | c | } \hline \text { Source } & \mathrm { df } & \mathrm { SS } & \mathrm { MS } & F & p \\ \hline \text { Repression } & & 700 & & & \\ \hline \text { Error } & & & & & \\ \hline \text { Total } & & 1000 & & & \\ \hline \end{array}\] The adjusted R2 value is __________.

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

The following ANOVA table is from a multiple regression analysis. \[\begin{array} { | c | c | c | c | c | c | } \hline \text { Source } & \mathrm { df } & \mathrm { SS } & \mathrm { MS } & F & p \\ \hline \text { Repression } & 3 & 1728 & & & \\ \hline \text { Error } & 25 & & & & \\ \hline \text { Total } & & 2571 & & & \\ \hline \end{array}\] The R2 value is __________.

A) 0.65 B) 0.67 C) 0.69 D) 0.71 E) 0.73 A) 0.65 B) 0.67 C) 0.69 D) 0.71 E) 0.73

Exam 13: Multiple Regression Analysis

A multiple regression analysis produced the following tables. Predictor Coefficients Stardard Error Statistic p -value Irtercept -139.609 2548.989 -0.05477 0.957154 24.24619 22.25267 1.089586 32.10171 17.44559 1.840105 0.08869 Source SS MS F p -value Repression 2 302689 151344.5 1.705942 0.219838 Residual 13 1153309 88716.07 Total 15 1455998 The regression equation for this analysis is ____________.

(Multiple Choice)

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Question 41

Minitab and Excel output for a multiple regression model show the F test for the overall model, but do not provide the t tests for the regression coefficients.

(True/False)

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Question 42

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).

(True/False)

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Question 43

A market analyst is developing a regression model to predict monthly household expenditures on groceries as a function of family size, household income, and household neighborhood (urban, suburban, and rural).The "income" variable in this model is ____.

(Multiple Choice)

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Question 44

The value of adjusted R² always goes up when a nontrivial explanatory variable is added to a regression model.

(True/False)

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Question 45

A multiple regression analysis produced the following tables. Predictor Coefficients Stardard Error t Statistic p -value Irtercept 624.5369 78.49712 7.956176 6.88E-06 8.569122 1.652255 5.186319 4.736515 0.699194 6.774248 Source SS MS F p -value Repression 2 1660914 58.31956 1.4-06 Residual 11 156637.5 14239.77 Total 13 1817552 The coefficient of multiple determination is ____________.

(Multiple Choice)

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Question 46

The value of R² always goes up when a nontrivial explanatory variable is added to a regression model.

(True/False)

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Question 47

A multiple regression analysis produced the following output from Excel. The overall proportion of variation of y accounted by x₁ and x₂ is _______

(Multiple Choice)

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Question 48

A multiple regression analysis produced the following output from Minitab. Regression Analysis: Y versus x₁ and x₂ Predictor Coef SE Coef T P Constant -0.0626 0.2034 -0.31 0.762 X₁ 1.1003 0.5441 2.02 0.058 X₂ -0.8960 0.5548 -1.61 0.124 S = 0.179449 R-Sq = 89.0% R-Sq(adj)= 87.8% Analysis of Variance Source DF SS MS F P Regression 2 4.7013 2.3506 73.00 0.000 Residual Error 18 0.5796 0.0322 Total 20 5.2809 These results indicate that ____________.

(Multiple Choice)

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Question 49

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

(Multiple Choice)

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Question 50

The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables. Source F p Repression 700 Error Total 1000 The observed F value is __________.

(Multiple Choice)

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Question 51

A multiple regression analysis produced the following tables. $A multiple regression analysis produced the following tables. Using \alpha = 0.05 to test the null hypothesis H<sub>0</sub>: \beta <sub>1</sub> = 0, the correct decision is .$ Using $\alpha$ = 0.05 to test the null hypothesis H₀: $\beta$ ₁ = 0, the correct decision is .

(Multiple Choice)

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Question 52

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 "central heating" variable in this model is _______.

(Multiple Choice)

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Question 53

The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables. Source F p Repression 700 Error Total 1000 The MSE value is __________.

(Multiple Choice)

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Question 54

A multiple regression analysis produced the following tables. Predictor Coefficients Stardard Eror Statistic p -value Irtercept 616.6849 154.5534 3.990108 0.000947 -3.33833 -1.43058 1.780075 5.30407 5.83-05 Source SS MS F p -value Repression 2 121783 60891.48 14.76117 0.000286 Residual 15 61876.68 4125.112 Total 17 183659.6 Using $\alpha$ = 0.01 to test the null hypothesis H₀: $\beta$ ₁ = $\beta$ ₂ = 0, the critical F value is ____.

(Multiple Choice)

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Question 55

The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables. Source F p Repression 700 Error Total 1000 The adjusted R² value is __________.

(Multiple Choice)

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Question 56

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).

(True/False)

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Question 57

The following ANOVA table is from a multiple regression analysis. Source F p Repression 3 1728 Error 25 Total 2571 The R²value is __________.

(Multiple Choice)

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Question 58

A human resources analyst is developing a regression model to predict electricity plant manager compensation as a function of production capacity of the plant, number of employees at the plant, and plant technology (coal, oil, and nuclear).The response variable in this model is ______.

(Multiple Choice)

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Question 59

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

(Multiple Choice)

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Question 60

Minitab and Excel output for a multiple regression model show the F test for the overall model, but do not provide the t tests for the regression coefficients.

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 market analyst is developing a regression model to predict monthly household expenditures on groceries as a function of family size, household income, and household neighborhood (urban, suburban, and rural).The "income" variable in this model is ____.

The value of adjusted R² always goes up when a nontrivial explanatory variable is added to a regression model.

The value of R² always goes up when a nontrivial explanatory variable is added to a regression model.

A multiple regression analysis produced the following output from Excel. The overall proportion of variation of y accounted by x₁ and x₂ 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. Source F p Repression 700 Error Total 1000 The observed F value 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 "central heating" variable in this model is _______.

The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables. Source F p Repression 700 Error Total 1000 The MSE value is __________.

The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables. Source F p Repression 700 Error Total 1000 The adjusted R² value is __________.

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. Source F p Repression 3 1728 Error 25 Total 2571 The R²value is __________.

A human resources analyst is developing a regression model to predict electricity plant manager compensation as a function of production capacity of the plant, number of employees at the plant, and plant technology (coal, oil, and nuclear).The response variable in this model is ______.

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

Introduction to Statistics and Business Analytics

Visualizing Data With 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

Minitab and Excel output for a multiple regression model show the F test for the overall model, but do not provide the t tests for the regression coefficients.

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 market analyst is developing a regression model to predict monthly household expenditures on groceries as a function of family size, household income, and household neighborhood (urban, suburban, and rural).The "income" variable in this model is ____.

The value of adjusted R2 always goes up when a nontrivial explanatory variable is added to a regression model.

The value of R2 always goes up when a nontrivial explanatory variable is added to a regression model.

A multiple regression analysis produced the following output from Excel. The overall proportion of variation of y accounted by x1 and x2 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. Source F p Repression 700 Error Total 1000 The observed F value is __________.

A multiple regression analysis produced the following tables. Using α\alphaα = 0.05 to test the null hypothesis H0: β\betaβ 1 = 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 "central heating" variable in this model is _______.

The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables. Source F p Repression 700 Error Total 1000 The MSE value is __________.

The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables. Source F p Repression 700 Error Total 1000 The adjusted R2 value is __________.

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. Source F p Repression 3 1728 Error 25 Total 2571 The R2 value is __________.

A human resources analyst is developing a regression model to predict electricity plant manager compensation as a function of production capacity of the plant, number of employees at the plant, and plant technology (coal, oil, and nuclear).The response variable in this model is ______.

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

Introduction to Statistics and Business Analytics

Visualizing Data With 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 value of adjusted R² always goes up when a nontrivial explanatory variable is added to a regression model.

The value of R² always goes up when a nontrivial explanatory variable is added to a regression model.

A multiple regression analysis produced the following output from Excel. The overall proportion of variation of y accounted by x₁ and x₂ is _______

The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables. Source F p Repression 700 Error Total 1000 The adjusted R² value is __________.

The following ANOVA table is from a multiple regression analysis. Source F p Repression 3 1728 Error 25 Total 2571 The R²value is __________.