The following ANOVA table is from a multiple regression analysis. \[\begin{array} { | c | c | c | c | c | c | } \hline \text { Source } & \mathrm { df } & \mathrm { SS } & \mathrm { MS } & F & p \\ \hline \text { Repression } & 3 & 1500 & & & \\ \hline \text { Error } & 26 & & & & \\ \hline \text { Total } & & 2300 & & & \\ \hline \end{array}\] The MSR value is __________.

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

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 sample size for this analysis is ____________.

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

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 value of the standard error of the estimate se is __________.

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

A multiple regression analysis produced the following tables. \[\begin{array} { | c | c | c | c | c | } \hline \text { Predictor } & \text { Coefficients } & \text { Stardard Error } & \boldsymbol { t } \text { Statistic } & p \text {-value } \\ \hline \text { Irtercept } & - 139.609 & 2548.989 & - 0.05477 & 0.957154 \\ \hline \boldsymbol { x } _ { 1 } & 24.24619 & 22.25267 & 1.089586 & \mathbf { 0 . 2 9 5 6 8 2 } \\ \hline \mathbf { x } _ { 2 } & 32.10171 & 17.44559 & 1.840105 & 0.08869 \\ \hline \end{array}\] \[\begin{array} { | c | c | c | c | c | c | } \hline \text { Source } & \mathrm { df } & \text { SS } & \text { MS } & F & p \text {-value } \\ \hline \text { Repression } & 2 & 302689 & 151344.5 & 1.705942 & 0.219838 \\ \hline \text { Residual } & 13 & 1153309 & 88716.07 & & \\ \hline \text { Total } & 15 & 1455998 & & & \\ \hline \end{array}\] Using $\alpha$= 0.01 to test the null hypothesis H0: $\beta$2 = 0, the critical t value is ____.

A) ± 1.174 B) ± 2.093 C) ± 2.131 D) ± 4.012 E) ± 3.012 A) ± 1.174 B) ± 2.093 C) ± 2.131 D) ± 4.012 E) ± 3.012

A multiple regression analysis produced the following tables. \[\begin{array} { | c | c | c | c | c | } \hline \text { Predictor } & \text { Coefficients } & \text { Stardard Error } & \boldsymbol { t } \text { Statistic } & p \text {-value } \\ \hline \text { Irtercept } & - 139.609 & 2548.989 & - 0.05477 & 0.957154 \\ \hline \boldsymbol { x } _ { 1 } & 24.24619 & 22.25267 & 1.089586 & \mathbf { 0 . 2 9 5 6 8 2 } \\ \hline \mathbf { x } _ { 2 } & 32.10171 & 17.44559 & 1.840105 & 0.08869 \\ \hline \end{array}\] \[\begin{array} { | c | c | c | c | c | c | } \hline \text { Source } & \mathrm { df } & \text { SS } & \text { MS } & F & p \text {-value } \\ \hline \text { Repression } & 2 & 302689 & 151344.5 & 1.705942 & 0.219838 \\ \hline \text { Residual } & 13 & 1153309 & 88716.07 & & \\ \hline \text { Total } & 15 & 1455998 & & & \\ \hline \end{array}\] For x1= 40 and x2 = 90, the predicted value of y is ____________.

A) 753.77 B) 1,173.00 C) 1,355.26 D) 3,719.39 E) 1,565.75 A) 753.77 B) 1,173.00 C) 1,355.26 D) 3,719.39 E) 1,565.75

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

A multiple regression analysis produced the following tables. \[\begin{array} { | c | c | c | c | c | } \hline \text { Predictor } & \text { Coefficients } & \text { Stardard Error } & \boldsymbol { t } \text { Statistic } & p \text {-value } \\ \hline \text { Irtercept } & - 139.609 & 2548.989 & - 0.05477 & 0.957154 \\ \hline \boldsymbol { x } _ { 1 } & 24.24619 & 22.25267 & 1.089586 & \mathbf { 0 . 2 9 5 6 8 2 } \\ \hline \mathbf { x } _ { 2 } & 32.10171 & 17.44559 & 1.840105 & 0.08869 \\ \hline \end{array}\] \[\begin{array} { | c | c | c | c | c | c | } \hline \text { Source } & \mathrm { df } & \text { SS } & \text { MS } & F & p \text {-value } \\ \hline \text { Repression } & 2 & 302689 & 151344.5 & 1.705942 & 0.219838 \\ \hline \text { Residual } & 13 & 1153309 & 88716.07 & & \\ \hline \text { Total } & 15 & 1455998 & & & \\ \hline \end{array}\] Using $\alpha$ = 0.01 to test the null hypothesis H0: $\beta$ 1 = $\beta$ 2 = 0, the critical F value is ____.

A) 5.99 B) 5.70 C) 1.96 D) 4.84 E) 6.70 A) 5.99 B) 5.70 C) 1.96 D) 4.84 E) 6.70

A multiple regression analysis produced the following tables. \[\begin{array} { | c | c | c | c | c | } \hline \text { Predictor } & \text { Coefficierts } & \text { Stardard Error } & \mathbf { E t a t i s t i c } & \boldsymbol { p } \text {-value } \\ \hline \text { Irntercept } & 752.0833 & \mathbf { 3 3 6 3 1 5 8 } & \mathbf { 2 . 2 3 6 2 4 1 } & \mathbf { 0 . 0 4 2 1 3 2 } \\ \hline \mathbf { x } _ { 1 } & 11.87375 & 5.32047 & \mathbf { 2 . 2 3 1 7 1 1 } & \mathbf { 0 . 0 4 2 4 9 3 } \\ \hline \mathbf { x } _ { 2 } & 1.908183 & \mathbf { 0 . 6 6 2 7 4 2 } & \mathbf { 2 . 8 7 9 2 2 6 } & \mathbf { 0 . 0 1 2 1 3 } \\ \hline \end{array}\] \[\begin{array} { | c | c | c | c | c | c | } \hline \text { Source } & \mathrm { df } & \text { SS } & \text { MS } & F & p \text {-value } \\ \hline \text { Regression } & 2 & 203693.3 & 101846.7 & 6.745406 & 0.010884 \\ \hline \text { Residual } & 12 & 181184.1 & 15098.67 & & \\ \hline \text { Total } & 14 & 384877.4 & & & \\ \hline \end{array}\] Using $\alpha$ = 0.05 to test the null hypothesis H0: $\beta$1 = $\beta$2 = 0, the critical F value is ____.

A) 3.74 B) 3.89 C) 4.75 D) 4.60 E) 2.74 A) 3.74 B) 3.89 C) 4.75 D) 4.60 E) 2.74

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 number of degrees of freedom for this regression is __________.

A) 1 B) 4 C) 34 D) 30 E) 35 A) 1 B) 4 C) 34 D) 30 E) 35

Exam 13: Multiple Regression Analysis

A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit boards as a linear function of batch size (the number of boards produced in one lot or batch), production plant (Kingsland, and Yorktown), and production shift (day, and evening).In this model, "shift" is ______.

(Multiple Choice)

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Question 61

The following ANOVA table is from a multiple regression analysis. Source F p Repression 3 1500 Error 26 Total 2300 The MSR value is __________.

(Multiple Choice)

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Question 62

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 sample size for this analysis is ____________.

(Multiple Choice)

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Question 63

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 value of the standard error of the estimate s_e is __________.

(Multiple Choice)

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Question 64

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 _______.

(Multiple Choice)

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Question 65

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

(True/False)

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Question 66

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

(True/False)

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Question 67

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 Using $\alpha$ = 0.01 to test the null hypothesis H₀: $\beta$ ₂ = 0, the critical t value is ____.

(Multiple Choice)

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Question 68

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 For x₁= 40 and x₂ = 90, the predicted value of y is ____________.

(Multiple Choice)

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Question 69

The multiple regression formulas used to estimate the regression coefficients are designed to ________________.

(Multiple Choice)

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Question 70

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 These results indicate that ____________.

(Multiple Choice)

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Question 71

A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit boards as a linear function of batch size (the number of boards produced in one lot or batch), production plant (Kingsland, and Yorktown), and production shift (day, and evening).The response variable in this model is ______.

(Multiple Choice)

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Question 72

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

(Multiple Choice)

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Question 73

The standard error of the estimate of a multiple regression model is essentially the standard deviation of the residuals for the regression model.

(True/False)

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Question 74

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 "plant technology" variable in this model is ______.

(Multiple Choice)

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Question 75

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 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 76

A multiple regression analysis produced the following tables. Predictor Coefficierts Stardard Error -value Irntercept 752.0833 11.87375 5.32047 1.908183 Source SS MS F p -value Regression 2 203693.3 101846.7 6.745406 0.010884 Residual 12 181184.1 15098.67 Total 14 384877.4 Using $\alpha$ = 0.05 to test the null hypothesis H₀: $\beta$ ₁ = $\beta$ ₂ = 0, the critical F value is ____.

(Multiple Choice)

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Question 77

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 number of degrees of freedom for this regression is __________.

(Multiple Choice)

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Question 78

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 "number of employees at the plant" variable in this model is ______.

(Multiple Choice)

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Question 79

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

(True/False)

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Question 80

The following ANOVA table is from a multiple regression analysis. Source F p Repression 3 1500 Error 26 Total 2300 The MSR 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 value of the standard error of the estimate s_e 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 _______.

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

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

The multiple regression formulas used to estimate the regression coefficients are designed to ________________.

The standard error of the estimate of a multiple regression model is essentially the standard deviation of the residuals for the regression model.

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 "plant technology" 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 number of degrees of freedom for this regression is __________.

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

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

The following ANOVA table is from a multiple regression analysis. Source F p Repression 3 1500 Error 26 Total 2300 The MSR 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 value of the standard error of the estimate se 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 _______.

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

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

The multiple regression formulas used to estimate the regression coefficients are designed to ________________.

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 ____.

The standard error of the estimate of a multiple regression model is essentially the standard deviation of the residuals for the regression model.

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 "plant technology" 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 number of degrees of freedom for this regression is __________.

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

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 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 value of the standard error of the estimate s_e is __________.

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

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 degrees of freedom in the error (df_err).