Use the regression output below to answer the following questions. Lingar Reperaidion Anthyis: Dep Var $( X ) =$ Weight $X = \{ A$ Ee, Gender, Heifht, MBA, $Y$ ear $\}$ $\begin{array}{|c|c|c|c|c|c|} \hline \text { Coefficients } & \boldsymbol{b} & \begin{array}{c} \text { Std. } \\ \text { Error } \end{array} & \begin{array}{c} \text { Std. } \\ \text { Beta } \end{array} & \begin{array}{c} \boldsymbol{t} \text {-test } \\ \text { Statistic } \end{array} & \begin{array}{c} \boldsymbol{p} \text {-value } \\ \text { Two Tailed } \end{array} \\ \hline \text { Intercept } & -210.603 & 20.560 & & -10.243 & 0.0000 \\ \hline \text { Age } & 0.660 & 0.279 & 0.101 & 2.363 & 0.0186 \\ \hline \text { Gender } & 17.449 & 2.450 & 0.267 & 7.122 & 0.0000 \\ \hline \text { Height } & 4.999 & 0.294 & 0.613 & 16.982 & 0.0000 \\ \hline \text { MBA } & -3.122 & 3.063 & -0.043 & -1.019 & 0.3087 \\ \hline \text { Year } & -0.111 & 0.507 & -0.006 & -0.218 & 0.8274 \\ \hline \end{array}$ $\begin{array}{|c|c|c|c|c|} \hline \boldsymbol{r} & \boldsymbol{r}^{2} & \text { Adj. } \boldsymbol{r}^{2} & \text { SE(Reg) } & \boldsymbol{n} \\ \hline 0.834 & 0.696 & 0.693 & 17.879 & 448 \\ \hline \end{array}$ $\begin{array}{|c|c|c|c|c|c|} \hline \begin{array}{c} \text { Source of } \\ \text { Variation } \end{array} & \begin{array}{c} \text { Sum of } \\ \text { Squares } \end{array} & \text { df } & \begin{array}{c} \text { Mean } \\ \text { Squares } \end{array} & \begin{array}{c} F \text {-test } \\ \text { Statistic } \end{array} & \begin{array}{c} \boldsymbol{p} \text {-value } \\ \text { One Tailed } \end{array} \\ \hline \text { Regression } & 323592.24 & 5 & 64718.4 & 202.471 & 0.0000 \\ \hline \text { Error } & 141282.23 & 442 & 319.643 & & \\ \hline \text { Total } & 464874.47 & 447 & & & \\ \hline \end{array}$ -Which variable(s)is/are (a)significant,strong predictor(s)of Weight in the regression above?

A) Age and Height B) Gender only C) Height only D) Age, Height, Gender, MBA and Year E) Height and Gender A) Age and Height B) Gender only C) Height only D) Age, Height, Gender, MBA and Year E) Height and Gender

Use the regression output below to answer the following questions. Lingar Reperaidion Anthyis: Dep Var $( X ) =$ Weight $X = \{ A$ Ee, Gender, Heifht, MBA, $Y$ ear $\}$ $\begin{array}{||c|c|c|c|c|c|} \hline \text { Coefficients } & \boldsymbol{b} & \begin{array}{c} \text { Std. } \\ \text { Error } \end{array} & \begin{array}{c} \text { Std. } \\ \text { Beta } \end{array} & \begin{array}{c} \boldsymbol{t} \text {-test } \\ \text { Statistic } \end{array} & \begin{array}{c} \boldsymbol{p} \text {-value } \\ \text { Two Tailed } \end{array} \\ \hline \text { Intercept } & -210.603 & 20.560 & & -10.243 & 0.0000 \\ \hline \text { Age } & 0.660 & 0.279 & 0.101 & 2.363 & 0.0186 \\ \hline \text { Gender } & 17.449 & 2.450 & 0.267 & 7.122 & 0.0000 \\ \hline \text { Height } & 4.999 & 0.294 & 0.613 & 16.982 & 0.0000 \\ \hline \text { MBA } & -3.122 & 3.063 & -0.043 & -1.019 & 0.3087 \\ \hline \text { Year } & -0.111 & 0.507 & -0.006 & -0.218 & 0.8274 \\ \hline \end{array}$ $\begin{array}{|c|c|c|c|c|} \hline \boldsymbol{r} & \boldsymbol{r}^{2} & \text { Adj. } \boldsymbol{r}^{2} & \mathrm{SE}(\mathrm{Reg}) & \boldsymbol{h} \\ \hline 0.834 & 0.696 & 0.693 & 17.879 & 448 \\ \hline \hline \end{array}$ $\begin{array}{|c|c|c|c|c|c|} \hline \begin{array}{c} \text { Source of } \\ \text { Variation } \end{array} & \begin{array}{c} \text { Sum of } \\ \text { Squares } \end{array} & \boldsymbol{d f} & \begin{array}{c} \text { Mean } \\ \text { Squares } \end{array} & \begin{array}{c} \text { F-test } \\ \text { Statistic } \end{array} & \begin{array}{c} \boldsymbol{p} \text {-value } \\ \text { One Tailed } \end{array} \\ \hline \text { Regression } & 323592.24 & 5 & 64718.4 & 202.471 & 0.0000 \\ \hline \text { Error } & 141282.23 & 442 & 319.643 & & \\ \hline \text { Total } & 464874.47 & 447 & & & \\ \hline \end{array}$ -Write the regression equation for the regression output above.

Weight = -210.603 + 0.660(Age)+ 17.449 (

Exam 9: Multiple Regression: Modeling Multivariate Relationships

Use the regression output below to answer the following questions. Lingar Reperaidion Anthyis: Dep Var $( X ) =$ Weight $X = \{ A$ Ee, Gender, Heifht, MBA, $Y$ ear $\}$ Coefficients Std. Error Std. Beta -test Statistic -value Two Tailed Intercept -210.603 20.560 -10.243 0.0000 Age 0.660 0.279 0.101 2.363 0.0186 Gender 17.449 2.450 0.267 7.122 0.0000 Height 4.999 0.294 0.613 16.982 0.0000 MBA -3.122 3.063 -0.043 -1.019 0.3087 Year -0.111 0.507 -0.006 -0.218 0.8274 Adj. () 0.834 0.696 0.693 17.879 448 Source of Variation Sum of Squares Mean Squares F-test Statistic -value One Tailed Regression 323592.24 5 64718.4 202.471 0.0000 Error 141282.23 442 319.643 Total 464874.47 447 -Based on the regression printout,what would be the predicted (mean)weight of a female,20 years old,68 inches tall,without an MBA,and in Year 1.

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Question 21

To determine if the regression coefficients (b's)in a regression model are significantly different from zero,the _______________ is used.

(Multiple Choice)

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Question 22

Each independent coefficient ( $\beta$ )in a regression model will show the impact on the dependent variable of a one-unit change in the corresponding independent variable,if all of the other variables are held constant.

(True/False)

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Question 23

One way to correct for heteroscedasticity is to transform either the independent variables or the dependent variable using logarithms.

(True/False)

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Question 24

Describe how transformations can be used to apply linear regression to nonlinear relationships.

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Question 25

-What does the Constant signify in this regression?

(Multiple Choice)

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Question 26

The model used most often in marketing that includes item-specific information is referred to as the

(Multiple Choice)

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Question 27

If the dependent variable is count data,the regression model that will be used is the

(Multiple Choice)

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Question 28

In regression analysis,the F-test tells you if all of the variables taken together help explain the variation in the dependent variable.

(True/False)

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Question 29

Use the regression output below to answer the following questions. Lingar Reperaidion Anthyis: Dep Var $( X ) =$ Weight $X = \{ A$ Ee, Gender, Heifht, MBA, $Y$ ear $\}$ Coefficients Std. Error Std. Beta -test Statistic -value Two Tailed Intercept -210.603 20.560 -10.243 0.0000 Age 0.660 0.279 0.101 2.363 0.0186 Gender 17.449 2.450 0.267 7.122 0.0000 Height 4.999 0.294 0.613 16.982 0.0000 MBA -3.122 3.063 -0.043 -1.019 0.3087 Year -0.111 0.507 -0.006 -0.218 0.8274 Adj. SE(Reg) 0.834 0.696 0.693 17.879 448 Source of Variation Sum of Squares df Mean Squares F -test Statistic -value One Tailed Regression 323592.24 5 64718.4 202.471 0.0000 Error 141282.23 442 319.643 Total 464874.47 447 -Which variable(s)is/are (a)significant,strong predictor(s)of Weight in the regression above?

(Multiple Choice)

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Question 30

The value of r²will always increase when adding additional variables to a multiple linear regression equation.

(True/False)

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Question 31

Standardized residuals are useful in seeing whether there are any strong outliers.

(True/False)

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Question 32

_______________ takes both binary and interval data and produces probabilities of an outcome,such as purchase probabilities and market share projections,as functions of the marketing mix.

(Multiple Choice)

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Question 33

-Based on the binary regression output above,which of the independent variable(s)are significant predictors of Gender?

(Multiple Choice)

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Question 34

Use the regression output below to answer the following questions. Lingar Reperaidion Anthyis: Dep Var $( X ) =$ Weight $X = \{ A$ Ee, Gender, Heifht, MBA, $Y$ ear $\}$ Coefficients Std. Error Std. Beta -test Statistic -value Two Tailed Intercept -210.603 20.560 -10.243 0.0000 Age 0.660 0.279 0.101 2.363 0.0186 Gender 17.449 2.450 0.267 7.122 0.0000 Height 4.999 0.294 0.613 16.982 0.0000 MBA -3.122 3.063 -0.043 -1.019 0.3087 Year -0.111 0.507 -0.006 -0.218 0.8274 Adj. () 0.834 0.696 0.693 17.879 448 Source of Variation Sum of Squares Mean Squares F-test Statistic -value One Tailed Regression 323592.24 5 64718.4 202.471 0.0000 Error 141282.23 442 319.643 Total 464874.47 447 -Write the regression equation for the regression output above.

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Question 35

Autocorrelation occurs when the error does not have a constant variance.

(True/False)

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Question 36

If the dependent variable is a nominal scale,then the proper regression model to use is

(Multiple Choice)

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Question 37

Nonlinear relationships can be examine with ordinary linear regression through transformation functions such as logs,exponents,squares,square roots,and polynomials.

(True/False)

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Question 38

For linear regression,the assumption is made that the error term is

(Multiple Choice)

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Question 39

To determine if the fit of a regression equation is larger than the error,the _______________ is used.

(Multiple Choice)

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Question 40

To determine if the regression coefficients (b's)in a regression model are significantly different from zero,the _______________ is used.

Each independent coefficient ( $\beta$ )in a regression model will show the impact on the dependent variable of a one-unit change in the corresponding independent variable,if all of the other variables are held constant.

One way to correct for heteroscedasticity is to transform either the independent variables or the dependent variable using logarithms.

Describe how transformations can be used to apply linear regression to nonlinear relationships.

-What does the Constant signify in this regression?

The model used most often in marketing that includes item-specific information is referred to as the

If the dependent variable is count data,the regression model that will be used is the

In regression analysis,the F-test tells you if all of the variables taken together help explain the variation in the dependent variable.

The value of r²will always increase when adding additional variables to a multiple linear regression equation.

Standardized residuals are useful in seeing whether there are any strong outliers.

_______________ takes both binary and interval data and produces probabilities of an outcome,such as purchase probabilities and market share projections,as functions of the marketing mix.

-Based on the binary regression output above,which of the independent variable(s)are significant predictors of Gender?

Autocorrelation occurs when the error does not have a constant variance.

If the dependent variable is a nominal scale,then the proper regression model to use is

Nonlinear relationships can be examine with ordinary linear regression through transformation functions such as logs,exponents,squares,square roots,and polynomials.

For linear regression,the assumption is made that the error term is

To determine if the fit of a regression equation is larger than the error,the _______________ is used.

The Purpose and Process of Marketing Research

Research Design and Data Sources

Measurement in Marketing Research

Causal Designs and Marketing Experiments

Data Collection: Exploratory and Conclusive Research

Designing Surveys and Data Collection Instruments

Sampling

Data Analysis and Statistical Methods: Univariate and Bivariate Analyses

Multivariate Methods of Marketing Research I: Factor, cluster, and Discriminant Analyses

Multivariate Methods of Marketing Reseach Ii: Conjoint Analysis and Multidimensional Scaling

Advanced Topics, research Frontiers, and Preparing the Final Report

Filters

Exam 9: Multiple Regression: Modeling Multivariate Relationships

To determine if the regression coefficients (b's)in a regression model are significantly different from zero,the _______________ is used.

Each independent coefficient ( β\betaβ )in a regression model will show the impact on the dependent variable of a one-unit change in the corresponding independent variable,if all of the other variables are held constant.

One way to correct for heteroscedasticity is to transform either the independent variables or the dependent variable using logarithms.

Describe how transformations can be used to apply linear regression to nonlinear relationships.

-What does the Constant signify in this regression?

The model used most often in marketing that includes item-specific information is referred to as the

If the dependent variable is count data,the regression model that will be used is the

In regression analysis,the F-test tells you if all of the variables taken together help explain the variation in the dependent variable.

The value of r2 will always increase when adding additional variables to a multiple linear regression equation.

Standardized residuals are useful in seeing whether there are any strong outliers.

_______________ takes both binary and interval data and produces probabilities of an outcome,such as purchase probabilities and market share projections,as functions of the marketing mix.

-Based on the binary regression output above,which of the independent variable(s)are significant predictors of Gender?

Autocorrelation occurs when the error does not have a constant variance.

If the dependent variable is a nominal scale,then the proper regression model to use is

Nonlinear relationships can be examine with ordinary linear regression through transformation functions such as logs,exponents,squares,square roots,and polynomials.

For linear regression,the assumption is made that the error term is

To determine if the fit of a regression equation is larger than the error,the _______________ is used.

The Purpose and Process of Marketing Research

Research Design and Data Sources

Measurement in Marketing Research

Causal Designs and Marketing Experiments

Data Collection: Exploratory and Conclusive Research

Designing Surveys and Data Collection Instruments

Sampling

Data Analysis and Statistical Methods: Univariate and Bivariate Analyses

Multivariate Methods of Marketing Research I: Factor, cluster, and Discriminant Analyses

Multivariate Methods of Marketing Reseach Ii: Conjoint Analysis and Multidimensional Scaling

Advanced Topics, research Frontiers, and Preparing the Final Report

Filters

Each independent coefficient ( $\beta$ )in a regression model will show the impact on the dependent variable of a one-unit change in the corresponding independent variable,if all of the other variables are held constant.

The value of r²will always increase when adding additional variables to a multiple linear regression equation.