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 statistics should one look at to determine which of the independent variables are most significant in the regression?

A) r2 and adj r2 B) sum of squares and F-test C) F-test and p D) t-test and p A) r2 and adj r2 B) sum of squares and F-test C) F-test and p D) t-test and p

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 $\}$ $\begin{array}{|c|c|c|c|c|c|} \hline \text { Coefficients } & \boldsymbol{b} & \begin{array}{c} \text { Std. } \\ \text { Error } \end{array} & \begin{array}{c} \text { Std. } \\ \text { Beta } \end{array} & \begin{array}{c} \boldsymbol{t} \text {-test } \\ \text { Statistic } \end{array} & \begin{array}{c} \boldsymbol{p} \text {-value } \\ \text { Two Tailed } \end{array} \\ \hline \text { Intercept } & -210.603 & 20.560 & & -10.243 & 0.0000 \\ \hline \text { Age } & 0.660 & 0.279 & 0.101 & 2.363 & 0.0186 \\ \hline \text { Gender } & 17.449 & 2.450 & 0.267 & 7.122 & 0.0000 \\ \hline \text { Height } & 4.999 & 0.294 & 0.613 & 16.982 & 0.0000 \\ \hline \text { MBA } & -3.122 & 3.063 & -0.043 & -1.019 & 0.3087 \\ \hline \text { Year } & -0.111 & 0.507 & -0.006 & -0.218 & 0.8274 \\ \hline \end{array}$ $\begin{array}{|c|c|c|c|c|} \hline \boldsymbol{r} & \boldsymbol{r}^{2} & \text { Adj. } \boldsymbol{r}^{2} & \text { SE(Reg) } & \boldsymbol{n} \\ \hline 0.834 & 0.696 & 0.693 & 17.879 & 448 \\ \hline \end{array}$ $\begin{array}{|c|c|c|c|c|c|} \hline \begin{array}{c} \text { Source of } \\ \text { Variation } \end{array} & \begin{array}{c} \text { Sum of } \\ \text { Squares } \end{array} & \text { df } & \begin{array}{c} \text { Mean } \\ \text { Squares } \end{array} & \begin{array}{c} F \text {-test } \\ \text { Statistic } \end{array} & \begin{array}{c} \boldsymbol{p} \text {-value } \\ \text { One Tailed } \end{array} \\ \hline \text { Regression } & 323592.24 & 5 & 64718.4 & 202.471 & 0.0000 \\ \hline \text { Error } & 141282.23 & 442 & 319.643 & & \\ \hline \text { Total } & 464874.47 & 447 & & & \\ \hline \end{array}$ -According to the regression output above,which of the following statements most accurately summarizes what can be said,for the entire population,about the weight of individuals with an MBA (MBA=1)versus the weight of individuals without an MBA (MBA=0)?

A) They weigh on average 3.122 pounds less than individuals who do not have an MBA. B) They weigh on average 3.122 pounds more than individuals who do not have an MBA. C) Their weights are roughly the same. D) The variable is not a significant predictor, so, even though its coefficient estimate is -3.222, we cannot assume it has an impact on weight. A) They weigh on average 3.122 pounds less than individuals who do not have an MBA. B) They weigh on average 3.122 pounds more than individuals who do not have an MBA. C) Their weights are roughly the same. D) The variable is not a significant predictor, so, even though its coefficient estimate is -3.222, we cannot assume it has an impact on weight.

Exam 9: Multiple Regression: Modeling Multivariate Relationships

The t-test is the first thing that should be checked in a regression output.If it is not significant,then the entire model is not providing sufficient explanatory power.

(True/False)

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Question 61

Identifying nonlinear relationships between variables is accomplished by transforming one or more variables and then estimating the fit with

(Multiple Choice)

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Question 62

In binary regression,it would be inappropriate to put a line through the observations in a data plot,because the values of the independent variables can only be 0 or 1.

(True/False)

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Question 63

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

(Multiple Choice)

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Question 64

All regression models,including simple linear,binary,ordinal,multinomial logit,rank-ordered,and count,can be viewed as special cases of the general formulation called the General Linear Model.

(True/False)

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Question 65

Discuss its limitations of regression.

(Essay)

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Question 66

The best way to see if heteroscedasticity is present is to

(Multiple Choice)

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Question 67

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 -For each additional inch of Height,how much additional Weight would be added?

(Essay)

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Question 68

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 statistics should one look at to determine which of the independent variables are most significant in the regression?

(Multiple Choice)

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Question 69

All of the following statements about regression are true except:

(Multiple Choice)

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Question 70

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 -According to the regression output above,which of the following statements most accurately summarizes what can be said,for the entire population,about the weight of individuals with an MBA (MBA=1)versus the weight of individuals without an MBA (MBA=0)?

(Multiple Choice)

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Question 71

Polynomial regression is a robust method that should be among the first transformations to try on nonlinear data.

(True/False)

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Question 72

Violating the underlying assumptions of regression can lead to all of the following problems except

(Multiple Choice)

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Question 73

Which list of common variables below would be examples of ordinal data?

(Multiple Choice)

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Question 74

The t-test is the first thing that should be checked in a regression output.If it is not significant,then the entire model is not providing sufficient explanatory power.

Identifying nonlinear relationships between variables is accomplished by transforming one or more variables and then estimating the fit with

In binary regression,it would be inappropriate to put a line through the observations in a data plot,because the values of the independent variables can only be 0 or 1.

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

All regression models,including simple linear,binary,ordinal,multinomial logit,rank-ordered,and count,can be viewed as special cases of the general formulation called the General Linear Model.

Discuss its limitations of regression.

The best way to see if heteroscedasticity is present is to

All of the following statements about regression are true except:

Polynomial regression is a robust method that should be among the first transformations to try on nonlinear data.

Violating the underlying assumptions of regression can lead to all of the following problems except

Which list of common variables below would be examples of ordinal data?

The Purpose and Process of Marketing Research

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Measurement in Marketing Research

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Data Collection: Exploratory and Conclusive Research

Designing Surveys and Data Collection Instruments

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Data Analysis and Statistical Methods: Univariate and Bivariate Analyses

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