Question 1

The multiple coefficient of determination is the _______ divided by the total variation.

Accepted Answer

A) Unexplained variation 
B) SSE 
C) Explained variation 
D) Distance value 
E) Leverage value 
A) Unexplained variation 
B) SSE 
C) Explained variation 
D) Distance value 
E) Leverage value

Question 2

In general,a multiple regression model is considered to be desirable if the value of the C statistic is small and the value of C is less than _____.

Accepted Answer

A) k 
B) k - 1 
C) k + 1 
D) n - k 
A) k 
B) k - 1 
C) k + 1 
D) n - k

Question 3

The normal plot is a residual plot that checks the normality assumption.

Accepted Answer

A) True 
 B)False

Question 4

Below is a partial multiple regression computer output.

Accepted Answer

\[\begin{array} { l l l } 
\text { Sourc

Question 5

The range of feasible values for the multiple coefficient of correlation is from ________.

Accepted Answer

A) 0 to
$\bar{w}$ 
B) -1 to 0 
C) -1 to 1 
D) 0 to 1 
E) - 
A) 0 to
$\bar{w}$ 
B) -1 to 0 
C) -1 to 1 
D) 0 to 1 
E) -

Question 6

Consider the following partial computer output for a multiple regression model.

Accepted Answer

\[\begin{array} { l l l } 
\text { Predi

Question 7

Use the following correlation matrix and determine the best multiple regression prediction equation that has no significant multicollinearity.

Accepted Answer

\[\begin{array} { l l c c c c } 
& y & x

Question 8

Consider the following analysis of variance table from a multiple regression model.Test the model for overall usefulness at α = .01 and carefully make a managerial conclusion.

Accepted Answer

\[\begin{array} { l l l l l } 
\text { A

Question 9

A member of the state legislature has expressed concern about the differences in the mathematics test scores of high school freshmen across the state.She asks her research assistant to conduct a study to investigate what factors could account for the differences.The research assistant looks at a random sample of school districts across the state and uses the factors of percentage of mathematics teachers in each district with a degree in mathematics,the average age of mathematics teachers,and the average salary of mathematics teachers.

Accepted Answer

\[\begin{array}{l}
\begin{array} { l r r

Question 10

Calculate the odds ratio for an event when its probability is 0.75.

Accepted Answer

3 to 1
Feedback:Odds

Question 11

A multiple linear regression analysis involving 45 observations resulted in the following least squares prediction equation:

Accepted Answer

@#LAT-DLM& 
The SSE for this model is 49.Adding tw

Question 12

When using a multiple regression model,we assume that error terms (residuals)are distributed according to a(n)________________ distribution.

Accepted Answer

A) Binomial 
B) Normal 
C) Exponential 
D) Poisson 
A) Binomial 
B) Normal 
C) Exponential 
D) Poisson

Question 13

Below is a partial multiple regression computer output based on a quadratic regression model to predict student enrollment at a local university.The dependent variable is the annual enrollment given in thousands of students,the independent variable X is the increase in tuition stated in thousands of dollars per year,and X2 is the square of the tuition increase given in squared thousands of dollars per year.Interpret β0 (the y-intercept)and β1 (the β coefficient for the X variable).Does the parabola open upward or downward? Why?

Accepted Answer

\[\begin{array} { l l l } 
\text { Sourc

Question 14

Significant _________ may exist when the overall F statistic is significant and the individual t statistics for all independent variables are insignificant.

Accepted Answer

A) Autocorrelation 
B) Independence 
C) Multicollinearity 
D) Outliers 
A) Autocorrelation 
B) Independence 
C) Multicollinearity 
D) Outliers

Question 15

Dummy variables take on the values of _________ and are used to model the effects of different levels of qualitative variables.

Accepted Answer

A) 1 or -1 
B) 1 or 2 
C) 0 or 2 
D) 0 or 1 
A) 1 or -1 
B) 1 or 2 
C) 0 or 2 
D) 0 or 1

Question 16

In using the multiple regression method,we can model the effects of the different levels of a qualitative independent variable by using a(n)____________.

Accepted Answer

A) Interaction variable 
B) Cross-product term 
C) Quadratic term 
D) Dummy (indicator)variable 
E) Variance equalizing transformation 
A) Interaction variable 
B) Cross-product term 
C) Quadratic term 
D) Dummy (indicator)variable 
E) Variance equalizing transformation

Question 17

It is appropriate to use an interaction variable if the relationship between the dependent variable and one of the independent variables depends on the value of the other independent variable.

Accepted Answer

A) True 
 B)False

Question 18

In the quadratic regression model $\mathrm { y } = \beta _ { 0 } + \beta _ { 1 } X _ { 1 } + \beta _ { 2 } X _ { 1 } ^ { 2 } + \varepsilon \text {, }$
The ?1 term represents the:

Accepted Answer

A) Rate of curvature of the parabolA. 
B) Value of Y when X is zero. 
C) Shift parameter of the parabola. 
D) Y-intercept of the parabola. 
A) Rate of curvature of the parabolA. 
B) Value of Y when X is zero. 
C) Shift parameter of the parabola. 
D) Y-intercept of the parabola.

Question 19

In a multiple regression mode,if the largest variance inflation factor (VIF)is 21.6,then it can be concluded that there are indications of multicollinearity.

Accepted Answer

A) True 
 B)False

Question 20

Consider the following partial computer output for a multiple regression model.

Accepted Answer

\[\begin{array} { l l l } 
\text { Predi

The multiple coefficient of determination is the _______ divided by the total variation.

In general,a multiple regression model is considered to be desirable if the value of the C statistic is small and the value of C is less than _____.

The normal plot is a residual plot that checks the normality assumption.

Below is a partial multiple regression computer output.

The range of feasible values for the multiple coefficient of correlation is from ________.

Consider the following partial computer output for a multiple regression model.

Use the following correlation matrix and determine the best multiple regression prediction equation that has no significant multicollinearity.

Consider the following analysis of variance table from a multiple regression model.Test the model for overall usefulness at α = .01 and carefully make a managerial conclusion.

Calculate the odds ratio for an event when its probability is 0.75.

A multiple linear regression analysis involving 45 observations resulted in the following least squares prediction equation:

When using a multiple regression model,we assume that error terms (residuals)are distributed according to a(n)________________ distribution.

Significant _________ may exist when the overall F statistic is significant and the individual t statistics for all independent variables are insignificant.

Dummy variables take on the values of _________ and are used to model the effects of different levels of qualitative variables.

In using the multiple regression method,we can model the effects of the different levels of a qualitative independent variable by using a(n)____________.

It is appropriate to use an interaction variable if the relationship between the dependent variable and one of the independent variables depends on the value of the other independent variable.

In the quadratic regression model $\mathrm { y } = \beta _ { 0 } + \beta _ { 1 } X _ { 1 } + \beta _ { 2 } X _ { 1 } ^ { 2 } + \varepsilon \text {, }$ The ?₁ term represents the:

In a multiple regression mode,if the largest variance inflation factor (VIF)is 21.6,then it can be concluded that there are indications of multicollinearity.

Consider the following partial computer output for a multiple regression model.

Statistical Inferences for Population Variances

Experimental Design and Analysis of Variance

Chi-Square Tests

Simple Linear Regression Analysis

Time Series Forecasting and Index Numbers

Process Improvement Using Control Charts

Nonparametric Methods

Decision Theory

Filters

Exam 15: Multiple Regression and Model Building

The multiple coefficient of determination is the _______ divided by the total variation.

In general,a multiple regression model is considered to be desirable if the value of the C statistic is small and the value of C is less than _____.

The normal plot is a residual plot that checks the normality assumption.

Below is a partial multiple regression computer output.

The range of feasible values for the multiple coefficient of correlation is from ________.

Consider the following partial computer output for a multiple regression model.

Use the following correlation matrix and determine the best multiple regression prediction equation that has no significant multicollinearity.

Consider the following analysis of variance table from a multiple regression model.Test the model for overall usefulness at α = .01 and carefully make a managerial conclusion.

Calculate the odds ratio for an event when its probability is 0.75.

A multiple linear regression analysis involving 45 observations resulted in the following least squares prediction equation:

When using a multiple regression model,we assume that error terms (residuals)are distributed according to a(n)________________ distribution.

Significant _________ may exist when the overall F statistic is significant and the individual t statistics for all independent variables are insignificant.

Dummy variables take on the values of _________ and are used to model the effects of different levels of qualitative variables.

In using the multiple regression method,we can model the effects of the different levels of a qualitative independent variable by using a(n)____________.

It is appropriate to use an interaction variable if the relationship between the dependent variable and one of the independent variables depends on the value of the other independent variable.

In the quadratic regression model y=β0+β1X1+β2X12+ε, \mathrm { y } = \beta _ { 0 } + \beta _ { 1 } X _ { 1 } + \beta _ { 2 } X _ { 1 } ^ { 2 } + \varepsilon \text {, }y=β0​+β1​X1​+β2​X12​+ε, The ?1 term represents the:

In a multiple regression mode,if the largest variance inflation factor (VIF)is 21.6,then it can be concluded that there are indications of multicollinearity.

Consider the following partial computer output for a multiple regression model.

Statistical Inferences for Population Variances

Experimental Design and Analysis of Variance

Chi-Square Tests

Simple Linear Regression Analysis

Time Series Forecasting and Index Numbers

Process Improvement Using Control Charts

Nonparametric Methods

Decision Theory

Filters

In the quadratic regression model $\mathrm { y } = \beta _ { 0 } + \beta _ { 1 } X _ { 1 } + \beta _ { 2 } X _ { 1 } ^ { 2 } + \varepsilon \text {, }$ The ?₁ term represents the: