Question 1

_______________ occurs when the error does not have a constant variance.
A) Non-normality
B) Heteroscedasticity
C) Autocorrelation
D) Multicollinearity

Accepted Answer

Heteroscedasticity refers the error not having a constant variation.

Question 2

The best way to see if heteroscedasticity is present is to
A) plot the residuals against the dependent variables and examine at them
B) plot the residuals against each of the independent variables and examine them
C) use the Kolmogorov-Smirnov or Shapiro-Wilk test supplied in most computer statistical programs
D) apply a logarithmic transformation

Accepted Answer

By plotting the residuals against each independent variable and examining the plot,researchers can determine if heteroscedasticity is a problem.

Question 3

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

Accepted Answer

Polynomial regressions,using various powers of X,must be applied very carefully,because powers of X are highly multicollinear.It is impossible to change X by one unit without also changing X²,X³,and so forth.

Question 4

All of the following statements about regression are true except: A) Regression is a way to put a line through a group of data points. B) Regression is a method for testing the validity of relationships. C) Regression is method of verifying causal relationships. D) Regression is a flexible methodology for measuring how things influence one another.

Accepted Answer

Regression can show relationships and how variables influence each other,but it cannot fully verify causal relationships.

Question 5

For multiple linear regression,researchers want to examine the r² instead of the adjusted r² because the r² is not as easily fooled by additional independent variables.

Accepted Answer

It is the opposite; researchers want to examine the adjusted r²because it is not as easily fooled by additional independent variables.

Question 6

Due to assumptions in linear regression about the distribution and mean of the error,the only population parameter that will need to be estimated to understand the extent of error is the
A) autocorrelation of the error
B) standard deviation of the error
C) coefficient of determination
D) coefficient of variation

Accepted Answer

Because the error is assumed to be normally distributed with a mean of zero,the only error term needed in the regression equation is the error standard deviation.

Question 7

Predicting one dependent variable based on many independent variables is called
A) simple linear regression
B) bivariate regression
C) multiple regression
D) multinomial regression

Accepted Answer

Multiple regression means there are multiple independent variables in the regression.

Question 8

For linear regression,the assumption is made that the error term is
A) normally distributed with a mean of 1
B) normally distributed with a mean of 0
C) the sum of the standard deviations of all of the $\beta$ values
D) the sum-of-squares explained by the regression model

Accepted Answer

For regression analysis,researchers assume that the error term is normally distributed around a mean of 0.

Question 9

To determine if the fit of a regression equation is larger than the error,the _______________ is used.
A) F-test
B) t-test
C) chi-square
D) significance level

Accepted Answer

For overall model fit,the F-test statistic is used.

Question 10

If the error for one data point can help predict the value of the error in nearby data points,then the problem is
A) non-normality
B) heteroscedasticity
C) autocorrelation
D) multicollinearity

Accepted Answer

Autocorrelation occurs when there are patterns in the standard errors or in the location of data when plotted on a graph.

Question 11

_______________ occurs when a number of potential predictors in a regression model are highly correlated.
A) Non-normality
B) Multicollinearity
C) Heteroscedasticity
D) Autocorrelation

Accepted Answer

Multicollinearity refers to a situation where a number of predictor variables are highly correlated.

Question 12

To determine if a regression model is useful,the
A) fit (or prediction) should be bigger, on average, than "error"
B) coefficients (the b's) should be clearly different from zero
C) p value should be greater than the F-test value
D) all of the above
E) both a and b

Accepted Answer

If the fit is greater than the error and the coefficients clearly different from zero,the regression model will be useful.

Question 13

Standardized residuals are used to determine if there are any potential outliers in the data.Data points that are greater that have residuals higher than _______________ in magnitude should be examined,unless the data set is very large.
A) 1
B) 3
C) 5
D) 10

Accepted Answer

Standardized residuals are useful in seeing whether there are any strong outliers; it is reasonable to examine any data point with a standardized residual greater than 3 in magnitude,unless the data set is very large.

Question 14

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

Accepted Answer

Because each new variable will have some error,the r²will always increase.

Question 15

To determine if the regression coefficients (b's)in a regression model are significantly different from zero,the _______________ is used.
A) F-test
B) t-test
C) chi-square
D) significance level

Accepted Answer

The t-test is used to determine if individual independent variables are significant predictors.

Question 16

If the dependent variable is nominal data,then you would use Poisson regression for the regression analysis

Accepted Answer

For nominal dependent data,multinomial regression would be used.Poisson regression is used for count (non-continuous)data.

Question 17

All of the following are limitations of regression except A) regression assumptions are usually violated B) almost nothing is really linear C) latent variables may be present D) insufficient predictor variables

Accepted Answer

Another limitation of regression is that there are often too many highly correlated predictor variables.

Question 18

_______________ occurs when the error is not normally distributed.
A) Non-normality
B) Heteroscedasticity
C) Autocorrelation
D) Multicollinearity

Accepted Answer

Non-normality refers to the error term not being normally distributed.

Question 19

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.

Accepted Answer

For binary regression,the dependent variable has only two values; one therefore cannot draw a straight line through the data points.

Question 20

Violating the underlying assumptions of regression can lead to all of the following problems except A) non-normality B) standardized residuals C) heteroscedasticity D) autocorrelation

Accepted Answer

Regression can have a number of problems,such as non-normality,heteroscedasticity,and autocorrelation.

Quiz 9: Multiple Regression: Modeling Multivariate Relationships

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

The best way to see if heteroscedasticity is present is to

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

All of the following statements about regression are true except:

For multiple linear regression,researchers want to examine the r² instead of the adjusted r² because the r² is not as easily fooled by additional independent variables.

Due to assumptions in linear regression about the distribution and mean of the error,the only population parameter that will need to be estimated to understand the extent of error is the

Predicting one dependent variable based on many independent variables is called

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.

If the error for one data point can help predict the value of the error in nearby data points,then the problem is

_______________ occurs when a number of potential predictors in a regression model are highly correlated.

To determine if a regression model is useful,the

Standardized residuals are used to determine if there are any potential outliers in the data.Data points that are greater that have residuals higher than _______________ in magnitude should be examined,unless the data set is very large.

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

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

If the dependent variable is nominal data,then you would use Poisson regression for the regression analysis

All of the following are limitations of regression except

_______________ occurs when the error is not normally distributed.

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.

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

Quiz 9: Multiple Regression: Modeling Multivariate Relationships

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

The best way to see if heteroscedasticity is present is to

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

All of the following statements about regression are true except:

For multiple linear regression,researchers want to examine the r2 instead of the adjusted r2 because the r2 is not as easily fooled by additional independent variables.

Due to assumptions in linear regression about the distribution and mean of the error,the only population parameter that will need to be estimated to understand the extent of error is the

Predicting one dependent variable based on many independent variables is called

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.

If the error for one data point can help predict the value of the error in nearby data points,then the problem is

_______________ occurs when a number of potential predictors in a regression model are highly correlated.

To determine if a regression model is useful,the

Standardized residuals are used to determine if there are any potential outliers in the data.Data points that are greater that have residuals higher than _______________ in magnitude should be examined,unless the data set is very large.

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

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

If the dependent variable is nominal data,then you would use Poisson regression for the regression analysis

All of the following are limitations of regression except

_______________ occurs when the error is not normally distributed.

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.

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

For multiple linear regression,researchers want to examine the r² instead of the adjusted r² because the r² is not as easily fooled by additional independent variables.

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