Question 1

If the probability plot of residuals resembles a straight line,the residuals show a fairly good fit to the normal distribution.

Accepted Answer

A) True 
 B)False  True

Question 2

Multicollinearity refers to relationships among the independent variables.

Accepted Answer

A) True 
 B)False  True

Question 3

R2adj can exceed R2 if there are several weak predictors.

Accepted Answer

A) True 
 B)False  False

Question 4

Plotting the residuals against a binary predictor (X = 0,1)reveals nothing about heteroscedasticity.

Accepted Answer

A) True 
 B)False

Question 5

In the fitted regression Y = 12 + 3X1 − 5X2 + 27X3 + 2X4 the most significant predictor is X3.

Accepted Answer

A) True 
 B)False

Question 6

A predictor whose pairwise correlation with Y is near zero can still have a significant t-value in a multiple regression when other predictors are included.

Accepted Answer

A) True 
 B)False

Question 7

When autocorrelation is present,the estimates of the coefficients will be unbiased.

Accepted Answer

A) True 
 B)False

Question 8

To find which predictors are most helpful in increasing R2,we might consider

Accepted Answer

A) Log transformations. 
B) Stepwise regression. 
C) Logistic regression. 
D) Nonlinear regression. 
A) Log transformations. 
B) Stepwise regression. 
C) Logistic regression. 
D) Nonlinear regression.

Question 9

Which statement about leverage is incorrect?

Accepted Answer

A) Leverage refers to an observation's distance from the mean of X. 
B) If n = 40 and k = 4 predictors,a leverage statistic of .15 would indicate high leverage. 
C) If n = 180 and k = 3 predictors,a leverage statistic of .08 would indicate high leverage. 
A) Leverage refers to an observation's distance from the mean of X. 
B) If n = 40 and k = 4 predictors,a leverage statistic of .15 would indicate high leverage. 
C) If n = 180 and k = 3 predictors,a leverage statistic of .08 would indicate high leverage.

Question 10

For a given set of values for x1,x2,... ,xk the confidence interval for the conditional mean of Y is

Accepted Answer

A) narrower than the prediction interval for the individual Y value. 
B) wider than the prediction interval for the individual Y value. 
C) usually about the same as the prediction interval for the individual Y value. 
A) narrower than the prediction interval for the individual Y value. 
B) wider than the prediction interval for the individual Y value. 
C) usually about the same as the prediction interval for the individual Y value.

Question 11

In a regression with n = 50 observations and k = 3 predictors,the criterion for high leverage is

Accepted Answer

A) hi > .33 
B) hi > .25 
C) hi > .21 
D) hi > .16 
A) hi > .33 
B) hi > .25 
C) hi > .21 
D) hi > .16

Question 12

A realtor is trying to predict the selling price of houses in Greenville (in thousands of dollars)as a function of Size (measured in thousands of square feet)and whether or not there is a fireplace (FP is 0 if there is no fireplace,1 if there is a fireplace).Part of the regression output is provided below,based on a sample of 20 homes.Some of the information has been omitted.   The estimated coefficient for Size is approximately

Accepted Answer

A) 9.5 
B) 13.8 
C) 122.5 
D) 1442.6 
A) 9.5 
B) 13.8 
C) 122.5 
D) 1442.6

Question 13

The F statistic and its p-value give a global test of significance for a multiple regression.

Accepted Answer

A) True 
 B)False

Question 14

Autocorrelation may be detected by looking at a plot of the residuals against time.

Accepted Answer

A) True 
 B)False

Question 15

Nonnormality of residuals is not usually considered a major problem unless there are outliers.

Accepted Answer

A) True 
 B)False

Question 16

If the standard error is 12,the width of a quick prediction interval for Y is

Accepted Answer

A) ±15 
B) ±24 
C) ±19 
D) impossible to determine without an F table. 
A) ±15 
B) ±24 
C) ±19 
D) impossible to determine without an F table.

Question 17

The regression equation Salary = 28,000 + 2700 YearsExperience + 1900 YearsCollege describes employee salaries at Ramjac Corporation.The standard error is 2400.Mary has 10 years' experience and 4 years of college.Her salary is $58,350.What is Mary's standardized residual (approximately)?

Accepted Answer

A) −1.150 
B) +2.007 
C) −1.771 
D) +1.400 
A) −1.150 
B) +2.007 
C) −1.771 
D) +1.400

Question 18

Does the picture below show strong evidence of heteroscedasticity against the predictor Wheelbase?

Accepted Answer

A) Yes,there is strong evidence of heteroscedasticity. 
B) No,the plot appears mostly random against Wheelbase. 
C) We need a probability plot to answer. 
D) We need VIF statistics to answer. 
A) Yes,there is strong evidence of heteroscedasticity. 
B) No,the plot appears mostly random against Wheelbase. 
C) We need a probability plot to answer. 
D) We need VIF statistics to answer.

Question 19

Analyze the regression below (n = 50 U.S.states)using the concepts you have learned about multiple regression.Circle things of interest and write comments in the margin.Make a prediction for Poverty for a state with Dropout = 15,TeenMom = 12,Unem = 4,and Age65% = 12 (show your work).The variables are Poverty = percentage below the poverty level;Dropout = percentage of adult population that did not finish high school;TeenMom = percentage of total births by teenage mothers;Unem = unemployment rate,civilian labor force;and Age65% = percentage of population aged 65 and over.

Accepted Answer

The regression is significant overall (F

Question 20

A logistic regression is appropriate when

Accepted Answer

A) the dependent variable is binary (0,1). 
B) an independent variable is binary (0,1). 
C) the dependent variable is log(Y). 
D) the independent variables have the form log(X). 
A) the dependent variable is binary (0,1). 
B) an independent variable is binary (0,1). 
C) the dependent variable is log(Y). 
D) the independent variables have the form log(X).

If the probability plot of residuals resembles a straight line,the residuals show a fairly good fit to the normal distribution.

Multicollinearity refers to relationships among the independent variables.

R2adj can exceed R2 if there are several weak predictors.

Plotting the residuals against a binary predictor (X = 0,1)reveals nothing about heteroscedasticity.

In the fitted regression Y = 12 + 3X1 − 5X2 + 27X3 + 2X4 the most significant predictor is X3.

A predictor whose pairwise correlation with Y is near zero can still have a significant t-value in a multiple regression when other predictors are included.

When autocorrelation is present,the estimates of the coefficients will be unbiased.

To find which predictors are most helpful in increasing R2,we might consider

Which statement about leverage is incorrect?

For a given set of values for x1,x2,... ,xk the confidence interval for the conditional mean of Y is

In a regression with n = 50 observations and k = 3 predictors,the criterion for high leverage is

The F statistic and its p-value give a global test of significance for a multiple regression.

Autocorrelation may be detected by looking at a plot of the residuals against time.

Nonnormality of residuals is not usually considered a major problem unless there are outliers.

If the standard error is 12,the width of a quick prediction interval for Y is

The regression equation Salary = 28,000 + 2700 YearsExperience + 1900 YearsCollege describes employee salaries at Ramjac Corporation.The standard error is 2400.Mary has 10 years' experience and 4 years of college.Her salary is $58,350.What is Mary's standardized residual (approximately)?

Does the picture below show strong evidence of heteroscedasticity against the predictor Wheelbase?

A logistic regression is appropriate when

Overview of Statistics

Data Collection

Describing Data Visually

Descriptive Statistics

Probability

Discrete Probability Distributions

Continuous Probability Distributions

Sampling Distributions and Estimation

One-Sample Hypothesis Tests

Two-Sample Hypothesis Tests

Analysis of Variance

Simple Regression

Chi-Square Tests

Filters

Exam 13: Multiple Regression

If the probability plot of residuals resembles a straight line,the residuals show a fairly good fit to the normal distribution.

Multicollinearity refers to relationships among the independent variables.

R2adj can exceed R2 if there are several weak predictors.

Plotting the residuals against a binary predictor (X = 0,1)reveals nothing about heteroscedasticity.

In the fitted regression Y = 12 + 3X1 − 5X2 + 27X3 + 2X4 the most significant predictor is X3.

A predictor whose pairwise correlation with Y is near zero can still have a significant t-value in a multiple regression when other predictors are included.

When autocorrelation is present,the estimates of the coefficients will be unbiased.

To find which predictors are most helpful in increasing R2,we might consider

Which statement about leverage is incorrect?

For a given set of values for x1,x2,... ,xk the confidence interval for the conditional mean of Y is

In a regression with n = 50 observations and k = 3 predictors,the criterion for high leverage is

The F statistic and its p-value give a global test of significance for a multiple regression.

Autocorrelation may be detected by looking at a plot of the residuals against time.

Nonnormality of residuals is not usually considered a major problem unless there are outliers.

If the standard error is 12,the width of a quick prediction interval for Y is

The regression equation Salary = 28,000 + 2700 YearsExperience + 1900 YearsCollege describes employee salaries at Ramjac Corporation.The standard error is 2400.Mary has 10 years' experience and 4 years of college.Her salary is $58,350.What is Mary's standardized residual (approximately)?

Does the picture below show strong evidence of heteroscedasticity against the predictor Wheelbase?

A logistic regression is appropriate when

Overview of Statistics

Data Collection

Describing Data Visually

Descriptive Statistics

Probability

Discrete Probability Distributions

Continuous Probability Distributions

Sampling Distributions and Estimation

One-Sample Hypothesis Tests

Two-Sample Hypothesis Tests

Analysis of Variance

Simple Regression

Chi-Square Tests

Filters