Question 1

In multiple regression analysis, multicollinearity (or simply collinearity) refers to the correlation among the independent variables.

Accepted Answer

A) True 
 B)False  True

Question 2

In residual analysis for multiple regression, if the assumptions about the error term are valid and the model is an adequate representation of the relationships between the variables, then the plot of the residuals versus the predicted y values

Accepted Answer

A) shows a horizontal band of points 
B) has a funnel shape 
C) has a curved shape 
D) is upward sloping 
E) none of the above 
A) shows a horizontal band of points 
B) has a funnel shape 
C) has a curved shape 
D) is upward sloping 
E) none of the above A

Question 3

In multiple linear regression, which of the following is NOT true about the hypothesis tests:

Accepted Answer

A) the F test measures the overall significance of the regression equation 
B) the t test measures the significance of an individual regression coefficient 
C) F = t2 just as in simple linear regression 
D) the null hypothesis for the F test is that all the slopes are equal to 0 
E) the null hypothesis for the t test is that the particular slope is equal to 0 
A) the F test measures the overall significance of the regression equation 
B) the t test measures the significance of an individual regression coefficient 
C) F = t2 just as in simple linear regression 
D) the null hypothesis for the F test is that all the slopes are equal to 0 
E) the null hypothesis for the t test is that the particular slope is equal to 0 C

Question 4

Residual analysis may make use of a plot showing the residuals or errors to evaluate the assumptions about the error term.

Accepted Answer

A) True 
 B)False

Question 5

A regression analysis involved 4 independent variables and 20 observations.In using the F table to find the critical value of F for testing the 'all s are 0' null hypothesis, the numerator degrees of freedom should be

Accepted Answer

A) 15 
B) 4 
C) 2 
D) 16 
E) none of the above 
A) 15 
B) 4 
C) 2 
D) 16 
E) none of the above

Question 6

A simple regression analysis linking dependent variable y to independent variable x is conducted with 20 observations.The F ratio used to test the  = 0 null hypothesis indicates that the slope coefficient is statistically significant at the 5% significance level.The value of tstat for the x coefficient will also show that the slope coefficient is

Accepted Answer

A) statistically significant at the 2.5% significance level. 
B) greater than F 
C) statistically significant at the 5% significance level. 
D) statistically significant at the 1% significance level. 
E) none of the above 
A) statistically significant at the 2.5% significance level. 
B) greater than F 
C) statistically significant at the 5% significance level. 
D) statistically significant at the 1% significance level. 
E) none of the above

Question 7

In regression analysis, which of the following is NOT a required assumption about the error term, $\varepsilon$?

Accepted Answer

A) the expected value of the error term is zero 
B) the error term has a normal distribution 
C) the standard deviation of the error term is constant 
D) the values of the error term are independent 
E) none of the above, all are required assumptions 
A) the expected value of the error term is zero 
B) the error term has a normal distribution 
C) the standard deviation of the error term is constant 
D) the values of the error term are independent 
E) none of the above, all are required assumptions

Question 8

Which of the following relationships in linear regression is correct?

Accepted Answer

A) SSE = SSR + SST 
B) SSR = SSE + SST 
C) SST = (SSR)2 
D) SST = SSR + SSE 
E) none of the above 
A) SSE = SSR + SST 
B) SSR = SSE + SST 
C) SST = (SSR)2 
D) SST = SSR + SSE 
E) none of the above

Question 9

Adding independent variables to a regression model will typically_______ the value of r2.

Accepted Answer

A) decrease 
B) increase 
C) not affect 
D) nullify 
E) reduce by half or more 
A) decrease 
B) increase 
C) not affect 
D) nullify 
E) reduce by half or more

Question 10

In an F distribution with numerator degrees of freedom of 4 and denominator degrees of freedom of 8, 99% of the values are less than ______ .

Accepted Answer

A) 3.838 
B) 6.041 
C) 7.006 
D) 14.799 
A) 3.838 
B) 6.041 
C) 7.006 
D) 14.799

Question 11

Which of the following is true regarding the F distribution?

Accepted Answer

A) it is the ratio of two variances from two normal populations 
B) it is not symmetric 
C) values can never be negative 
D) it has numerator degrees of freedom from sample 1 and denominator degrees of freedom from sample 2 
E) all of the above 
A) it is the ratio of two variances from two normal populations 
B) it is not symmetric 
C) values can never be negative 
D) it has numerator degrees of freedom from sample 1 and denominator degrees of freedom from sample 2 
E) all of the above

Question 12

A simple regression analysis linking a dependent variable y to an independent variable x is conducted with 20 observations.The p-value value for tstat in the  = 0 hypothesis test turns out to be .0162.The p-value value for Fstat in the same test must be

Accepted Answer

A) 1 - .0162 = .9838. 
B) 2 times .0162 = .0324 
C) .0162. 
D) statistically significant at the 1% significance level. 
E) none of the above 
A) 1 - .0162 = .9838. 
B) 2 times .0162 = .0324 
C) .0162. 
D) statistically significant at the 1% significance level. 
E) none of the above

Question 13

In regression analysis, which of the following is not a required assumption about the error term $\varepsilon$:

Accepted Answer

A) the expected value of the error term is zero 
B) the variance of the error term is constant 
C) the values of the error term must be positive. 
D) the error term is normally distributed 
E) all of the above 
A) the expected value of the error term is zero 
B) the variance of the error term is constant 
C) the values of the error term must be positive. 
D) the error term is normally distributed 
E) all of the above

Question 14

In simple linear regression analysis, when testing for significance, the F test and the t test will always yield consistent results.

Accepted Answer

A) True 
 B)False

Question 15

In multiple regression, an adjusted r2 value can be used to help avoid adding more and more potentially collinear independent variables to the model.

Accepted Answer

A) True 
 B)False

Question 16

A regression analysis involved 6 independent variables and 20 observations.In using the F table to find the critical value of F for testing the 'all s are 0' null hypothesis, the denominator degrees of freedom should be

Accepted Answer

A) 26 
B) 13 
C) 14 
D) 25 
E) none of the above 
A) 26 
B) 13 
C) 14 
D) 25 
E) none of the above

Question 17

In multiple regression, the adjusted r2 value is generally

Accepted Answer

A) greater than r2 
B) used to discourage adding more independent variables. 
C) used to encourage adding more independent variables. 
D) test for the significance of y 
E) a and d 
A) greater than r2 
B) used to discourage adding more independent variables. 
C) used to encourage adding more independent variables. 
D) test for the significance of y 
E) a and d

Question 18

The standard error of the estimate (sy.x) in multiple linear regression is the square of the Mean Square Error (MSE).

Accepted Answer

A) True 
 B)False

Question 19

The mean square (MS) values in an Analysis of Variance (ANOVA) for multiple regression are the sum of squares (SS) values divided by their corresponding degrees of freedom.

Accepted Answer

A) True 
 B)False

Question 20

In multiple regression, one's goal should be to effectively explain the variation in the dependent variable by using as many independent variables as possible.

Accepted Answer

A) True 
 B)False

In multiple regression analysis, multicollinearity (or simply collinearity) refers to the correlation among the independent variables.

In residual analysis for multiple regression, if the assumptions about the error term are valid and the model is an adequate representation of the relationships between the variables, then the plot of the residuals versus the predicted y values

In multiple linear regression, which of the following is NOT true about the hypothesis tests:

Residual analysis may make use of a plot showing the residuals or errors to evaluate the assumptions about the error term.

A regression analysis involved 4 independent variables and 20 observations.In using the F table to find the critical value of F for testing the 'all s are 0' null hypothesis, the numerator degrees of freedom should be

In regression analysis, which of the following is NOT a required assumption about the error term, $\varepsilon$ ?

Which of the following relationships in linear regression is correct?

Adding independent variables to a regression model will typically_______ the value of r².

In an F distribution with numerator degrees of freedom of 4 and denominator degrees of freedom of 8, 99% of the values are less than ______ .

Which of the following is true regarding the F distribution?

A simple regression analysis linking a dependent variable y to an independent variable x is conducted with 20 observations.The p-value value for t_stat in the  = 0 hypothesis test turns out to be .0162.The p-value value for F_stat in the same test must be

In regression analysis, which of the following is not a required assumption about the error term $\varepsilon$ :

In simple linear regression analysis, when testing for significance, the F test and the t test will always yield consistent results.

In multiple regression, an adjusted r² value can be used to help avoid adding more and more potentially collinear independent variables to the model.

A regression analysis involved 6 independent variables and 20 observations.In using the F table to find the critical value of F for testing the 'all s are 0' null hypothesis, the denominator degrees of freedom should be

In multiple regression, the adjusted r² value is generally

The standard error of the estimate (s_y.x) in multiple linear regression is the square of the Mean Square Error (MSE).

The mean square (MS) values in an Analysis of Variance (ANOVA) for multiple regression are the sum of squares (SS) values divided by their corresponding degrees of freedom.

In multiple regression, one's goal should be to effectively explain the variation in the dependent variable by using as many independent variables as possible.

An Introduction to Statistics

Descriptive Statistics I: Elementary Data Presentation and Description

Descriptive Statistics II: Additional Descriptive Measures and Data Displays

Probability

Discrete Probability Distributions

Continuous Probability Distributions

Statistical Inference: Estimating a Population Mean

Interval Estimates for Proportions, Mean Differences and Proportion Differences

Statistical Hypothesis Testing: Hypothesis Tests for a Population Mean

Hypothesis Tests for Proportions, Mean Differences and Proportion Differences

Basic Regression Analysis

F Tests and Analysis of Variance

Experimental Designonline Only

Chi-Square Tests

Filters

Exam 12: Multiple Regression

In multiple regression analysis, multicollinearity (or simply collinearity) refers to the correlation among the independent variables.

In residual analysis for multiple regression, if the assumptions about the error term are valid and the model is an adequate representation of the relationships between the variables, then the plot of the residuals versus the predicted y values

In multiple linear regression, which of the following is NOT true about the hypothesis tests:

Residual analysis may make use of a plot showing the residuals or errors to evaluate the assumptions about the error term.

A regression analysis involved 4 independent variables and 20 observations.In using the F table to find the critical value of F for testing the 'all s are 0' null hypothesis, the numerator degrees of freedom should be

In regression analysis, which of the following is NOT a required assumption about the error term, ε\varepsilonε ?

Which of the following relationships in linear regression is correct?

Adding independent variables to a regression model will typically_______ the value of r2.

In an F distribution with numerator degrees of freedom of 4 and denominator degrees of freedom of 8, 99% of the values are less than ______ .

Which of the following is true regarding the F distribution?

A simple regression analysis linking a dependent variable y to an independent variable x is conducted with 20 observations.The p-value value for tstat in the  = 0 hypothesis test turns out to be .0162.The p-value value for Fstat in the same test must be

In regression analysis, which of the following is not a required assumption about the error term ε\varepsilonε :

In simple linear regression analysis, when testing for significance, the F test and the t test will always yield consistent results.

In multiple regression, an adjusted r2 value can be used to help avoid adding more and more potentially collinear independent variables to the model.

A regression analysis involved 6 independent variables and 20 observations.In using the F table to find the critical value of F for testing the 'all s are 0' null hypothesis, the denominator degrees of freedom should be

In multiple regression, the adjusted r2 value is generally

The standard error of the estimate (sy.x) in multiple linear regression is the square of the Mean Square Error (MSE).

The mean square (MS) values in an Analysis of Variance (ANOVA) for multiple regression are the sum of squares (SS) values divided by their corresponding degrees of freedom.

In multiple regression, one's goal should be to effectively explain the variation in the dependent variable by using as many independent variables as possible.

An Introduction to Statistics

Descriptive Statistics I: Elementary Data Presentation and Description

Descriptive Statistics II: Additional Descriptive Measures and Data Displays

Probability

Discrete Probability Distributions

Continuous Probability Distributions

Statistical Inference: Estimating a Population Mean

Interval Estimates for Proportions, Mean Differences and Proportion Differences

Statistical Hypothesis Testing: Hypothesis Tests for a Population Mean

Hypothesis Tests for Proportions, Mean Differences and Proportion Differences

Basic Regression Analysis

F Tests and Analysis of Variance

Experimental Designonline Only

Chi-Square Tests

Filters

In regression analysis, which of the following is NOT a required assumption about the error term, $\varepsilon$ ?

Adding independent variables to a regression model will typically_______ the value of r².

A simple regression analysis linking a dependent variable y to an independent variable x is conducted with 20 observations.The p-value value for t_stat in the  = 0 hypothesis test turns out to be .0162.The p-value value for F_stat in the same test must be

In regression analysis, which of the following is not a required assumption about the error term $\varepsilon$ :

In multiple regression, an adjusted r² value can be used to help avoid adding more and more potentially collinear independent variables to the model.

In multiple regression, the adjusted r² value is generally

The standard error of the estimate (s_y.x) in multiple linear regression is the square of the Mean Square Error (MSE).