Question 1

The regression equation is determined by finding the minimum value for which of the following?

Accepted Answer

A) $\Sigma$(X - Y)2 
B) $\Sigma$(Y - ?) 
C) $\Sigma$(Y - ?)2 
D) r2$\Sigma$(Y - ?)2 
A) $\Sigma$(X - Y)2 
B) $\Sigma$(Y - ?) 
C) $\Sigma$(Y - ?)2 
D) r2$\Sigma$(Y - ?)2

Question 2

If the correlation between X1 and Y is r = 0.40 and the correlation between X2 and Y is r = 0.30, then a multiple regression equation using both X1 and X2 as predictors will produce R2 = 0.25.

Accepted Answer

A) True 
 B)False

Question 3

A linear regression equation is computed for a sample of n = 13 pairs of X and Y scores.For the analysis of regression testing the significance of the equation what are the df values for the F-ratio?

Accepted Answer

A) df = 1, 11 
B) df = 1, 12 
C) df = 2, 10 
D) df = 2, 11 
A) df = 1, 11 
B) df = 1, 12 
C) df = 2, 10 
D) df = 2, 11

Question 4

If the correlation between X and Y is r = 0.00, then the regression equation, Ŷ = bX + a, will have b = 0.

Accepted Answer

A) True 
 B)False

Question 5

For a linear regression equation computed for a sample of n = 30, the predicted portion of the Y-score variance (MSregression) has df = 2.

Accepted Answer

A) True 
 B)False

Question 6

A multiple regression equation is computed for a sample of n = 20 sets of X1, X2, and Y scores.If the significance of the regression equation is evaluated using an F-ratio, then the ratio would have degrees of freedom equal to

Accepted Answer

A) df = 1, 18 
B) df = 1, 19 
C) df = 2, 17 
D) df = 2, 18 
A) df = 1, 18 
B) df = 1, 19 
C) df = 2, 17 
D) df = 2, 18

Question 7

The linear regression equation is structured so that when X = MX, the predicted value of Y is equal to MY.

Accepted Answer

A) True 
 B)False

Question 8

A set of n = 15 pairs of X and Y scores has SSX = 10, SSY = 40, and SP = 30.What is the slope for the regression equation for predicting Y from X?

Accepted Answer

A) 10/30 
B) 10/40 
C) 40/10 
D) 30/10 
A) 10/30 
B) 10/40 
C) 40/10 
D) 30/10

Question 9

Assuming that SSY is constant, which of the following correlations would have the largest SSresidual?

Accepted Answer

A) r = -0.10 
B) r = +0.40 
C) r = -0.70 
D) There is no relationship between the correlation and SSresidual. 
A) r = -0.10 
B) r = +0.40 
C) r = -0.70 
D) There is no relationship between the correlation and SSresidual.

Question 10

The Pearson correlation between X1 and Y is r = 0.40 and SSY = 100.When a second variable, X2, is added to the regression equation, we obtain R2 = 0.25.How much additional variance is predicted by adding the second variable compared to using X1 alone?

Accepted Answer

A) 9 points 
B) 15 points 
C) 25 points 
D) 40 points 
A) 9 points 
B) 15 points 
C) 25 points 
D) 40 points

Question 11

For a linear regression equation calculated for a sample of n = 20 pairs of X and Y values, what would be the df value for the standard error of estimate?

Accepted Answer

A) 1 
B) 2 
C) 18 
D) 19 
A) 1 
B) 2 
C) 18 
D) 19

Question 12

It is possible for the regression equation to have none of the actual (observed) data points located on the regression line.

Accepted Answer

A) True 
 B)False

Question 13

For a linear regression equation computed for a sample of n = 30, the unpredicted portion of the Y-score variance (MSresidual) has df = 28.

Accepted Answer

A) True 
 B)False

Question 14

The standardized form of the regression equation is zY = (beta)zX.In the equation, what is the value of beta?

Accepted Answer

A) Beta is equal to the correlation between X and Y. 
B) Beta is equal to SP/ SSX. 
C) Beta is equal to r/(n - 1) 
D) Beta is equal to r/n 
A) Beta is equal to the correlation between X and Y. 
B) Beta is equal to SP/ SSX. 
C) Beta is equal to r/(n - 1) 
D) Beta is equal to r/n

Question 15

A multiple regression equation with two predictor variables produces R2 = 0.10.What portion of the variability for the Y scores is predicted by the equation?

Accepted Answer

A) 1% 
B) 10% 
C) 90% 
D) 99% 
A) 1% 
B) 10% 
C) 90% 
D) 99%

Question 16

If the Pearson correlation between X and Y is r = 0.60, then the regression equation predicts 36% of the variance in the Y scores.

Accepted Answer

A) True 
 B)False

Question 17

For the linear equation Y = 2X - 3, which of the following points will not be on the line?

Accepted Answer

A) 0, -3 
B) 2, 1 
C) 3, 0 
D) 4, 5 
A) 0, -3 
B) 2, 1 
C) 3, 0 
D) 4, 5

Question 18

Which of the following is the standard error of estimate for a multiple regression equation with two predictor variables?

Accepted Answer

A) the square root of SSresidual/(n - 2) 
B) the square root of SSresidual/(n - 3) 
C) the square root of SSregression/(n - 2) 
D) the square root of SSregression/(n - 3) 
A) the square root of SSresidual/(n - 2) 
B) the square root of SSresidual/(n - 3) 
C) the square root of SSregression/(n - 2) 
D) the square root of SSregression/(n - 3)

Question 19

For either linear regression or multiple regression, the standard error of estimate is equal to the square root of MSresidual.

Accepted Answer

A) True 
 B)False

Question 20

If a linear regression equation predicts more than 50% of the variance in the Y scores, then the correlation between X and Y must be greater than r = 0.70.

Accepted Answer

A) True 
 B)False

The regression equation is determined by finding the minimum value for which of the following?

If the correlation between X₁ and Y is r = 0.40 and the correlation between X₂ and Y is r = 0.30, then a multiple regression equation using both X₁ and X₂ as predictors will produce R² = 0.25.

A linear regression equation is computed for a sample of n = 13 pairs of X and Y scores.For the analysis of regression testing the significance of the equation what are the df values for the F-ratio?

If the correlation between X and Y is r = 0.00, then the regression equation, Ŷ = bX + a, will have b = 0.

For a linear regression equation computed for a sample of n = 30, the predicted portion of the Y-score variance (MS_regression) has df = 2.

A multiple regression equation is computed for a sample of n = 20 sets of X₁, X₂, and Y scores.If the significance of the regression equation is evaluated using an F-ratio, then the ratio would have degrees of freedom equal to

The linear regression equation is structured so that when X = M_X, the predicted value of Y is equal to M_Y.

A set of n = 15 pairs of X and Y scores has SS_X = 10, SS_Y = 40, and SP = 30.What is the slope for the regression equation for predicting Y from X?

Assuming that SS_Y is constant, which of the following correlations would have the largest SS_residual?

The Pearson correlation between X₁ and Y is r = 0.40 and SS_Y = 100.When a second variable, X₂, is added to the regression equation, we obtain R² = 0.25.How much additional variance is predicted by adding the second variable compared to using X₁ alone?

For a linear regression equation calculated for a sample of n = 20 pairs of X and Y values, what would be the df value for the standard error of estimate?

It is possible for the regression equation to have none of the actual (observed) data points located on the regression line.

For a linear regression equation computed for a sample of n = 30, the unpredicted portion of the Y-score variance (MS_residual) has df = 28.

The standardized form of the regression equation is z_Y = (beta)z_X.In the equation, what is the value of beta?

A multiple regression equation with two predictor variables produces R² = 0.10.What portion of the variability for the Y scores is predicted by the equation?

If the Pearson correlation between X and Y is r = 0.60, then the regression equation predicts 36% of the variance in the Y scores.

For the linear equation Y = 2X - 3, which of the following points will not be on the line?

Which of the following is the standard error of estimate for a multiple regression equation with two predictor variables?

For either linear regression or multiple regression, the standard error of estimate is equal to the square root of MS_residual.

If a linear regression equation predicts more than 50% of the variance in the Y scores, then the correlation between X and Y must be greater than r = 0.70.

Introduction to Statistics

Frequency Distributions

Central Tendency

Variability

Z-Scores

Probability

The Distribution of Sample Means

Introduction to Hypothesis Testing

Introduction to the T Statistic

The T Test for Two Independent Samples

The T Test for Two Related Samples

Introduction to Analysis of Variance

Repeated-Measures Anova

Two-Factor Analysis of Variance

Correlation

Chi-Square Tests

The Binomial Test

Choosing the Right Statistics

Filters

Exam 16: Introduction to Regression

The regression equation is determined by finding the minimum value for which of the following?

If the correlation between X1 and Y is r = 0.40 and the correlation between X2 and Y is r = 0.30, then a multiple regression equation using both X1 and X2 as predictors will produce R2 = 0.25.

A linear regression equation is computed for a sample of n = 13 pairs of X and Y scores.For the analysis of regression testing the significance of the equation what are the df values for the F-ratio?

If the correlation between X and Y is r = 0.00, then the regression equation, Ŷ = bX + a, will have b = 0.

For a linear regression equation computed for a sample of n = 30, the predicted portion of the Y-score variance (MSregression) has df = 2.

A multiple regression equation is computed for a sample of n = 20 sets of X1, X2, and Y scores.If the significance of the regression equation is evaluated using an F-ratio, then the ratio would have degrees of freedom equal to

The linear regression equation is structured so that when X = MX, the predicted value of Y is equal to MY.

A set of n = 15 pairs of X and Y scores has SSX = 10, SSY = 40, and SP = 30.What is the slope for the regression equation for predicting Y from X?

Assuming that SSY is constant, which of the following correlations would have the largest SSresidual?

The Pearson correlation between X1 and Y is r = 0.40 and SSY = 100.When a second variable, X2, is added to the regression equation, we obtain R2 = 0.25.How much additional variance is predicted by adding the second variable compared to using X1 alone?

For a linear regression equation calculated for a sample of n = 20 pairs of X and Y values, what would be the df value for the standard error of estimate?

It is possible for the regression equation to have none of the actual (observed) data points located on the regression line.

For a linear regression equation computed for a sample of n = 30, the unpredicted portion of the Y-score variance (MSresidual) has df = 28.

The standardized form of the regression equation is zY = (beta)zX.In the equation, what is the value of beta?

A multiple regression equation with two predictor variables produces R2 = 0.10.What portion of the variability for the Y scores is predicted by the equation?

If the Pearson correlation between X and Y is r = 0.60, then the regression equation predicts 36% of the variance in the Y scores.

For the linear equation Y = 2X - 3, which of the following points will not be on the line?

Which of the following is the standard error of estimate for a multiple regression equation with two predictor variables?

For either linear regression or multiple regression, the standard error of estimate is equal to the square root of MSresidual.

If a linear regression equation predicts more than 50% of the variance in the Y scores, then the correlation between X and Y must be greater than r = 0.70.

Introduction to Statistics

Frequency Distributions

Central Tendency

Variability

Z-Scores

Probability

The Distribution of Sample Means

Introduction to Hypothesis Testing

Introduction to the T Statistic

The T Test for Two Independent Samples

The T Test for Two Related Samples

Introduction to Analysis of Variance

Repeated-Measures Anova

Two-Factor Analysis of Variance

Correlation

Chi-Square Tests

The Binomial Test

Choosing the Right Statistics

Filters

If the correlation between X₁ and Y is r = 0.40 and the correlation between X₂ and Y is r = 0.30, then a multiple regression equation using both X₁ and X₂ as predictors will produce R² = 0.25.

For a linear regression equation computed for a sample of n = 30, the predicted portion of the Y-score variance (MS_regression) has df = 2.

A multiple regression equation is computed for a sample of n = 20 sets of X₁, X₂, and Y scores.If the significance of the regression equation is evaluated using an F-ratio, then the ratio would have degrees of freedom equal to

The linear regression equation is structured so that when X = M_X, the predicted value of Y is equal to M_Y.

A set of n = 15 pairs of X and Y scores has SS_X = 10, SS_Y = 40, and SP = 30.What is the slope for the regression equation for predicting Y from X?

Assuming that SS_Y is constant, which of the following correlations would have the largest SS_residual?

The Pearson correlation between X₁ and Y is r = 0.40 and SS_Y = 100.When a second variable, X₂, is added to the regression equation, we obtain R² = 0.25.How much additional variance is predicted by adding the second variable compared to using X₁ alone?

For a linear regression equation computed for a sample of n = 30, the unpredicted portion of the Y-score variance (MS_residual) has df = 28.

The standardized form of the regression equation is z_Y = (beta)z_X.In the equation, what is the value of beta?

A multiple regression equation with two predictor variables produces R² = 0.10.What portion of the variability for the Y scores is predicted by the equation?

For either linear regression or multiple regression, the standard error of estimate is equal to the square root of MS_residual.