Question 1

Multiple regression is a statistical method that includes ____ predictor variable(s)in the equation of the regression line.

Accepted Answer

A) zero 
B) one 
C) two 
D) two or more 
A) zero 
B) one 
C) two 
D) two or more

Question 2

Which of the following is a step to evaluate the significance for the relative contribution of each factor:

Accepted Answer

A) Find r2 for the "other" predictor variable 
B) Complete the F table and make a decision 
C) Identify SS accounted for by the predictor variable of interest 
D) All of the above 
A) Find r2 for the "other" predictor variable 
B) Complete the F table and make a decision 
C) Identify SS accounted for by the predictor variable of interest 
D) All of the above

Question 3

The degrees of freedom residual is equal to the number of criterion variables.

Accepted Answer

A) True 
 B)False

Question 4

The degrees of freedom associated with regression variation are equal to

Accepted Answer

A) the number of predictor variables 
B) the number of predictor variables minus one 
C) n ñ 1 
D) n ñ 2 
A) the number of predictor variables 
B) the number of predictor variables minus one 
C) n ñ 1 
D) n ñ 2

Question 5

The standard error of estimate provides an estimate of the standard distance that data points fall from the regression line.

Accepted Answer

A) True 
 B)False

Question 6

Linear regression describes the extent to which _______ predicts ________.

Accepted Answer

A) X;Y 
B) the predictor variable;the criterion variable 
C) the known variable;the to-be-predicted variable 
D) all of the above 
A) X;Y 
B) the predictor variable;the criterion variable 
C) the known variable;the to-be-predicted variable 
D) all of the above

Question 7

To summarize any type of regression analysis using APA format,we report each of the following except the,

Accepted Answer

A) test statistic 
B) degrees of freedom 
C) p value 
D) critical values 
A) test statistic 
B) degrees of freedom 
C) p value 
D) critical values

Question 8

The "left-over" or remaining variation attributed to error in an analysis of regression is called residual variation.

Accepted Answer

A) True 
 B)False

Question 9

The regression equation measures

Accepted Answer

A) how far the sample mean deviates from the population mean 
B) how far each data point deviates from the line that most closely fits the data 
C) how significant mean differences are between groups 
D) how often scores regress from deviations in the data 
A) how far the sample mean deviates from the population mean 
B) how far each data point deviates from the line that most closely fits the data 
C) how significant mean differences are between groups 
D) how often scores regress from deviations in the data

Question 10

Multiple regression can be used to measure predictive variability for any number of predictor variables.

Accepted Answer

A) True 
 B)False

Question 11

One key advantage for including multiple predictor variables in the equation of a regression line is that it allows you to

Accepted Answer

A) detect mean differences between groups for each criterion variable 
B) detect the extent to which two or more predictor variables interact 
C) show cause-and-effect because many predictor variables are added 
D) all of the above 
A) detect mean differences between groups for each criterion variable 
B) detect the extent to which two or more predictor variables interact 
C) show cause-and-effect because many predictor variables are added 
D) all of the above

Question 12

Using an analysis of regression,the variability in Y that is associated with error is measured by the

Accepted Answer

A) regression variation 
B) residual variation 
C) correlation coefficient 
D) coefficient of determination 
A) regression variation 
B) residual variation 
C) correlation coefficient 
D) coefficient of determination

Question 13

With multiple regression,we can use the method of least squares to find the regression equation and test for significance just as we did using simple linear regression.

Accepted Answer

A) True 
 B)False

Question 14

Linear regression is used to measure the extent to which a criterion variable causes changes in a predictor variable.

Accepted Answer

A) True 
 B)False

Question 15

To compute the standard error of estimate,we take the square root of the mean square residual.

Accepted Answer

A) True 
 B)False

Question 16

In a sample of 28 participants,suppose we conduct an analysis of regression with one predictor variable.If   = 4.28,then what is the decision for this test at a .05 level of significance?

Accepted Answer

A) X significantly predicts Y. 
B) X does not significantly predict Y. 
C) There is not enough information to answer this question. 
A) X significantly predicts Y. 
B) X does not significantly predict Y. 
C) There is not enough information to answer this question.

Question 17

For a simple linear regression with one predictor variable,we report the test statistic,degrees of freedom,and p value for the regression analysis.

Accepted Answer

A) True 
 B)False

Question 18

A researcher reports the following equation for a best-fitting straight line to a set of data points:   .Which value is the y-intercept?

Accepted Answer

A)    
B)    
C) ñ1.01 
D) 3.24 
A)    
B)    
C) ñ1.01 
D) 3.24

Question 19

The larger the standard error of estimate,the more accurately known values of X will predict values of Y.

Accepted Answer

A) True 
 B)False

Question 20

The regression line is not always the best fitting straight line to a set of data points.

Accepted Answer

A) True 
 B)False

Multiple regression is a statistical method that includes ____ predictor variable(s)in the equation of the regression line.

Which of the following is a step to evaluate the significance for the relative contribution of each factor:

The degrees of freedom residual is equal to the number of criterion variables.

The degrees of freedom associated with regression variation are equal to

The standard error of estimate provides an estimate of the standard distance that data points fall from the regression line.

Linear regression describes the extent to which _ predicts __.

To summarize any type of regression analysis using APA format,we report each of the following except the,

The "left-over" or remaining variation attributed to error in an analysis of regression is called residual variation.

The regression equation measures

Multiple regression can be used to measure predictive variability for any number of predictor variables.

One key advantage for including multiple predictor variables in the equation of a regression line is that it allows you to

Using an analysis of regression,the variability in Y that is associated with error is measured by the

With multiple regression,we can use the method of least squares to find the regression equation and test for significance just as we did using simple linear regression.

Linear regression is used to measure the extent to which a criterion variable causes changes in a predictor variable.

To compute the standard error of estimate,we take the square root of the mean square residual.

In a sample of 28 participants,suppose we conduct an analysis of regression with one predictor variable.If = 4.28,then what is the decision for this test at a .05 level of significance?

For a simple linear regression with one predictor variable,we report the test statistic,degrees of freedom,and p value for the regression analysis.

A researcher reports the following equation for a best-fitting straight line to a set of data points: .Which value is the y-intercept?

The larger the standard error of estimate,the more accurately known values of X will predict values of Y.

The regression line is not always the best fitting straight line to a set of data points.

Introduction to Statistics

Summarizing Data: Frequency Distributions in

Summarizing Data: Central Tendency

Summarizing Data: Variability

Probability, Normal Distributions, and Z Scores

Characteristics of the Sample Mean

Hypothesis Testing: Significance, Effect Size, and Power

Testing Means: One-Sample T Test With Confidence Intervals

Testing Means: Two- Independent-Sample T Test With Confidence Intervals

Testing Means: Related-Samples T Test With Confidence Intervals

One-Way Analysis of Variance: Between- Subjects and Within- Subjects Repeated- Measures Designs

Two-Way Analysis of Variance: Between-Subjects Factorial Design

Correlation and Linear Regression

Chi-Square Tests: Goodness of Fit and the Test for Independence

Nonparametric Tests for Ordinal Data: Understanding and Interpretation

Chi-Square Tests: Goodness-of-Fit and Independence

Understanding Correlation Coefficients in Statistical Analysis

Filters

Exam 17: Understanding Regression Analysis

Multiple regression is a statistical method that includes ____ predictor variable(s)in the equation of the regression line.

Which of the following is a step to evaluate the significance for the relative contribution of each factor:

The degrees of freedom residual is equal to the number of criterion variables.

The degrees of freedom associated with regression variation are equal to

The standard error of estimate provides an estimate of the standard distance that data points fall from the regression line.

Linear regression describes the extent to which _______ predicts ________.

To summarize any type of regression analysis using APA format,we report each of the following except the,

The "left-over" or remaining variation attributed to error in an analysis of regression is called residual variation.

The regression equation measures

Multiple regression can be used to measure predictive variability for any number of predictor variables.

One key advantage for including multiple predictor variables in the equation of a regression line is that it allows you to

Using an analysis of regression,the variability in Y that is associated with error is measured by the

With multiple regression,we can use the method of least squares to find the regression equation and test for significance just as we did using simple linear regression.

Linear regression is used to measure the extent to which a criterion variable causes changes in a predictor variable.

To compute the standard error of estimate,we take the square root of the mean square residual.

In a sample of 28 participants,suppose we conduct an analysis of regression with one predictor variable.If = 4.28,then what is the decision for this test at a .05 level of significance?

For a simple linear regression with one predictor variable,we report the test statistic,degrees of freedom,and p value for the regression analysis.

A researcher reports the following equation for a best-fitting straight line to a set of data points: .Which value is the y-intercept?

The larger the standard error of estimate,the more accurately known values of X will predict values of Y.

The regression line is not always the best fitting straight line to a set of data points.

Introduction to Statistics

Summarizing Data: Frequency Distributions in

Summarizing Data: Central Tendency

Summarizing Data: Variability

Probability, Normal Distributions, and Z Scores

Characteristics of the Sample Mean

Hypothesis Testing: Significance, Effect Size, and Power

Testing Means: One-Sample T Test With Confidence Intervals

Testing Means: Two- Independent-Sample T Test With Confidence Intervals

Testing Means: Related-Samples T Test With Confidence Intervals

One-Way Analysis of Variance: Between- Subjects and Within- Subjects Repeated- Measures Designs

Two-Way Analysis of Variance: Between-Subjects Factorial Design

Correlation and Linear Regression

Chi-Square Tests: Goodness of Fit and the Test for Independence

Nonparametric Tests for Ordinal Data: Understanding and Interpretation

Chi-Square Tests: Goodness-of-Fit and Independence

Understanding Correlation Coefficients in Statistical Analysis

Filters

Linear regression describes the extent to which _ predicts __.