Question 1

Which of the following is used to determine the linear equation that ìbest fitsî a set of data points?

Accepted Answer

A) correlational analysis 
B) analysis of variance 
C) analysis of regression 
D) method of least squares 
A) correlational analysis 
B) analysis of variance 
C) analysis of regression 
D) method of least squares

Question 2

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

Accepted Answer

A) True 
 B)False

Question 3

For a multiple regression analysis with 2 and 12 degrees of freedom,MS regression is 135 and MS residual is 15.What is the decision for this test?

Accepted Answer

A) reject the null hypothesis;the predictive variability of two predictor factors are significant 
B) retain the null hypothesis;the predictive variability of two predictor factors are not significant 
C) reject the null hypothesis;the predictive variability of one predictor factor is significant 
D) retain the null hypothesis;the predictive variability of one predictor factor is significant 
A) reject the null hypothesis;the predictive variability of two predictor factors are significant 
B) retain the null hypothesis;the predictive variability of two predictor factors are not significant 
C) reject the null hypothesis;the predictive variability of one predictor factor is significant 
D) retain the null hypothesis;the predictive variability of one predictor factor is significant

Question 4

The null hypothesis for a test of the relative contribution of two predictor variables is that adding one factor improves the prediction of variance in Y beyond that already predicted by the second factor.

Accepted Answer

A) True 
 B)False

Question 5

The equation for a straight line is Y = bX + a.

Accepted Answer

A) True 
 B)False

Question 6

Which of the following is used to determine the significance of predictions made by a best fitting linear equation?

Accepted Answer

A) correlational analysis 
B) analysis of variance 
C) analysis of regression 
D) method of least squares 
A) correlational analysis 
B) analysis of variance 
C) analysis of regression 
D) method of least squares

Question 7

A researcher measures the extent to which the speed at which people eat (in minutes)predicts calorie intake (in kilocalories).Which factor is the predictor variable in this example?

Accepted Answer

A) the speed at which people eat 
B) calorie intake 
C) minutes and kilocalories 
D) all of the above 
A) the speed at which people eat 
B) calorie intake 
C) minutes and kilocalories 
D) all of the above

Question 8

The slope (b)is a measure of the change in Y relative to the change in X.

Accepted Answer

A) True 
 B)False

Question 9

In an analysis of regression there are three sources of variation: regression,residual,and error.

Accepted Answer

A) True 
 B)False

Question 10

Including two or more predictor variables into a single equation of a regression line can be more informative than an analysis using only one predictor variable.

Accepted Answer

A) True 
 B)False

Question 11

We can evaluate the significance of the relative contribution of each factor by first determining the differences between group means.

Accepted Answer

A) True 
 B)False

Question 12

To standardize the beta coefficients,we first,

Accepted Answer

A) analyze the significance of each data point 
B) analyze the residual variation 
C) convert the original data to standardized z scores 
D) compute the standard error of estimate 
A) analyze the significance of each data point 
B) analyze the residual variation 
C) convert the original data to standardized z scores 
D) compute the standard error of estimate

Question 13

A researcher reports the following regression equation for two variables,X and Y:   .If X = 2.30,then what is the value of   ?

Accepted Answer

A) 10.23 
B) 11.73 
C) 13.23 
A) 10.23 
B) 11.73 
C) 13.23

Question 14

A statistical method that includes two or more predictor variables in the equation of a regression line to predict changes in a criterion variable is called

Accepted Answer

A) analysis of variance 
B) standard error of estimate 
C) residual regression 
D) multiple regression 
A) analysis of variance 
B) standard error of estimate 
C) residual regression 
D) multiple regression

Question 15

We can evaluate the relative contribution of each predictor variable by evaluating the significance of the added contribution of each factor.

Accepted Answer

A) True 
 B)False

Question 16

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 17

If the coefficient of determination is 0.30 and the sum of squares regression for an analysis of regression is 210,then what is the value of   ?

Accepted Answer

A) 210 
B) 300 
C) 490 
D) 700 
A) 210 
B) 300 
C) 490 
D) 700

Question 18

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 19

Using an analysis of regression,the variability in Y that is predicted by X 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 20

A researcher measures the extent to which years of marriage predict perceptions of forgiveness.Which factor is the criterion variable in this example?

Accepted Answer

A) years of marriage 
B) perceptions of forgiveness 
C) both years of marriage and perceptions of forgiveness 
D) none of the above 
A) years of marriage 
B) perceptions of forgiveness 
C) both years of marriage and perceptions of forgiveness 
D) none of the above

Which of the following is used to determine the linear equation that ìbest fitsî a set of data points?

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

For a multiple regression analysis with 2 and 12 degrees of freedom,MS regression is 135 and MS residual is 15.What is the decision for this test?

The null hypothesis for a test of the relative contribution of two predictor variables is that adding one factor improves the prediction of variance in Y beyond that already predicted by the second factor.

The equation for a straight line is Y = bX + a.

Which of the following is used to determine the significance of predictions made by a best fitting linear equation?

A researcher measures the extent to which the speed at which people eat (in minutes)predicts calorie intake (in kilocalories).Which factor is the predictor variable in this example?

The slope (b)is a measure of the change in Y relative to the change in X.

In an analysis of regression there are three sources of variation: regression,residual,and error.

Including two or more predictor variables into a single equation of a regression line can be more informative than an analysis using only one predictor variable.

We can evaluate the significance of the relative contribution of each factor by first determining the differences between group means.

To standardize the beta coefficients,we first,

A researcher reports the following regression equation for two variables,X and Y: .If X = 2.30,then what is the value of ?

A statistical method that includes two or more predictor variables in the equation of a regression line to predict changes in a criterion variable is called

We can evaluate the relative contribution of each predictor variable by evaluating the significance of the added contribution of each factor.

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

If the coefficient of determination is 0.30 and the sum of squares regression for an analysis of regression is 210,then what is the value of ?

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.

Using an analysis of regression,the variability in Y that is predicted by X is measured by the

A researcher measures the extent to which years of marriage predict perceptions of forgiveness.Which factor is the criterion variable in this example?

Introduction to Statistics

Summarizing Data: Frequency Distributions in Tables and Graphs

Summarizing Data: Central Tendency

Summarizing Data: Variability

Probability

Probability, Normal Distributions, and Z Scores

Probability and Sampling Distributions

Hypothesis Testing: Significance, Effect Size, and Power

Testing Means: One-Sample and Two-Independent Sample T Tests

Testing Means: Related Samples T Test

Estimation and Confidence Intervals

Analysis of Variance: One-Way Between-Subjects Design

Analysis of Variance: One-Way Within-Subjects Repeated Measures Design

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

Correlation

Nonparametric Tests: Chi-Square Tests

Nonparametric Tests: Tests for Ordinal Data

Filters

Exam 16: Linear Regression and Multiple Regression

Which of the following is used to determine the linear equation that ìbest fitsî a set of data points?

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

For a multiple regression analysis with 2 and 12 degrees of freedom,MS regression is 135 and MS residual is 15.What is the decision for this test?

The null hypothesis for a test of the relative contribution of two predictor variables is that adding one factor improves the prediction of variance in Y beyond that already predicted by the second factor.

The equation for a straight line is Y = bX + a.

Which of the following is used to determine the significance of predictions made by a best fitting linear equation?

A researcher measures the extent to which the speed at which people eat (in minutes)predicts calorie intake (in kilocalories).Which factor is the predictor variable in this example?

The slope (b)is a measure of the change in Y relative to the change in X.

In an analysis of regression there are three sources of variation: regression,residual,and error.

Including two or more predictor variables into a single equation of a regression line can be more informative than an analysis using only one predictor variable.

We can evaluate the significance of the relative contribution of each factor by first determining the differences between group means.

To standardize the beta coefficients,we first,

A researcher reports the following regression equation for two variables,X and Y: .If X = 2.30,then what is the value of ?

A statistical method that includes two or more predictor variables in the equation of a regression line to predict changes in a criterion variable is called

We can evaluate the relative contribution of each predictor variable by evaluating the significance of the added contribution of each factor.

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

If the coefficient of determination is 0.30 and the sum of squares regression for an analysis of regression is 210,then what is the value of ?

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.

Using an analysis of regression,the variability in Y that is predicted by X is measured by the

A researcher measures the extent to which years of marriage predict perceptions of forgiveness.Which factor is the criterion variable in this example?

Introduction to Statistics

Summarizing Data: Frequency Distributions in Tables and Graphs

Summarizing Data: Central Tendency

Summarizing Data: Variability

Probability

Probability, Normal Distributions, and Z Scores

Probability and Sampling Distributions

Hypothesis Testing: Significance, Effect Size, and Power

Testing Means: One-Sample and Two-Independent Sample T Tests

Testing Means: Related Samples T Test

Estimation and Confidence Intervals

Analysis of Variance: One-Way Between-Subjects Design

Analysis of Variance: One-Way Within-Subjects Repeated Measures Design

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

Correlation

Nonparametric Tests: Chi-Square Tests

Nonparametric Tests: Tests for Ordinal Data

Filters