Question 1

Which of the following is that part of the dependent variable that is not predicted by the independent variable?

Accepted Answer

A)  Covariate 
B)  Intercept 
C)  Residual 
D)  Slope 
A)  Covariate 
B)  Intercept 
C)  Residual 
D)  Slope C

Question 2

You are given the following pairs of scores on X (Pretest score) and Y (Posttest score).

$\begin{array}{cc}
\hline X & Y \
\hline 65 & 74 \
82 & 87 \
70 & 82 \
46 & 53 \
55 & 69 \
75 & 81 \
\hline
\end{array}$

a. Find the linear regression model for predicting Y from X.
b. Use the prediction model obtained to predict the value of Y for a new person who scored 80 on the pretest.

Accepted Answer

a. Intercept a = 15.919, slope b = .892. The regression model is Y_i = .892X_i + 15.919 + e_i The prediction equation is Y_i = .892X_i + 15.919 b. When X = 80, Y' = .892X + 15.919 = .892(80) + 15.919 = 87.279Y' Procedure: Create a data set with two variables: Pretest (X), Posttest (Y). The data set should have 6 cases. 1) Go to Analyze $\rightarrow$ Regression $\rightarrow$ Linear. 2) Select Posttest to the Dependent list. Select Pretest to the Independent(s) list. 3) Click Statistics. Select Confidence interval under Regression Coefficients. Click Continue. "4) Click OK. Selected SPSS Output: $\begin{array}{l} \text { Model Summary }\ \begin{array}{ccccc} \hline \text { Model } & R & R \text { Square } & \text { Adjusted R Square } & \text { Std. Error of the Estimate } \ \hline 1 & .964 & .930 & .912 & 3.627 \ \hline \end{array} \end{array}$

Question 3

The sample intercept is which of the following? Select all that apply.

Accepted Answer

A)  The point where the regression line crosses the Y axis 
B)  The predicted change in Y for a one-unit change in X 
C)  The unstandardized regression coefficient 
D)  The value of the dependent variable when the independent variable is zero 
A)  The point where the regression line crosses the Y axis 
B)  The predicted change in Y for a one-unit change in X 
C)  The unstandardized regression coefficient 
D)  The value of the dependent variable when the independent variable is zero A,D

Question 4

Which one of the following reflects variables appropriate for a simple linear regression model?

Accepted Answer

A)  One categorical dependent variable and one continuous independent variable 
B)  One continuous dependent variable and one continuous or categorical independent variable 
C)  One continuous dependent variable and two or more continuous independent variables 
D)  Two or more continuous dependent variables and one continuous or categorical independent variable 
A)  One categorical dependent variable and one continuous independent variable 
B)  One continuous dependent variable and one continuous or categorical independent variable 
C)  One continuous dependent variable and two or more continuous independent variables 
D)  Two or more continuous dependent variables and one continuous or categorical independent variable

Question 5

In which of the following situations is it most appropriate to use the simple linear regression model?

Accepted Answer

A)    
B)    
C)    
D)    
A)    
B)    
C)    
D)

Question 6

You are given the following pairs of scores on X (Percentage of students whose families are below poverty line) and Y (Percentage of students at or above proficiency level) for nine schools.

$\begin{array}{cc}
\hline X & Y \
\hline 92.3 & 18.9 \
0.9 & 87.3 \
25.1 & 48.0 \
67.1 & 45.4 \
24.7 & 80.2 \
90.7 & 13.7 \
44.0 & 26.9 \
65.5 & 58.6 \
40.6 & 46.3 \
\hline
\end{array}$

a. Find the linear regression model for predicting Y from X.
b. Use the prediction model obtained to predict the value of Y for a school that has 50% of students whose families are below the poverty line.

Accepted Answer

a. Intercept a = 15.919, slope b = .892.

Question 7

You are given the following pairs of scores on X (height in inches) and Y (weight in lbs).

$\begin{array}{cc}
X & Y \
\hline 66 & 140 \
69 & 155 \
72 & 195 \
74 & 160 \
72 & 155 \
67 & 145 \
66 & 135 \
71 & 170 \
70 & 130 \
68 & 170 \
72 & 190 \
69 & 145 \
73 & 155 \
68 & 150 \
68 & 130 \
69 & 145 \
69 & 150 \
66 & 120 \
62 & 131 \
62 & 120 \
64 & 102 \
68 & 110 \
63 & 116 \
64 & 125 \
62 & 110 \
\hline
\end{array}$ 
Perform the following computations using $\alpha$= .05. 
a. The regression equation of Y predicted by X.
b. Test of the significance of X as a predictor.
c. Plot Y versus X.
d. Compute the residuals.
e. Plot residuals versus X.

Accepted Answer

a. Intercept a = -199.139, slope b = 5.0

Question 8

Complete this sentence by selecting one of the following statements: "In simple linear regression, if the slope is found to be ?0.002, . . ."

Accepted Answer

A)  the value of Y is equal to ?.002 when X is 0. 
B)  the value of Y is equal to ?.002 when X is 1. 
C)  the value of Y will decrease by 0.002 units when X increases by 1 unit. 
D)  there is a negative, but very weak relationship between X and Y. 
A)  the value of Y is equal to ?.002 when X is 0. 
B)  the value of Y is equal to ?.002 when X is 1. 
C)  the value of Y will decrease by 0.002 units when X increases by 1 unit. 
D)  there is a negative, but very weak relationship between X and Y.

Which of the following is that part of the dependent variable that is not predicted by the independent variable?

You are given the following pairs of scores on X (Pretest score) and Y (Posttest score). X Y 65 74 82 87 70 82 46 53 55 69 75 81 a. Find the linear regression model for predicting Y from X. b. Use the prediction model obtained to predict the value of Y for a new person who scored 80 on the pretest.

The sample intercept is which of the following? Select all that apply.

Which one of the following reflects variables appropriate for a simple linear regression model?

In which of the following situations is it most appropriate to use the simple linear regression model?

Complete this sentence by selecting one of the following statements: "In simple linear regression, if the slope is found to be ?0.002, . . ."

Introduction

Data Representation

Univariate Population Parameters and Sample Statistics

The Normal Distribution and Standard Scores

Introduction to Probability and Sample Statistics

Introduction to Hypothesis Testing: Inferences About a Single Mean

Inferences About the Difference Between Two Means

Inferences About Proportions

Inferences About Variances

Bivariate Measures of Association

One-Factor Anova: Fixed-Effects Mode

Multiple Comparison Procedures

Factorial Anova: Fixed-Effects Mode

One Factor Fixed-Effects Ancova With Single Covariate

Random- and Mixed-Effects Analysis of Variance Models

Hierarchical and Randomized Block Analysis of Variance Models

Multiple Linear Regression

Logistic Regression

Mediation and Moderation

Filters

Exam 17: Simple Linear Regression

Which of the following is that part of the dependent variable that is not predicted by the independent variable?

You are given the following pairs of scores on X (Pretest score) and Y (Posttest score). X Y 65 74 82 87 70 82 46 53 55 69 75 81 a. Find the linear regression model for predicting Y from X. b. Use the prediction model obtained to predict the value of Y for a new person who scored 80 on the pretest.

The sample intercept is which of the following? Select all that apply.

Which one of the following reflects variables appropriate for a simple linear regression model?

In which of the following situations is it most appropriate to use the simple linear regression model?

Complete this sentence by selecting one of the following statements: "In simple linear regression, if the slope is found to be ?0.002, . . ."

Introduction

Data Representation

Univariate Population Parameters and Sample Statistics

The Normal Distribution and Standard Scores

Introduction to Probability and Sample Statistics

Introduction to Hypothesis Testing: Inferences About a Single Mean

Inferences About the Difference Between Two Means

Inferences About Proportions

Inferences About Variances

Bivariate Measures of Association

One-Factor Anova: Fixed-Effects Mode

Multiple Comparison Procedures

Factorial Anova: Fixed-Effects Mode

One Factor Fixed-Effects Ancova With Single Covariate

Random- and Mixed-Effects Analysis of Variance Models

Hierarchical and Randomized Block Analysis of Variance Models

Multiple Linear Regression

Logistic Regression

Mediation and Moderation

Filters