Question 1

In the equation for a straight line, b represents the.

Accepted Answer

A) X-intercept 
B) Y-intercept 
C) degree of association between X and Y 
D) slope 
A) X-intercept 
B) Y-intercept 
C) degree of association between X and Y 
D) slope D

Question 2

The sum of the residuals when using a least-squares regression line is.

Accepted Answer

A) normally distributed around the mean of Y 
B) normally distributed around the mean of X 
C) equal to zero 
D) equal to the variance of the Y variable 
A) normally distributed around the mean of Y 
B) normally distributed around the mean of X 
C) equal to zero 
D) equal to the variance of the Y variable C

Question 3

In the equation for a straight line, the slope of the line is represented by the symbol.

Accepted Answer

A) Y 
B) b 
C) X 
D) a 
A) Y 
B) b 
C) X 
D) a B

Question 4

Each time variable X increases by one unit, variable Y increases by 0.7 of a unit. Variables X and Y are related.

Accepted Answer

A) nonlinearly 
B) not 
C) perfectly negatively 
D) perfectly positively 
A) nonlinearly 
B) not 
C) perfectly negatively 
D) perfectly positively

Question 5

The purpose of a linear regression line is to.

Accepted Answer

A) predict one set of scores from another set 
B) display the bivariate distribution of X and Y 
C) identify the mean of the X and Y variables 
D) calculate the correlation coefficient 
A) predict one set of scores from another set 
B) display the bivariate distribution of X and Y 
C) identify the mean of the X and Y variables 
D) calculate the correlation coefficient

Question 6

In the equation Y = -0.4X + 8, the value of Y will be when X = 2.

Accepted Answer

A) 7.2 
B) 0.0 
C) 8.0 
D) 8.8 
A) 7.2 
B) 0.0 
C) 8.0 
D) 8.8

Question 7

If Y = -.8X, then what is the value of Y when X = 10?

Accepted Answer

A) -8.0 
B) +8.0 
C) -0.8 
D) Not enough 
A) -8.0 
B) +8.0 
C) -0.8 
D) Not enough

Question 8

The equation of a straight line is Y = -1.4X + 5.0. From this equation we know that.

Accepted Answer

A) the line has a negative slope and intersects the X axis at +5.0 
B) the line has a slope of +5.0 and intersects the Y axis at -1.4 
C) the line has a slope of -1.4 and intersects the Y axis at +5.0 
D) X and Y are not linearly related 
A) the line has a negative slope and intersects the X axis at +5.0 
B) the line has a slope of +5.0 and intersects the Y axis at -1.4 
C) the line has a slope of -1.4 and intersects the Y axis at +5.0 
D) X and Y are not linearly related

Question 9

The value of sY . X will be equal to zero when the.

Accepted Answer

A) coefficient of determination is zero 
B) slope of the regression line is negative 
C) correlation coefficient for variables X and Y is 1.00 
D) SSResidual equals 1.00 
A) coefficient of determination is zero 
B) slope of the regression line is negative 
C) correlation coefficient for variables X and Y is 1.00 
D) SSResidual equals 1.00

Question 10

In the equation Y = +1.5X - 3, the value of Y will be when X = 1.

Accepted Answer

A) -1.5 
B) +1.5 
C) -3.0 
D) +3.5 
A) -1.5 
B) +1.5 
C) -3.0 
D) +3.5

Question 11

In a linear relationship,.

Accepted Answer

A) the X and Y variables are not related 
B) the X and Y variables are related with a curved line 
C) a change of one unit in variable X is accompanied by a constant amount of change in variable Y 
D) all the above 
A) the X and Y variables are not related 
B) the X and Y variables are related with a curved line 
C) a change of one unit in variable X is accompanied by a constant amount of change in variable Y 
D) all the above

Question 12

The difference between a predicted and an obtained value of Y when using a linear regression line is the.

Accepted Answer

A) residual 
B) statistical residue 
C) error of measurement 
D) error of estimate 
A) residual 
B) statistical residue 
C) error of measurement 
D) error of estimate

Question 13

The Y-intercept is the value of Y when the value of X is equal to.

Accepted Answer

A) its lowest score 
B) its highest score 
C) its mean 
D) zero 
A) its lowest score 
B) its highest score 
C) its mean 
D) zero

Question 14

The measure of accuracy of prediction in linear regression is provided by the.

Accepted Answer

A) standard deviation of Y ' 
B) standard deviation of Y - Y values 
C) standard error of estimate 
D) standard error of the mean 
A) standard deviation of Y ' 
B) standard deviation of Y - Y values 
C) standard error of estimate 
D) standard error of the mean

Question 15

If a change of one unit in variable X is accompanied by a constant change in variable Y, then X and Y are related.

Accepted Answer

A) not 
B) linearly 
C) nonlinearly 
D) correlationally 
A) not 
B) linearly 
C) nonlinearly 
D) correlationally

Question 16

The standard error of estimate is defined as the square root of the quantity divided by the.

Accepted Answer

A) SSY; Npairs - 2 
B) SSY; Npairs + 2 
C) SSResidual; Npairs - 1 
D) SSResidual; Npairs - 2 
A) SSY; Npairs - 2 
B) SSY; Npairs + 2 
C) SSResidual; Npairs - 1 
D) SSResidual; Npairs - 2

Question 17

The symbol .
SY ) X
In linear regression represents the standard error of for predicting

Accepted Answer

A) estimate; X from Y 
B) estimate; Y from X 
C) measurement; X from Y 
D) measurement; Y from X 
A) estimate; X from Y 
B) estimate; Y from X 
C) measurement; X from Y 
D) measurement; Y from X

Question 18

Assume that you are predicting X from Y. Which of the following correlation coefficients would yield predictions with the most error?

Accepted Answer

A) r = -.20. 
B) r = -.40. 
C) r = -1.00. 
D) r = +.10. 
A) r = -.20. 
B) r = -.40. 
C) r = -1.00. 
D) r = +.10.

Question 19

The slope of a straight line is defined as (Y2 - Y1) (X2 - X1).

Accepted Answer

A) minus 
B) divided by 
C) plus 
D) multiplied by 
A) minus 
B) divided by 
C) plus 
D) multiplied by

Question 20

The value of Y when X equals zero in the equation of a straight line is called.

Accepted Answer

A) the slope of the line 
B) b 
C) the X-intercept of the line 
D) the Y-intercept of the line 
A) the slope of the line 
B) b 
C) the X-intercept of the line 
D) the Y-intercept of the line

In the equation for a straight line, b represents the.

The sum of the residuals when using a least-squares regression line is.

In the equation for a straight line, the slope of the line is represented by the symbol.

Each time variable X increases by one unit, variable Y increases by 0.7 of a unit. Variables X and Y are related.

The purpose of a linear regression line is to.

In the equation Y = -0.4X + 8, the value of Y will be when X = 2.

If Y = -.8X, then what is the value of Y when X = 10?

The equation of a straight line is Y = -1.4X + 5.0. From this equation we know that.

The value of sY . X will be equal to zero when the.

In the equation Y = +1.5X - 3, the value of Y will be when X = 1.

In a linear relationship,.

The difference between a predicted and an obtained value of Y when using a linear regression line is the.

The Y-intercept is the value of Y when the value of X is equal to.

The measure of accuracy of prediction in linear regression is provided by the.

If a change of one unit in variable X is accompanied by a constant change in variable Y, then X and Y are related.

The standard error of estimate is defined as the square root of the quantity divided by the.

The symbol . SY ) X In linear regression represents the standard error of for predicting

Assume that you are predicting X from Y. Which of the following correlation coefficients would yield predictions with the most error?

The slope of a straight line is defined as (Y2 - Y1) (X2 - X1).

The value of Y when X equals zero in the equation of a straight line is called.

Making Sense of Variability: an Introduction to Statistics

Statistics in the Context of Scientific Research

Looking at Data: Frequency Distributions and Graphs

Looking at Data: Measures of Central Tendency

Looking at Data: Measures of Variability

The Normal Distribution, Probability, and Standard Scores

Understanding Data: Using Statistics for Inference and Estimation

Is There Really a Difference Introduction to Statistical Hypothesis Testing

The Basics of Experimentation and Testing for a Difference Between Means

One-Factor Between-Subjects Analysis of Variance

Two-Factor Between-Subjects Analysis of Variance

One-Factor Within-Subjects Analysis of Variance

Correlation: Understanding Covariation

Nonparametric Tests

Filters

Exam 14: Regression Analysis: Predicting Linear Relationships

In the equation for a straight line, b represents the.

The sum of the residuals when using a least-squares regression line is.

In the equation for a straight line, the slope of the line is represented by the symbol.

Each time variable X increases by one unit, variable Y increases by 0.7 of a unit. Variables X and Y are related.

The purpose of a linear regression line is to.

In the equation Y = -0.4X + 8, the value of Y will be when X = 2.

If Y = -.8X, then what is the value of Y when X = 10?

The equation of a straight line is Y = -1.4X + 5.0. From this equation we know that.

The value of sY . X will be equal to zero when the.

In the equation Y = +1.5X - 3, the value of Y will be when X = 1.

In a linear relationship,.

The difference between a predicted and an obtained value of Y when using a linear regression line is the.

The Y-intercept is the value of Y when the value of X is equal to.

The measure of accuracy of prediction in linear regression is provided by the.

If a change of one unit in variable X is accompanied by a constant change in variable Y, then X and Y are related.

The standard error of estimate is defined as the square root of the quantity divided by the.

The symbol . SY ) X In linear regression represents the standard error of for predicting

Assume that you are predicting X from Y. Which of the following correlation coefficients would yield predictions with the most error?

The slope of a straight line is defined as (Y2 - Y1) (X2 - X1).

The value of Y when X equals zero in the equation of a straight line is called.

Making Sense of Variability: an Introduction to Statistics

Statistics in the Context of Scientific Research

Looking at Data: Frequency Distributions and Graphs

Looking at Data: Measures of Central Tendency

Looking at Data: Measures of Variability

The Normal Distribution, Probability, and Standard Scores

Understanding Data: Using Statistics for Inference and Estimation

Is There Really a Difference Introduction to Statistical Hypothesis Testing

The Basics of Experimentation and Testing for a Difference Between Means

One-Factor Between-Subjects Analysis of Variance

Two-Factor Between-Subjects Analysis of Variance

One-Factor Within-Subjects Analysis of Variance

Correlation: Understanding Covariation

Nonparametric Tests

Filters