Question 1

In linear regression, the sum of the differences between the actual Y scores and the predicted Y ' scores is.

Accepted Answer

A) normally distributed around the mean of Y 
B) always equal to zero 
C) the numerator of the slope of the regression equation 
D) equal to the variance of the Y variable 
A) normally distributed around the mean of Y 
B) always equal to zero 
C) the numerator of the slope of the regression equation 
D) equal to the variance of the Y variable

Question 2

The typical amount of error in predicting from the linear regression line is represented by the.

Accepted Answer

A) standard deviation for the Y variable 
B) coefficient of determination 
C) standard error of estimate 
D) slope of the regression equation 
A) standard deviation for the Y variable 
B) coefficient of determination 
C) standard error of estimate 
D) slope of the regression equation

Question 3

If Y ' = 2.2X - 18, then Y ' for X = 90 is.

Accepted Answer

A) +198.0 
B) -180.0 
C) +180.0 
D) +216.0 
A) +198.0 
B) -180.0 
C) +180.0 
D) +216.0

Question 4

A regression equation describes the fitted to the scatterplot of scores.

Accepted Answer

A) coefficient of determination 
B) straight line 
C) slope of the correlation coefficient 
D) residuals 
A) coefficient of determination 
B) straight line 
C) slope of the correlation coefficient 
D) residuals

Question 5

A linear relationship requires that.

Accepted Answer

A) a change of one unit in variable X be accompanied by a constant amount of change in variable Y 
B) when X equals zero, then Y must equal zero 
C) the population distributions of X and Y are normal 
D) a change of one unit in variable X be accompanied by a varying amount of change in variable Y 
A) a change of one unit in variable X be accompanied by a constant amount of change in variable Y 
B) when X equals zero, then Y must equal zero 
C) the population distributions of X and Y are normal 
D) a change of one unit in variable X be accompanied by a varying amount of change in variable Y

Question 6

The general equation for a straight line is expressed as.

Accepted Answer

A) Y = bX - a 
B) Y = bX + a 
C) Y = X(b + a) 
D) Y = X(b - a) 
A) Y = bX - a 
B) Y = bX + a 
C) Y = X(b + a) 
D) Y = X(b - a)

Question 7

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

Accepted Answer

A) r = zero. 
B) r = +.30. 
C) r = -.70. 
D) r = +.60. 
A) r = zero. 
B) r = +.30. 
C) r = -.70. 
D) r = +.60.

Question 8

In the equation Y = +1.3X - 7, the Y-intercept is.

Accepted Answer

A) +1.3 
B) -7.0 
C) +7.0 
D) the value of Y when X = 1 
A) +1.3 
B) -7.0 
C) +7.0 
D) the value of Y when X = 1

Question 9

If SSResidual = 490 and Npairs = 12, then sY . X

Accepted Answer

A) 40.8 
B) 7.0 
C) 49.0 
D) -40.8 
A) 40.8 
B) 7.0 
C) 49.0 
D) -40.8

Question 10

In the equation Y = -0.4X + 8, the slope is.

Accepted Answer

A) -0.4 
B) +8.0 
C) +0.4 
D) -8.0 
A) -0.4 
B) +8.0 
C) +0.4 
D) -8.0

Question 11

The $\Sigma \left( Y - Y ^ { \prime } \right) ^ { 2 }$

Accepted Answer

A) SSResidual 
B) SSCorrelation 
C) SSY 
D) SSError 
A) SSResidual 
B) SSCorrelation 
C) SSY 
D) SSError

Question 12

In a least-squares regression line, b is defined as the ratio of CPXY to.

Accepted Answer

A) SSY 
B) SSX 
C) SSX + SSY 
D) (SSX)(SSY) 
A) SSY 
B) SSX 
C) SSX + SSY 
D) (SSX)(SSY)

Question 13

If r for variables X and Y = zero, then the.

Accepted Answer

A) best prediction of Y is the mean of Y 
B) regression equation will predict Y exactly 
C) mean of X will be higher than the mean of Y 
D) Y-intercept is equal to 1.0 
A) best prediction of Y is the mean of Y 
B) regression equation will predict Y exactly 
C) mean of X will be higher than the mean of Y 
D) Y-intercept is equal to 1.0

Question 14

The formula (Y2 - Y1) ÷ (X2 - X1) expresses the.

Accepted Answer

A) equation of a straight line 
B) linear relationship of X and Y 
C) slope of a straight line 
D) Y-intercept of a straight line 
A) equation of a straight line 
B) linear relationship of X and Y 
C) slope of a straight line 
D) Y-intercept of a straight line

Question 15

In linear regression the difference between a value of Y and Y ' is known as the.

Accepted Answer

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

Question 16

Y ' represents the.

Accepted Answer

A) slope of a straight line 
B) value of Y predicted from a value of X 
C) value of the Y score obtained by a subject 
D) Y-intercept 
A) slope of a straight line 
B) value of Y predicted from a value of X 
C) value of the Y score obtained by a subject 
D) Y-intercept

Question 17

If Y = +12 when X = 0, then the slope of the straight line relating X and Y.

Accepted Answer

A) equals +12 
B) equals -12 
C) equals zero 
D) cannot be determined from the 
A) equals +12 
B) equals -12 
C) equals zero 
D) cannot be determined from the

Question 18

The value of sY . X will be equal to sY
When.

Accepted Answer

A) r for variables X and Y = zero 
B) r for variables X and Y = plus or minus 1.00 
C) the mean of X is equal to the mean of Y 
D) the standard deviation of X is equal to the standard deviation of Y 
A) r for variables X and Y = zero 
B) r for variables X and Y = plus or minus 1.00 
C) the mean of X is equal to the mean of Y 
D) the standard deviation of X is equal to the standard deviation of Y

Question 19

A straight line fitted through the points in a scatterplot that minimizes Σ(Y - Y') 2 is called a line.

Accepted Answer

A) correlation 
B) residual 
C) linear 
D) least-squares regression 
A) correlation 
B) residual 
C) linear 
D) least-squares regression

Question 20

The standard error of estimate in linear regression will be zero when.

Accepted Answer

A) r = zero 
B) r = -1.00 or +1.00 
C) the slope of the regression line is 0.00 
D) the slope of the regression line is 1.00 
A) r = zero 
B) r = -1.00 or +1.00 
C) the slope of the regression line is 0.00 
D) the slope of the regression line is 1.00

In linear regression, the sum of the differences between the actual Y scores and the predicted Y ' scores is.

The typical amount of error in predicting from the linear regression line is represented by the.

If Y ' = 2.2X - 18, then Y ' for X = 90 is.

A regression equation describes the fitted to the scatterplot of scores.

A linear relationship requires that.

The general equation for a straight line is expressed as.

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

In the equation Y = +1.3X - 7, the Y-intercept is.

If SSResidual = 490 and Npairs = 12, then sY . X

In the equation Y = -0.4X + 8, the slope is.

The $\Sigma \left( Y - Y ^ { \prime } \right) ^ { 2 }$

In a least-squares regression line, b is defined as the ratio of CPXY to.

If r for variables X and Y = zero, then the.

The formula (Y2 - Y1) ÷ (X2 - X1) expresses the.

In linear regression the difference between a value of Y and Y ' is known as the.

Y ' represents the.

If Y = +12 when X = 0, then the slope of the straight line relating X and Y.

The value of sY . X will be equal to sY When.

A straight line fitted through the points in a scatterplot that minimizes Σ(Y - Y') 2 is called a line.

The standard error of estimate in linear regression will be zero when.

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 linear regression, the sum of the differences between the actual Y scores and the predicted Y ' scores is.

The typical amount of error in predicting from the linear regression line is represented by the.

If Y ' = 2.2X - 18, then Y ' for X = 90 is.

A regression equation describes the fitted to the scatterplot of scores.

A linear relationship requires that.

The general equation for a straight line is expressed as.

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

In the equation Y = +1.3X - 7, the Y-intercept is.

If SSResidual = 490 and Npairs = 12, then sY . X

In the equation Y = -0.4X + 8, the slope is.

The Σ(Y−Y′)2\Sigma \left( Y - Y ^ { \prime } \right) ^ { 2 }Σ(Y−Y′)2

In a least-squares regression line, b is defined as the ratio of CPXY to.

If r for variables X and Y = zero, then the.

The formula (Y2 - Y1) ÷ (X2 - X1) expresses the.

In linear regression the difference between a value of Y and Y ' is known as the.

Y ' represents the.

If Y = +12 when X = 0, then the slope of the straight line relating X and Y.

The value of sY . X will be equal to sY When.

A straight line fitted through the points in a scatterplot that minimizes Σ(Y - Y') 2 is called a line.

The standard error of estimate in linear regression will be zero when.

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

The $\Sigma \left( Y - Y ^ { \prime } \right) ^ { 2 }$