Question 1

A linear regression line is assumed to.

Accepted Answer

A) curve slightly upwards at the highest value of X 
B) be a straight line relation throughout the range of X values 
C) curve slightly downwards at the lowest value of X 
D) assume a negative slope near zero if the correlation coefficient is negative 
A) curve slightly upwards at the highest value of X 
B) be a straight line relation throughout the range of X values 
C) curve slightly downwards at the lowest value of X 
D) assume a negative slope near zero if the correlation coefficient is negative

Question 2

When predicting a participant's Y score from a linear regression line, the error in that prediction is.

Accepted Answer

A) how much the actual score, Y, differs from the mean of the scores, Y 
B) the difference between the predicted score, Y ', and the X score used to make the prediction 
C) the difference between the predicted score, Y ', and the mean of the predicted scores, Y ' 
D) how much the actual Y score differs from the predicted Y ' score 
A) how much the actual score, Y, differs from the mean of the scores, Y 
B) the difference between the predicted score, Y ', and the X score used to make the prediction 
C) the difference between the predicted score, Y ', and the mean of the predicted scores, Y ' 
D) how much the actual Y score differs from the predicted Y ' score

Question 3

The amount of change in a Y variable that accompanies a given amount of change in an X variable is the .

Accepted Answer

A) slope of a straight line 
B) Y-intercept of a straight line 
C) correlation between X and Y 
D) length of the prediction line 
A) slope of a straight line 
B) Y-intercept of a straight line 
C) correlation between X and Y 
D) length of the prediction line

Question 4

The slope for a least-squares regression line is defined as the correlation coefficient multiplied by the.

Accepted Answer

A) ratio of the estimated population standard deviations for Y and X 
B) ratio of the means for X and Y 
C) estimated population variance for X 
D) standard error of the estimate 
A) ratio of the estimated population standard deviations for Y and X 
B) ratio of the means for X and Y 
C) estimated population variance for X 
D) standard error of the estimate

Question 5

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

Accepted Answer

A) X; X 
B) X; Y 
C) Y; X 
D) Y; Y 
A) X; X 
B) X; Y 
C) Y; X 
D) Y; Y

Question 6

, then the Y-intercept of the least-squares regression line is     $$\text { If } \bar { Y } = 21 , \bar { X } = 6 \text {, and } b = + 1.5 \text {, }$$ .

Accepted Answer

A) +30.0 
B) -12.0 
C) +12.0 
D) -30.0 
A) +30.0 
B) -12.0 
C) +12.0 
D) -30.0

Question 7

Each time variable X increases by one unit, variable Y decreases by 1.3 units. 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 8

In the equation Y = +0.6X + 14, +0.6 is the and 14 is the.

Accepted Answer

A) Y-intercept; slope 
B) X-intercept; slope 
C) slope; Y-intercept 
D) slope; X-intercept 
A) Y-intercept; slope 
B) X-intercept; slope 
C) slope; Y-intercept 
D) slope; X-intercept

Question 9

If Y ' = -1.3X + 20, then Y ' for X = 50 is.

Accepted Answer

A) -70.0 
B) +70.0 
C) +45.0 
D) -45.0 
A) -70.0 
B) +70.0 
C) +45.0 
D) -45.0

Question 10

The standard error of estimate indicates the amount of error in predictions when using the to predict values of Y from X.

Accepted Answer

A) mean of X 
B) mean of Y 
C) linear regression line 
D) coefficient of determination 
A) mean of X 
B) mean of Y 
C) linear regression line 
D) coefficient of determination

Question 11

Values of Y predicted from values of X are symbolized by.

Accepted Answer

A) Ypred 
B) Y 
C) Yp 
D) Y ' 
A) Ypred 
B) Y 
C) Yp 
D) Y '

Question 12

The minimizes the sum of values. $\left( Y - Y ^ { \prime } \right) ^ { 2 }$

Accepted Answer

A) least-squares regression line 
B) coefficient of determination 
C) correlation line 
D) standard error of the estimate 
A) least-squares regression line 
B) coefficient of determination 
C) correlation line 
D) standard error of the estimate

Question 13

In the equation Y = -0.8X - 10, the value of Y will be when X = 4.

Accepted Answer

A) -10.0 
B) +6.8 
C) -13.2 
D) +13.2 
A) -10.0 
B) +6.8 
C) -13.2 
D) +13.2

Question 14

The expression Y = bX is true, if.

Accepted Answer

A) the slope is positive 
B) both X and Y variables represent interval measurement scales 
C) the correlation coefficient equals +1.0 
D) X = 0, then Y = 0 
A) the slope is positive 
B) both X and Y variables represent interval measurement scales 
C) the correlation coefficient equals +1.0 
D) X = 0, then Y = 0

Question 15

If Y ' = +0.5X + 8, then Y ' for X = 12 is.

Accepted Answer

A) +2.0 
B) +14.0 
C) -14.0 
D) +20.0 
A) +2.0 
B) +14.0 
C) -14.0 
D) +20.0

A linear regression line is assumed to.

When predicting a participant's Y score from a linear regression line, the error in that prediction is.

The amount of change in a Y variable that accompanies a given amount of change in an X variable is the .

The slope for a least-squares regression line is defined as the correlation coefficient multiplied by the.

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

Each time variable X increases by one unit, variable Y decreases by 1.3 units. Variables X and Y are related.

In the equation Y = +0.6X + 14, +0.6 is the and 14 is the.

If Y ' = -1.3X + 20, then Y ' for X = 50 is.

The standard error of estimate indicates the amount of error in predictions when using the to predict values of Y from X.

Values of Y predicted from values of X are symbolized by.

The minimizes the sum of values. $\left( Y - Y ^ { \prime } \right) ^ { 2 }$

In the equation Y = -0.8X - 10, the value of Y will be when X = 4.

The expression Y = bX is true, if.

If Y ' = +0.5X + 8, then Y ' for X = 12 is.

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

A linear regression line is assumed to.

When predicting a participant's Y score from a linear regression line, the error in that prediction is.

The amount of change in a Y variable that accompanies a given amount of change in an X variable is the .

The slope for a least-squares regression line is defined as the correlation coefficient multiplied by the.

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

, then the Y-intercept of the least-squares regression line is If Yˉ=21,Xˉ=6, and b=+1.5, \text { If } \bar { Y } = 21 , \bar { X } = 6 \text {, and } b = + 1.5 \text {, } If Yˉ=21,Xˉ=6, and b=+1.5, .

Each time variable X increases by one unit, variable Y decreases by 1.3 units. Variables X and Y are related.

In the equation Y = +0.6X + 14, +0.6 is the and 14 is the.

If Y ' = -1.3X + 20, then Y ' for X = 50 is.

The standard error of estimate indicates the amount of error in predictions when using the to predict values of Y from X.

Values of Y predicted from values of X are symbolized by.

The minimizes the sum of values. (Y−Y′)2\left( Y - Y ^ { \prime } \right) ^ { 2 }(Y−Y′)2

In the equation Y = -0.8X - 10, the value of Y will be when X = 4.

The expression Y = bX is true, if.

If Y ' = +0.5X + 8, then Y ' for X = 12 is.

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 minimizes the sum of values. $\left( Y - Y ^ { \prime } \right) ^ { 2 }$