Question 1

If the correlation between smoking and lung cancer is .50, smoking accounts for 50% of the variability in lung cancer.

Accepted Answer

A) True 
 B)False

Question 2

In the equation Ŷ  = 12.6 X + 5

Accepted Answer

A)  a difference of one unit in X will lead to a 5 point difference in the prediction. 
B)  Ŷ will decrease as X increases. 
C)  the correlation is certain to be significant. 
D)  a difference of one unit in X will lead to a 12.6 point difference in the prediction. 
A)  a difference of one unit in X will lead to a 5 point difference in the prediction. 
B)  Ŷ will decrease as X increases. 
C)  the correlation is certain to be significant. 
D)  a difference of one unit in X will lead to a 12.6 point difference in the prediction.

Question 3

An important thing about r 2 is that it represents a measure of

Accepted Answer

A)  causal relationships. 
B)  accountable variability. 
C)  the correlation. 
D)  statistical significance. 
A)  causal relationships. 
B)  accountable variability. 
C)  the correlation. 
D)  statistical significance.

Question 4

The symbols a and b are frequently referred to as

Accepted Answer

A)  regression coefficients. 
B)  constants. 
C)  slopes. 
D)  regression correlations. 
A)  regression coefficients. 
B)  constants. 
C)  slopes. 
D)  regression correlations.

Question 5

If the correlation between X and Y is negative, the slope of the regression equation must be

Accepted Answer

A)  negative. 
B)  positive. 
C)  non-significant. 
D)  It could be either a or b. 
A)  negative. 
B)  positive. 
C)  non-significant. 
D)  It could be either a or b.

Question 6

The "best fitting line" is that regression line that

Accepted Answer

A)  minimizes the errors of prediction. 
B)  minimizes each squared error of prediction. 
C)  minimizes the sum of squared errors of prediction. 
D)  hits the most points as it goes through the scatterplot. 
A)  minimizes the errors of prediction. 
B)  minimizes each squared error of prediction. 
C)  minimizes the sum of squared errors of prediction. 
D)  hits the most points as it goes through the scatterplot.

Question 7

A regression analysis of hours spent exercising and ounces of weight loss had a slope of 3. We would predict that

Accepted Answer

A)  for every 1 hour of exercise, a person would lose 3 ounces of weight. 
B)  for every ounce lost, a person has to exercise for 7 hours. 
C)  every hour of exercise would have no effect on weight. 
D)  The slope cannot be used to make predictions, you need the intercept. 
A)  for every 1 hour of exercise, a person would lose 3 ounces of weight. 
B)  for every ounce lost, a person has to exercise for 7 hours. 
C)  every hour of exercise would have no effect on weight. 
D)  The slope cannot be used to make predictions, you need the intercept.

Question 8

We can think of the standard error of estimate as

Accepted Answer

A)  the standard deviation of the errors that we make when using the regression equation. 
B)  the standard deviation of Y. 
C)  the variance of the errors that we would make when using the regression equation. 
D)  the variance of X . 
A)  the standard deviation of the errors that we make when using the regression equation. 
B)  the standard deviation of Y. 
C)  the variance of the errors that we would make when using the regression equation. 
D)  the variance of X .

Question 9

In a regression using standardized data, to predict health symptoms from stress, the beta = .5. This means that for every 1 point increase in stress there is half a point increase in symptoms.

Accepted Answer

A) True 
 B)False

Question 10

The standard error of estimate is given by

Accepted Answer

A)    
B)    
C)    
D)  none of the above 
A)    
B)    
C)    
D)  none of the above

Question 11

When we think in terms of standardized data, the slope represents

Accepted Answer

A)  the change in X for a one unit change in Y . 
B)  the number of standard deviations Ŷ will differ for a one standard deviation difference in X . 
C)  the height of the regression line. 
D)  0. 
A)  the change in X for a one unit change in Y . 
B)  the number of standard deviations Ŷ will differ for a one standard deviation difference in X . 
C)  the height of the regression line. 
D)  0.

Question 12

The notation Ŷ is   used instead of Y

Accepted Answer

A)  to indicate that the answer is only approximate. 
B)  to indicate that we have an equation for a straight line. 
C)  to indicate that the result is a prediction. 
D)  because this is a mathematical equation. 
A)  to indicate that the answer is only approximate. 
B)  to indicate that we have an equation for a straight line. 
C)  to indicate that the result is a prediction. 
D)  because this is a mathematical equation.

Question 13

The standard error of estimate is denoted by

Accepted Answer

A)    
B)    
C)    
D)  none of the above 
A)    
B)    
C)    
D)  none of the above

Question 14

Regression can be used to examine both linear and curvilinear relationships.

Accepted Answer

A) True 
 B)False

Question 15

A regression line is

Accepted Answer

A)  a line of covariance. 
B)  a correlation matrix. 
C)  the best fit straight line. 
D)  the equal to a correlation line. 
A)  a line of covariance. 
B)  a correlation matrix. 
C)  the best fit straight line. 
D)  the equal to a correlation line.

Question 16

If we do not know X , our measure of error in predicting Y is

Accepted Answer

A)  the standard deviation of Y. 
B)  the standard deviation of X. 
C)  the standard error of estimate. 
D)  the standardized residual. 
A)  the standard deviation of Y. 
B)  the standard deviation of X. 
C)  the standard error of estimate. 
D)  the standardized residual.

Question 17

Suppose that you sell ice cream from a cart on the street. After you pay the ice cream supplier, the regression line that predicts your ice cream profits from the number of hours you work has a slope of 15. But the man who owns the cart charges you $5 per hour in rent. How much money will you earn per hour?

Accepted Answer

A)  $15 
B)  $10 
C)  $5 
D)  nothing 
A)  $15 
B)  $10 
C)  $5 
D)  nothing

Question 18

The notation SS stands for

Accepted Answer

A)  simply sensational. 
B)  statistical significance. 
C)  squared sums. 
D)  sum of squares. 
A)  simply sensational. 
B)  statistical significance. 
C)  squared sums. 
D)  sum of squares.

Question 19

When we have standardized data, the slope will be denoted as

Accepted Answer

A)  b 
B)    
C)  1.0 
D)  r 
A)  b 
B)    
C)  1.0 
D)  r

Question 20

Given this regression equation,  Ŷ  = .3 X + 25, estimate the values of X given the following values of Y. 
a. Y = 0 
b. Y = 25 
c. Y = -30

Accepted Answer

a. 83.33

If the correlation between smoking and lung cancer is .50, smoking accounts for 50% of the variability in lung cancer.

In the equation Ŷ = 12.6 X + 5

An important thing about r ² is that it represents a measure of

The symbols a and b are frequently referred to as

If the correlation between X and Y is negative, the slope of the regression equation must be

The "best fitting line" is that regression line that

A regression analysis of hours spent exercising and ounces of weight loss had a slope of 3. We would predict that

We can think of the standard error of estimate as

In a regression using standardized data, to predict health symptoms from stress, the beta = .5. This means that for every 1 point increase in stress there is half a point increase in symptoms.

The standard error of estimate is given by

When we think in terms of standardized data, the slope represents

The notation Ŷ is used instead of Y

The standard error of estimate is denoted by

Regression can be used to examine both linear and curvilinear relationships.

A regression line is

If we do not know X , our measure of error in predicting Y is

The notation SS stands for

When we have standardized data, the slope will be denoted as

Given this regression equation, Ŷ = .3 X + 25, estimate the values of X given the following values of Y. a. Y = 0 b. Y = 25 c. Y = -30

Introduction

Basic Concepts

Displaying Data

Measures of Central Tendency

Measures of Variability

The Normal Distribution

Basic Concepts of Probability

Sampling Distributions and Hypothesis Testing

Correlation

Multiple Regression

Hypothesis Tests Applied to Means: One Sample

Hypothesis Tests Applied to Means: Two Related Samples

Hypothesis Tests Applied to Means: Two Independent Samples

Power

One-Way Analysis of Variance

Factorial Analysis of Variance

Repeated-Measures Analysis of Variance

Chi-Square

Nonparametric and Resampling Statistical Tests

Meta-Analysis

Filters

Exam 10: Regression

If the correlation between smoking and lung cancer is .50, smoking accounts for 50% of the variability in lung cancer.

In the equation Ŷ = 12.6 X + 5

An important thing about r 2 is that it represents a measure of

The symbols a and b are frequently referred to as

If the correlation between X and Y is negative, the slope of the regression equation must be

The "best fitting line" is that regression line that

A regression analysis of hours spent exercising and ounces of weight loss had a slope of 3. We would predict that

We can think of the standard error of estimate as

In a regression using standardized data, to predict health symptoms from stress, the beta = .5. This means that for every 1 point increase in stress there is half a point increase in symptoms.

The standard error of estimate is given by

When we think in terms of standardized data, the slope represents

The notation Ŷ is used instead of Y

The standard error of estimate is denoted by

Regression can be used to examine both linear and curvilinear relationships.

A regression line is

If we do not know X , our measure of error in predicting Y is

The notation SS stands for

When we have standardized data, the slope will be denoted as

Given this regression equation, Ŷ = .3 X + 25, estimate the values of X given the following values of Y. a. Y = 0 b. Y = 25 c. Y = -30

Introduction

Basic Concepts

Displaying Data

Measures of Central Tendency

Measures of Variability

The Normal Distribution

Basic Concepts of Probability

Sampling Distributions and Hypothesis Testing

Correlation

Multiple Regression

Hypothesis Tests Applied to Means: One Sample

Hypothesis Tests Applied to Means: Two Related Samples

Hypothesis Tests Applied to Means: Two Independent Samples

Power

One-Way Analysis of Variance

Factorial Analysis of Variance

Repeated-Measures Analysis of Variance

Chi-Square

Nonparametric and Resampling Statistical Tests

Meta-Analysis

Filters

An important thing about r ² is that it represents a measure of