Question 1

An r-squared value of .50 means that the relationship is positive.

Accepted Answer

A) True 
 B)False  False

Question 2

If the correlation value for Pearson's r is positive, an asymmetric version of the test has been used.

Accepted Answer

A) True 
 B)False  False

Question 3

[Instructor note: If you are creating an exam with all measures of association, then use this.] Outliers (extreme scores) will most affect:

Accepted Answer

A)  The Uncertainty Coefficient (U) 
B)  Gamma 
C)  Pearson's r 
D)  Lambda 
A)  The Uncertainty Coefficient (U) 
B)  Gamma 
C)  Pearson's r 
D)  Lambda C

Question 4

An r-squared value of .40 means that the independent variable has accounted for 16% of the variance in the dependent variable.

Accepted Answer

A) True 
 B)False

Question 5

In order to assess the results of a prediction using Pearson's r, you would need

Accepted Answer

A)  both the correlation coefficient and the regression coefficient 
B)  both b and Beta 
C)  only r 
D)  none of the above 
A)  both the correlation coefficient and the regression coefficient 
B)  both b and Beta 
C)  only r 
D)  none of the above

Question 6

Pearson's r will provide both symmetric and asymmetric relationship values, but you have to know which one to use.

Accepted Answer

A) True 
 B)False

Question 7

The most important advantage of using Pearson's r is that

Accepted Answer

A)  it can be interpreted as a direct-PRE measure 
B)  it is the only statistic that has multivariate capabilities 
C)  the measure itself has very few assumptions about data 
D)  it is sensitive to the magnitude of change between pairs of scores 
A)  it can be interpreted as a direct-PRE measure 
B)  it is the only statistic that has multivariate capabilities 
C)  the measure itself has very few assumptions about data 
D)  it is sensitive to the magnitude of change between pairs of scores

Question 8

An alternative to regression, when assumptions are severely violated, is the Contingency Coefficient statistic.

Accepted Answer

A) True 
 B)False

Question 9

Pearson's r is designed for data measured at the

Accepted Answer

A)  nominal level 
B)  ordinal level 
C)  interval level 
D)  metric ordinal level 
A)  nominal level 
B)  ordinal level 
C)  interval level 
D)  metric ordinal level

Question 10

The correlation value for Pearson's r does not reach 1.0 (unity) but will after you square it.

Accepted Answer

A) True 
 B)False

Question 11

The linear function, Y = a + bx, is used to estimate the actual amount of effect variable X will have on variable Y

Accepted Answer

A)  under the condition that X = 2Y 
B)  under the condition that Y = 2X 
C)  for each unit increase in Y 
D)  for each unit increase in X 
A)  under the condition that X = 2Y 
B)  under the condition that Y = 2X 
C)  for each unit increase in Y 
D)  for each unit increase in X

Question 12

If you wanted to know the amount of change in Y as a produce of X, you would be interested in

Accepted Answer

A)  the correlation coefficient or r 
B)  the coefficient of nondetermination or c 
C)  the standardized coefficient or s 
D)  the regression coefficient or b 
A)  the correlation coefficient or r 
B)  the coefficient of nondetermination or c 
C)  the standardized coefficient or s 
D)  the regression coefficient or b

Question 13

The type of relationship measured by Pearson's r is

Accepted Answer

A)  exponential 
B)  uniform 
C)  linear 
D)  curvilinear 
A)  exponential 
B)  uniform 
C)  linear 
D)  curvilinear

Question 14

Pearson's r is reasonably able to withstand the effect of violating one of its assumptions.

Accepted Answer

A) True 
 B)False

Question 15

As a member of the General Linear Model, Pearson's r can be calculated from ANOVA.

Accepted Answer

A) True 
 B)False

Question 16

Given the formula Y = a + bX, if the Pearson's r value is .60 the value of b would have to be:

Accepted Answer

A)  half of .60 or .30 
B)  60 
C)  .36 
D)  none of the above 
A)  half of .60 or .30 
B)  60 
C)  .36 
D)  none of the above

Question 17

It is possible to have both a high correlation coefficient and a low regression coefficient.

Accepted Answer

A) True 
 B)False

Question 18

Pearson's r can easily be extended to a multivariate statistical technique

Accepted Answer

A)  with multiple independent variables 
B)  with multiple dependent variables 
C)  with the restriction that only one control variable can be used 
D)  all of the above 
A)  with multiple independent variables 
B)  with multiple dependent variables 
C)  with the restriction that only one control variable can be used 
D)  all of the above

Question 19

Assumptions of Pearson's r include which of the following?

Accepted Answer

A)  normally distributed data 
B)  homoscedasticity 
C)  the residual variance in uncorrelated with the independent variable 
D)  all of the above 
A)  normally distributed data 
B)  homoscedasticity 
C)  the residual variance in uncorrelated with the independent variable 
D)  all of the above

Question 20

If you calculated a Pearson's r-square of .36 between median incomes and city crime rates it means that:

Accepted Answer

A)  36 percent of the variance in city crime rates is explained 
B)  60 percent of the variance in city crime rates is explained 
C)  6 percent of the variance in median incomes is explained 
D)  13 percent of the variance in either median incomes or city crime rates is explained 
A)  36 percent of the variance in city crime rates is explained 
B)  60 percent of the variance in city crime rates is explained 
C)  6 percent of the variance in median incomes is explained 
D)  13 percent of the variance in either median incomes or city crime rates is explained

An r-squared value of .50 means that the relationship is positive.

If the correlation value for Pearson's r is positive, an asymmetric version of the test has been used.

[Instructor note: If you are creating an exam with all measures of association, then use this.] Outliers (extreme scores) will most affect:

An r-squared value of .40 means that the independent variable has accounted for 16% of the variance in the dependent variable.

In order to assess the results of a prediction using Pearson's r, you would need

Pearson's r will provide both symmetric and asymmetric relationship values, but you have to know which one to use.

The most important advantage of using Pearson's r is that

An alternative to regression, when assumptions are severely violated, is the Contingency Coefficient statistic.

Pearson's r is designed for data measured at the

The correlation value for Pearson's r does not reach 1.0 (unity) but will after you square it.

The linear function, Y = a + bx, is used to estimate the actual amount of effect variable X will have on variable Y

If you wanted to know the amount of change in Y as a produce of X, you would be interested in

The type of relationship measured by Pearson's r is

Pearson's r is reasonably able to withstand the effect of violating one of its assumptions.

As a member of the General Linear Model, Pearson's r can be calculated from ANOVA.

Given the formula Y = a + bX, if the Pearson's r value is .60 the value of b would have to be:

It is possible to have both a high correlation coefficient and a low regression coefficient.

Pearson's r can easily be extended to a multivariate statistical technique

Assumptions of Pearson's r include which of the following?

If you calculated a Pearson's r-square of .36 between median incomes and city crime rates it means that:

Introduction to Statistics

Levels of Measurement

Graphical Statistics

Measures of Central Tendency

Measures of Dispersion

Curves and Distributions

Frequency Distributions

Elementary Relationships: Crosstabulation Tables

Hypotheses and Sampling Distributions

Statistical Significance

Testing for Significance: the Chi-Square Test

Testing for Significance in Two Groups: The T-Test

Testing for Significance in Multiple Groups: The Analysis of Variance Statistic

The Concept of Association

Testing for Association: Phi

Doing Real Research: Elementary Multivariate Relationships

Statistics and Data Analysis

Filters

Exam 16: Testing for Association: Pearsons R and Regression

An r-squared value of .50 means that the relationship is positive.

If the correlation value for Pearson's r is positive, an asymmetric version of the test has been used.

[Instructor note: If you are creating an exam with all measures of association, then use this.] Outliers (extreme scores) will most affect:

An r-squared value of .40 means that the independent variable has accounted for 16% of the variance in the dependent variable.

In order to assess the results of a prediction using Pearson's r, you would need

Pearson's r will provide both symmetric and asymmetric relationship values, but you have to know which one to use.

The most important advantage of using Pearson's r is that

An alternative to regression, when assumptions are severely violated, is the Contingency Coefficient statistic.

Pearson's r is designed for data measured at the

The correlation value for Pearson's r does not reach 1.0 (unity) but will after you square it.

The linear function, Y = a + bx, is used to estimate the actual amount of effect variable X will have on variable Y

If you wanted to know the amount of change in Y as a produce of X, you would be interested in

The type of relationship measured by Pearson's r is

Pearson's r is reasonably able to withstand the effect of violating one of its assumptions.

As a member of the General Linear Model, Pearson's r can be calculated from ANOVA.

Given the formula Y = a + bX, if the Pearson's r value is .60 the value of b would have to be:

It is possible to have both a high correlation coefficient and a low regression coefficient.

Pearson's r can easily be extended to a multivariate statistical technique

Assumptions of Pearson's r include which of the following?

If you calculated a Pearson's r-square of .36 between median incomes and city crime rates it means that:

Introduction to Statistics

Levels of Measurement

Graphical Statistics

Measures of Central Tendency

Measures of Dispersion

Curves and Distributions

Frequency Distributions

Elementary Relationships: Crosstabulation Tables

Hypotheses and Sampling Distributions

Statistical Significance

Testing for Significance: the Chi-Square Test

Testing for Significance in Two Groups: The T-Test

Testing for Significance in Multiple Groups: The Analysis of Variance Statistic

The Concept of Association

Testing for Association: Phi

Doing Real Research: Elementary Multivariate Relationships

Statistics and Data Analysis

Filters