Question 1

R2 varies from 1 to 2.

Accepted Answer

A) True 
 B)False  False

Question 2

The Pearson's correlation coefficient, r, always has the same sign as b.

Accepted Answer

A) True 
 B)False  True

Question 3

The dependent variable is also called the error term.

Accepted Answer

A) True 
 B)False  False

Question 4

The Spearman rank order correlation coefficient tests whether the rank orders of responses of two variables are statistically associated.

Accepted Answer

A) True 
 B)False

Question 5

The Spearman rank order correlation coefficient is appropriate for nominal-, ordinal-, and continuous-level variables.

Accepted Answer

A) True 
 B)False

Question 6

R = r and |R| = |r|.

Accepted Answer

A) True 
 B)False

Question 7

A scatterplot is a plot of the data points of two continuous variables.

Accepted Answer

A) True 
 B)False

Question 8

A regression line assumes a linear relationship that is constant over the range of observations.

Accepted Answer

A) True 
 B)False

Question 9

A perfect fit is indicated when the coefficient of determination is zero.

Accepted Answer

A) True 
 B)False

Question 10

The slope indicates the steepness of the regression line.

Accepted Answer

A) True 
 B)False

Question 11

The null hypothesis in regression is that the intercept is zero.

Accepted Answer

A) True 
 B)False

Question 12

The statistical significance of regression slopes is indeterminable.

Accepted Answer

A) True 
 B)False

Question 13

Pearson's correlation coefficient, r, measures the association (significance, direction, and strength) between two continuous variables.

Accepted Answer

A) True 
 B)False

Question 14

The standard error of the estimate is a measure of the spread of the y values around the regression line as calculated for the mean value of the independent variable, only, and assuming a large sample.

Accepted Answer

A) True 
 B)False

Question 15

R2 is the percent variation of the dependent variable explained by the independent variable(s).

Accepted Answer

A) True 
 B)False

Question 16

The slope of the regression line is also called the regression coefficient.

Accepted Answer

A) True 
 B)False

Question 17

A negative slope indicates an upward sloping line.

Accepted Answer

A) True 
 B)False

Question 18

Simple regression is appropriate for examining the bivariate relationships between two continuous variables.

Accepted Answer

A) True 
 B)False

R² varies from 1 to 2.

The Pearson's correlation coefficient, r, always has the same sign as b.

The dependent variable is also called the error term.

The Spearman rank order correlation coefficient tests whether the rank orders of responses of two variables are statistically associated.

The Spearman rank order correlation coefficient is appropriate for nominal-, ordinal-, and continuous-level variables.

R = r and |R| = |r|.

A scatterplot is a plot of the data points of two continuous variables.

A regression line assumes a linear relationship that is constant over the range of observations.

A perfect fit is indicated when the coefficient of determination is zero.

The slope indicates the steepness of the regression line.

The null hypothesis in regression is that the intercept is zero.

The statistical significance of regression slopes is indeterminable.

Pearson's correlation coefficient, r, measures the association (significance, direction, and strength) between two continuous variables.

The standard error of the estimate is a measure of the spread of the y values around the regression line as calculated for the mean value of the independent variable, only, and assuming a large sample.

R² is the percent variation of the dependent variable explained by the independent variable(s).

The slope of the regression line is also called the regression coefficient.

A negative slope indicates an upward sloping line.

Simple regression is appropriate for examining the bivariate relationships between two continuous variables.

Why Statistics for Public Managers and Policy Analysts

Research Design

Conceptualization and Measurement

Measuring and Managing Performance: Present and Future

Data Collection

Central Tendency

Measures of Dispersion

Contingency Tables

Getting Results

Introducing Inference: Estimation From Samples

Hypothesis Testing With Chi-Square

The T-Test

Analysis of Variance Anova

Multiple Regression

Logistic and Time Series Regression

Survey of Other Techniques

Filters

Exam 14: Simple Regression

R2 varies from 1 to 2.

The Pearson's correlation coefficient, r, always has the same sign as b.

The dependent variable is also called the error term.

The Spearman rank order correlation coefficient tests whether the rank orders of responses of two variables are statistically associated.

The Spearman rank order correlation coefficient is appropriate for nominal-, ordinal-, and continuous-level variables.

R = r and |R| = |r|.

A scatterplot is a plot of the data points of two continuous variables.

A regression line assumes a linear relationship that is constant over the range of observations.

A perfect fit is indicated when the coefficient of determination is zero.

The slope indicates the steepness of the regression line.

The null hypothesis in regression is that the intercept is zero.

The statistical significance of regression slopes is indeterminable.

Pearson's correlation coefficient, r, measures the association (significance, direction, and strength) between two continuous variables.

The standard error of the estimate is a measure of the spread of the y values around the regression line as calculated for the mean value of the independent variable, only, and assuming a large sample.

R2 is the percent variation of the dependent variable explained by the independent variable(s).

The slope of the regression line is also called the regression coefficient.

A negative slope indicates an upward sloping line.

Simple regression is appropriate for examining the bivariate relationships between two continuous variables.

Why Statistics for Public Managers and Policy Analysts

Research Design

Conceptualization and Measurement

Measuring and Managing Performance: Present and Future

Data Collection

Central Tendency

Measures of Dispersion

Contingency Tables

Getting Results

Introducing Inference: Estimation From Samples

Hypothesis Testing With Chi-Square

The T-Test

Analysis of Variance Anova

Multiple Regression

Logistic and Time Series Regression

Survey of Other Techniques

Filters

R² varies from 1 to 2.

R² is the percent variation of the dependent variable explained by the independent variable(s).