Question 1

Measures of association enable prediction. Explain whether prediction requires a causal relationship between the variables.

Accepted Answer

No-prediction does not require a causal

Question 2

Which of these statements describes the variables if the distribution of the scores of one variable changes across the categories of another variable?

Accepted Answer

A)  They are perfectly associated. 
B)  They are associated to some extent. 
C)  They have a cause-and-effect relationship. 
D)  They are related in a way that is statistically significant. 
A)  They are perfectly associated. 
B)  They are associated to some extent. 
C)  They have a cause-and-effect relationship. 
D)  They are related in a way that is statistically significant.

Question 3

When should analysts be very cautious about using lambda as a measure of proportional reduction in error?

Accepted Answer

A)  when both variables are measured at the nominal level 
B)  when the sample size is very large 
C)  when the bivariate table is larger than 2 × 2 
D)  when the row marginals are very unequal 
A)  when both variables are measured at the nominal level 
B)  when the sample size is very large 
C)  when the bivariate table is larger than 2 × 2 
D)  when the row marginals are very unequal

Measures of association enable prediction. Explain whether prediction requires a causal relationship between the variables.

Which of these statements describes the variables if the distribution of the scores of one variable changes across the categories of another variable?

When should analysts be very cautious about using lambda as a measure of proportional reduction in error?

Introduction

Basic Descriptive Statistics: Percentages, Ratios and Rates, Tables, Charts, and Graphs

Measures of Central Tendency and Dispersion

The Normal Curve

Introduction to Inferential Statistics: Sampling and the Sampling Distribution

Estimation Procedures for Sample Means and Proportions

Hypothesis Testing With Nominal and Ordinal Variables: CHI Square

Measures of Association for Variables Measured at the Ordinal Level

Hypothesis Testing With Means and Proportions: The One-Sample Case

Hypothesis Testing With Means and Proportions: The Two-Sample Case

Hypothesis Testing With More Than Two Means: One-Way Analysis of Variance

Hypothesis Testing and Measures of Association for Variables Measured at the Interval-Ratio Level

Partial Correlation and Multiple Regression and Correlation

Filters

Exam 8: Measures of Association for Variables Measured at the Nominal Level

Measures of association enable prediction. Explain whether prediction requires a causal relationship between the variables.

Which of these statements describes the variables if the distribution of the scores of one variable changes across the categories of another variable?

When should analysts be very cautious about using lambda as a measure of proportional reduction in error?

Introduction

Basic Descriptive Statistics: Percentages, Ratios and Rates, Tables, Charts, and Graphs

Measures of Central Tendency and Dispersion

The Normal Curve

Introduction to Inferential Statistics: Sampling and the Sampling Distribution

Estimation Procedures for Sample Means and Proportions

Hypothesis Testing With Nominal and Ordinal Variables: CHI Square

Measures of Association for Variables Measured at the Ordinal Level

Hypothesis Testing With Means and Proportions: The One-Sample Case

Hypothesis Testing With Means and Proportions: The Two-Sample Case

Hypothesis Testing With More Than Two Means: One-Way Analysis of Variance

Hypothesis Testing and Measures of Association for Variables Measured at the Interval-Ratio Level

Partial Correlation and Multiple Regression and Correlation

Filters