An association between two variables explains how one variable changes in response to changes in the other variable. A lack of association indicates that the two variables are independent of each other, meaning the chances of events of one variable are not affected by the occurrence or non-occurrence of events of the other variable. In this module we will learn about the different tools used for analyzing associations in two-variable categorical data sets.Learning Objectives:Identify response and explanatory variablesOrganize data into two-way tablesStudy joint, marginal and conditional distributions and learn the relationship between themObserved and expected frequencies, Chi-Square test statisticChi-Square test of independence – set up hypothesis, use technology to run the test and interpret P-valueTextbook Material - · Chapter 11.3 – Test of Independence – Pages 627 - 632
Statistics and Probability
Community College / Lower Division, College / Upper Division
Ohio Transfer Module Mathematics, Statistics, and Logic (TMM) Standards
Core TMM010 Outcome: Core skill demonstrated by students who successfully complete an Introductory Statistics Course
Standard: Summarize univariate and bivariate data by employing appropriate graphical, tabular, and numerical methods and describe the attributes of or relationships between the data. These may include (but are not limited to): frequency distributions; box plots; scatter plots; correlation coefficients; regression analysis; and measures of center, variation, and relative position.