A set of step by step walk through of a hypothesis testing

one proportion z-test using p-values
Chi-square goodness of fit using p-values
one mean with population standard deviation is known using p-values
one mean with population standard deviation is unknown, using p-value

Introductory statistics courses prepare students to think statistically but cover relatively few statistical methods. Building on the basic statistical thinking emphasized in an introductory course, a second course in statistics at the undergraduate level can explore a large number of statistical methods. This text covers more advanced graphical summaries, One-Way ANOVA with pair-wise comparisons, Two-Way ANOVA, Chi-square testing, and simple and multiple linear regression models. Models with interactions are discussed in the Two-Way ANOVA and multiple linear regression setting with categorical explanatory variables. Randomization-based inferences are used to introduce new parametric distributions and to enhance understanding of what evidence against the null hypothesis “looks like”. Throughout, the use of the statistical software R via Rstudio is emphasized with all useful code and data sets provided within the text. This is Version 3.0 of the book.

Introductory statistics course developed through the Ohio Department of Higher Education OER Innovation Grant. The course is part of the Ohio Transfer Module and is also named TMM010. For more information about credit transfer between Ohio colleges and universities please visit: www.ohiohighered.org/transfer.Team LeadKameswarrao Casukhela                     Ohio State University – LimaContent ContributorsEmily Dennett                                       Central Ohio Technical CollegeSara Rollo                                            North Central State CollegeNicholas Shay                                      Central Ohio Technical CollegeChan Siriphokha                                   Clark State Community CollegeLibrarianJoy Gao                                                Ohio Wesleyan UniversityReview TeamAlice Taylor                                           University of Rio GrandeJim Cottrill                                             Ohio Dominican University

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