# All resources in Statistics

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This module contains Excel and Google Spreadsheets for all statistical procedures used in an Intro Stats course. The spreadsheets are self-explanatory. Students insert data in the indicated areas. Spreadsheets are designed to automatically complete all calculations and show the results.One Variable Statistics Frequency DistributionDiscrete Probability DistributionNormal DistributionConfidence IntervalsTest of HypothesisLinear RegressionIndependence

Material Type: Module

# Statistics Course Content, Association Between Categorical Variables, Chi-Square Test of Association, Contingency Tables

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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

Material Type: Module

# Statistics Course Content, Course Final Project, Course Final Project

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This module contains a template for course final project which ties up different concepts taught in an intro stats course. Student are encouraged to seek problems that could be answered using relationships between variables. Students need to collect, summarize, visually represent and explore relationships using data.

Material Type: Module

Authors: Kameswarrao Casukhela, Sara Rollo

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# Statistics Course Content, Correlation and Simple Linear Regression, Correlation and Simple Linear Regression

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Sometimes it is difficult to measure or find information on a variable of interest. The problem then is to use information from easily measurable variables to find the needed information. Naturally, the variables to use must be related to the variable of interest. In this module we will study about relationships between two quantitative variables. We will explore some standard mathematical (linear, quadratic, cubic, etc.) forms of relationships.Learning Objectives:Identify response and explanatory variablesGiven bivariate data make a scatterplot of data and predict the pattern and strength of the relationship between the variablesLinear relationshipDefine correlation, study its properties and use themFind correlation for a bivariate data and interpret the resultsInterpret the square of the correlationTest for the significance of correlation – set up hypothesis and interpret the p-value of the testLinear relationship – Estimate the linear relationship between the two variables.Interpret slope and intercept.Interpret the square of the correlationStudy residuals and residual plots,Distinguish between the terms correlation and causationTest for the significance of the slope coefficient – set up hypothesis and interpret the p-value of the test.Study quadratic and other non-linear models.Textbook Material -  Chapter 12 – Correlation and Regression – Pages 673 - 699

Material Type: Module

Author: Kameswarrao Casukhela

# Statistics Course Content, Full Length Quick Adoption Guide, Pooled Resources and Quick Adoption Guide

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Included here are resources found in individual topics. These includeOpenStax Textbook Introductory Statistics by Barbara Illowsky and Susan Dean  (senior contributing authors) in pdf format.Class Worksheets, Spreadsheets and Course Final Project developed by Statistics Team -Sara Rollo, Assistant Professor, Mathematics,College Mathematics Program Coordinator, North Central State College, 2441 Kenwood Circle, Mansfield, Ohio 44906Nicholas Shay, Assistant Professor of Mathematics, Central Ohio Technical Colleg, 165 Hopewell Hall, Newark, OH 43055Emily Dennett, Assistant Professor of Mathematics, Central Ohio Technical Colleg, 165 Hopewell Hall, Newark, OH 43055Chan Siriphokha, Assistant Professor, Clark State CC, Greene Center, 3775 Pentagon Blvd, Beavercreek, OH 45431Dr. Kameswarrao Casukhela, Senior Lecturer, Department of Mathematics, The Ohio State University at Lima, 4240 Campus Drive, Lima, Ohio 45806Quick Adoption Guide

Material Type: Module