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

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

The study of a population, a process or a relationship of interest begins with collecting relevant data from representative samples. A good understanding of the key terms will help us design appropriate statistical analysis of the collected data.Learning Objectives:Distinguish between the terms – population, parameter, sample, statistics, and other key terms.Distinguish between different types of statistical studies – observational, experimental, retrospective or prospectiveLearn about different levels of measurement, data types, and variablesLearn about different ways of collecting data – Sampling Methods and ExperimentsIdentify sources of measurement bias and variabilityTextbook Material: Chapter 1, Sampling & DataSuggested Homework AssignmentChapter 1 – Pages 47 – 60 - Problems 42 to 62, 65, 67, 73, 74, 75, 78, 87, 91

A data set is a listing of variables and their observed values on individuals or objects of study. In this topic we will learn about how to organize data on a single variable using frequency distributions which form the basis for constructing charts including Pie Charts, Bar Charts and Histograms. We also explore Stemplot. It is important to note that the type of chart to be used depends on the type of data/variable to be represented.Given a data set be able to construct a frequency table, find and interpret relative frequency of a value, find and interpret cumulative frequency.For categorical data make and interpret a Pie chart and a Bar Chart. Further know when to construct each type of chart, their relative advantages and limitations.For quantitative data be able to make and interpret Stemplot, Histogram, Frequency Polygon, Ogive, Time Series Graph. Further know when to construct each type of chart, their relative advantages and limitations. Textbook Material -  Chapter 1 – Sampling and Data – pages 13 – 19Chapter 2 – Descriptive Statistics – Pages 67 – 88

A data set is a listing of variables and their observed values on individuals or objects of study. In this topic we will learn about numerical summaries of data on a single variable and learn how to use them to describe data distribution and determine unusual values in the data. The type of numerical summaries to use depend on the data. We will also learn about boxplots.Learning Objectives:Understand which numerical summaries must be used to represent dataBe able to compute and interpret them. Also, know their properties and relative advantages and disadvantages. Further, use these measures to describe distributions, compare values from distributions, detect unusual values in the data, etc.For categorical data use counts and proportions to describe categoriesFor quantitative data useMeasures of Center – Mean, Median, ModeMeasure of Spread – Range, Interquartile Range (IQR), Variance and Standard DeviationMeasures of Location – Minimum, Maximum, Quartiles and PercentilesLearn to distinguish between different types of distributions for quantitative data – symmetric, skewed, bell-shaped, multimodal distributionsLearn about Empirical Rule for bell-shaped distributionsUse z-scores to compare values and detect unusual valuesMake boxplot of dataTextbook Material: Chapter 2 – Descriptive Statistics – Pages 88 - 122Suggested HomeworkChapter 2 - Descriptive Statistics – 29, 31, 32, 43, 57, 60, 69, 71, 82, 84, 86, 88, 89, 104, 106, 108, 109, 115, 119

Producing Data – Sampling MethodsIn this module we will explore the different sampling methods to obtain representative samples from a population. We also learn about the relative advantages and disadvantages of each method. Learning Objectives:Reasons for samplingRandom Vs. Non-Random SamplesSampling Bias and VariabilityRandom Sampling Methods – Simple, Stratified, Systematic, Cluster and Multistage random samplesNon-Random Sampling Methods – Voluntary Response and Convenience samplingSample surveys, sampling errorsBest method of random samplingSampling distributions