Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression

This course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, linear regression, and logistic regression. This course (or equivalent knowledge) is a prerequisite to many of the courses in the statistical analysis curriculum.

Learn how to

• generate descriptive statistics and explore data with graphs
• perform analysis of variance and apply multiple comparison techniques
• perform linear regression and assess the assumptions
• build a predictive model using SAS Rapid Predictive Modeler
• use regression model selection techniques to aid in the choice of predictor variables in multiple regression
• use diagnostic statistics to assess statistical assumptions and identify potential outliers in multiple regression
• use chi-square statistics to detect associations among categorical variables
• fit a multiple logistic regression model.

Who should attend

Statisticians, researchers, and business analysts who use SAS programming to generate analyses using either continuous or categorical response (dependent) variables.

Prerequisites

Before attending this course, you should:

• have completed the equivalent of an undergraduate course in statistics covering p-values, hypothesis testing, analysis of variance, and regression
• be able to execute SAS programs and create SAS data sets. You can gain this experience by completing the SAS Programming 1: Essentials Course.

This course addresses SAS/STAT software. This course also addresses Base SAS software.

Course Outline

Prerequisite Basic Concepts (e-Course Only)

• descriptive statistics
• inferential statistics
• steps for conducting a hypothesis test
• basics of using your SAS software

Introduction to Statistics

• examining data distributions
• obtaining and interpreting sample statistics using the UNIVARIATE and MEANS procedures
• examining data distributions graphically in the UNIVARIATE and SGPLOT procedures
• constructing confidence intervals
• performing simple tests of hypothesis

t Tests and Analysis of Variance

• performing tests of differences between two group means using PROC TTEST
• performing one-way ANOVA with the GLM procedure
• performing post-hoc multiple comparisons tests in PROC
• GLM
• performing two-way ANOVA with and without interactions

Linear Regression

• producing correlations with the CORR procedure
• fitting a simple linear regression model with the REG procedure
• understanding the concepts of multiple regression
• using automated model selection techniques in PROC REG to choose from among several candidate models
• interpreting models

Linear Regression Diagnostics

• examining residuals
• investigating influential observations
• assessing collinearity

Categorical Data Analysis

• producing frequency tables with the FREQ procedure
• examining tests for general and linear association using the FREQ procedure
• understanding exact tests
• understanding the concepts of logistic regression
• fitting univariate and multivariate logistic regression models using the LOGISTIC procedure