# 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.

This course can help prepare you for the following certification exam(s): SAS Certified Clinical Trials Programmer Using SAS 9, SAS Statistical Business Analysis Using SAS 9: Regression and Modeling.

• generate descriptive statistics and explore data with graphs
• perform analysis of variance and apply multiple comparison techniques
• perform linear regression and assess the assumptions
• 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 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.

### Course Outline

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