This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression. This course (or equivalent knowledge) is a prerequisite to many of the courses in the statistical analysis curriculum. A more advanced treatment of ANOVA and regression occurs in the Statistics 2: ANOVA and Regression course. A more advanced treatment of logistic regression occurs in the Categorical Data Analysis Using Logistic Regression course and the Predictive Modeling Using Logistic Regression course.

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
• 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

Duration:  3 days

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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 and touches on SAS/GRAPH software. You can benefit from this course even if SAS/GRAPH software is not installed at your location.

Prerequisite Basic Concepts

• 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