There is a new version of this course. Please see 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
- 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
||24 hours/1 year license
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.
This course addresses Base SAS, SAS/STAT software.
This course also addresses Base SAS software and touches on SAS/GRAPH software. You benefit from this course even if SAS/GRAPH software is not installed at your location.
Introduction to Statistics
t-Tests and Analysis of Variance
- 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
- 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 Diagnostics
- 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
Categorical Data Analysis
- examining residuals
- investigating influential observations
- assessing collinearity
- 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