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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.

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

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
score new data using developed models.

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

Course Overview and Review of Concepts

descriptive statistics
inferential statistics
examining data distributions
obtaining and interpreting sample statistics using the UNIVARIATE procedure
examining data distributions graphically in the UNIVARIATE and FREQ procedures
constructing confidence intervals
performing simple tests of hypothesis
performing tests of differences between two group means using PROC TTEST

ANOVA and Regression

performing one-way ANOVA with the GLM procedure
performing post-hoc multiple comparisons tests in PROC GLM
producing correlations with the CORR procedure
fitting a simple linear regression model with the REG procedure

More Complex Linear Models

performing two-way ANOVA with and without interactions
understanding the concepts of multiple regression

Model Building and Effect Selection

automated model selection techniques in PROC GLMSELECT to choose from among several candidate models
interpreting and comparison of selected models

Model Post-Fitting for Inference

examining residuals
investigating influential observations
assessing collinearity

Model Building and Scoring for Prediction

understanding the concepts of predictive modeling
understanding the importance of data partitioning
understanding the concepts of scoring
obtaining predictions (scoring) for new data using PROC GLMSELECT and PROC PLM

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
using automated model selection techniques in PROC LOGISTIC including interaction terms
obtaining predictions (scoring) for new data using PROC PLM

Upcoming Trainings

Join our public courses in our Republic of the Philippines facilities. Private class trainings will be organized at the location of your preference, according to your schedule.

Classroom / Virtual Classroom

18 August 2024