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

Register* for Upcoming Training

December 10 - 12, 2018

*Please Note: Completing the registration form will result in the generation of an invoice. Payment is expected within 30 days of course completion. Cancellation less than two weeks prior to class start date will result in a cancellation fee equal to 50% of the registration cost. No Exceptions.

Course Overview

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.

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, SAS Big Data Preparation, Statistics, and Visual Exploration.

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
  • 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.


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.


Statistics 1: $2,100

Course Outline

Note: This course will last all day for the length of the course. Most classes will start at 9:00AM and end at 5:00PM. 

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

Course Logistics

Location: FedEx Institute of Technology, 365 Innovation Drive
Duration: 9:00 AM - 5:00 PM CST

We ask that attendees bring their own device in order to follow along with the class presentations. The temperature in our training room tends to be cooler, we advise you to bring a light jacket or sweater. Parking passes are provided for the garage across to the FedEx Institute of Technology (located on Innovation Drive).

If you have any additional questions please let us know at fedex@memphis.edu.

Travel Information

Click here to learn more about travel information. 

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