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

COURSE OUTLINE:

Description

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.

#### Certification:

• SAS Certified Clinical Trials Programmer Using SAS 9
• SAS Statistical Business Analysis Using SAS 9: Regression and Modeling

Audience

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

Prerequisites

• Completion of an undergraduate course in statistics covering p-values, hypothesis testing, analysis of variance, and regression
• Ability to execute SAS programs and create SAS data sets

Note: You can gain this experience by completing the SAS Programming 1: Essentials course.

Learning Objectives

• 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, and fit a multiple logistic regression model.

#### 1. 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

#### 2. 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

#### 3. More Complex Linear Models

• Performing two-way ANOVA with and without interactions
• The concepts of multiple regression

#### 4. 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

#### 5. Model Post-Fitting for Inference

• Examining residuals
• Investigating influential observations
• Assessing collinearity

#### 6. Model Building and Scoring for Prediction

• The concepts of predictive modeling
• The importance of data partitioning
• The concepts of scoring
• Obtaining predictions (scoring) for new data using PROC GLMSELECT and PROC PLM

#### 7. Categorical Data Analysis

• Producing frequency tables with the FREQ procedure
• Examining tests for general and linear association using the FREQ procedure
• Exact tests
• 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