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.

Lernen Sie, wie Sie / 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.

Zielgruppe / Who should attend

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

Verfügbare Schulungsformen / Formats available	Dauer (kann variieren, Details s. Terminliste)
Präsenzkurs:	3 Tage
Live Web Class:	21 Stunden	Systemvoraussetzungen
E-Learning:	21 Stunden/180 Tag Lizenz	Systemvoraussetzungen

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® Programmierung 1: Grundlagen course.

In diesem Kurs wird mit folgenden Software Modulen gearbeitet / 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.

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