Winter Savings - 4 days left to save on IT training. Use promo code SNOWBALL

closeClose

# SAS(R) Enterprise Guide(R): ANOVA, Regression, and Logistic Regression

 Code: EGBS71 Tuition (USD): \$2,100.00 • Classroom (3 days) \$2,100.00 • Virtual (3 days)
 GSA (USD): \$1,904.28 • Classroom (3 days) \$1,904.28 • Virtual (3 days)

This course is designed for SAS Enterprise Guide users who want to perform statistical analyses. The course is written for SAS Enterprise Guide 7.1 along with SAS 9.4, but students with previous SAS Enterprise Guide versions will also get value from this course. An e-course is also available for SAS Enterprise Guide 5.1 and SAS Enterprise Guide 4.3.

#### Skills Gained

• generate descriptive statistics and explore data with graphs
• perform analysis of variance
• perform linear regression and assess the assumptions
• use diagnostic statistics to identify potential outliers in multiple regression
• use chi-square statistics to detect associations among categorical variables
• fit a multiple logistic regression model.

#### Who Can Benefit

• Statisticians and business analysts who want to use a point-and-click interface to SAS

#### Prerequisites

• Before attending this course, you should
• be familiar with both SAS Enterprise Guide and basic statistical concepts
• have completed an undergraduate course in statistics covering -values, hypothesis testing, analysis of variance, and regression
• be able to perform analyses and create data sets with SAS Enterprise Guide software. You can gain this experience by completing the SAS(R) Enterprise Guide(R) 1: Querying and Reporting course.

### Course Details

#### Prerequisite Basic Concepts

• discussing descriptive statistics
• discussing inferential statistics
• listing steps for conducting a hypothesis test
• discussing basics of using your SAS software

#### Getting Started in Enterprise Guide 7.1

• introducing to the SAS Enterprise Guide 7.1 environment

#### Introduction to Statistics

• discussing fundamental statistical concepts
• examining distributions
• describing categorical data
• constructing confidence intervals
• performing simple tests of hypothesis

#### Analysis of Variance (ANOVA)

• performing one-way ANOVA
• performing multiple comparisons
• performing two-way ANOVA with and without interactions

#### Regression

• using exploratory data analysis
• producing correlations
• fitting a simple linear regression model
• understanding the concepts of multiple regression
• building and interpreting models
• describing all regression techniques
• exploring stepwise selection techniques

#### Regression Diagnostics

• examining residuals
• investigating influential observations and collinearity

#### Categorical Data Analysis

• describing categorical data
• examining tests for general and linear association
• understanding the concepts of logistic regression and multiple logistic regression
• performing backward elimination with logistic regression