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SAS Enterprise Guide: ANOVA, Regression, and Logistic Regression

30 Hours
Online Instructor-led Training
USD 1399 (USD 2800)
Save 50% Offer ends on 30-Jun-2024
SAS Enterprise Guide: ANOVA, Regression, and Logistic Regression course and certification
318 Learners

About this Course

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.

Learn how to
  • 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.
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Target Audience

Statisticians and business analysts who want to use a point-and-click interface to SAS
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SAS Enterprise Guide: ANOVA, Regression, and Logistic Regression

Course Details & Curriculum
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
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