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Course fee and EPTO/APTO units differ for on-site training.Making Business Decisions Using Linear Models in SAS: A Harvard Case Study
Business Knowledge Series course
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Duration: 2.0 days Course fee: $1,450 EPTO units: 2.8 CEUs: 1.2 | ||
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Presented by F. Michael Speed, Ph.D. and Simon Sheather, PhD, professors at Texas A&M University
This advanced course identifies the benefits and pitfalls of using statistical analyses for making business decisions. It addresses practical applications of regression and analysis of covariance in a business setting using a Harvard Business School Case Study. The primary software used in the course is SAS Enterprise Guide, although some code is also used to run analyses.
Learn how to
- make a business decision using linear models in SAS
- build complex analysis of covariance models
- determine the hypothesis being tested by SAS software
- use and understand advance diagnostic techniques.
Who should attend
This course is aimed a business analysts as well as researchers in most disciplines including agricultural sciences, psychology, education, social science, engineering, and medicine. It is also appropriate for professionals conducting organizational or business research.
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Prerequisites
Before attending this course, you should
- be familiar with the basic structure and concepts of the SAS System, which you can gain from the SAS Programming I: Essentials course
- be familiar with the core concepts of descriptive statistics and multiple regression
- have completed the Statistics I: Introduction to ANOVA, Regression, and Logistic Regression course or have equivalent knowledge
- have some experience using SAS Enterprise Guide, specifically being able to import data, open a data set, and run tasks.
Course Contents
Business Scenario and Motivation: Harvard Case Study - Store 24
- introduction
- the main business issue
- overview of SAS Enterprise Guide
- our first regression
- interpretation of results
- scatter plots
- descriptive statistics
- exploratory plots
- the model and assumptions
- possible problem points, multicollinearity, and additional residual analyses
- variable subset selection
- making scale independent variables into categorical variables
- introduction
- the model and assumptions
- residual analyses
- variable subset selection
- pre-post analysis
- LSMEANS versus MEANS
Software
This course addresses SAS/STAT. This course also addresses Base SAS software, SAS/GRAPH software, and SAS Enterprise Guide.
Course Materials
Students receive a hardcopy of the course notes and, in some courses, can choose to take home a copy of the course data.
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