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.
This course can help prepare you for the following certification exam(s): SAS Certified Clinical Trials Programming Using SAS 9, SAS Statistical Business Analysis Using SAS 9: Regression and Modeling.
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.
Who should attend
Statisticians, researchers, and business analysts who use SAS programming to generate analyses using either continuous or categorical response (dependent) variables
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® Programming 1: Essentials course.
This course addresses SAS/STAT software.