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.
- 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 Can Benefit
- 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.