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.
Statisticians, researchers, and business analysts who use SAS programming to generate analyses using either continuous or categorical response (dependent) variables
• 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.
Module 1: Course Overview and Review of Concepts
Module 2: ANOVA and Regression
Module 3: More Complex Linear Models
Module 4: Model Building and Effect Selection
Module 5: Model Post-Fitting for Inference
Module 6: Model Building and Scoring for Prediction
Module 7: Categorical Data Analysis