- Module I: Introduction to SAS/STAT®
- Module II: Introduction to predictive modeling using Regression Model
Module I: Introduction to SAS/STAT
1 Introduction to Statistics
- Fundamental Statistical Concepts
- Examining Distributions
- Confidence Intervals for the Mean
- Hypothesis Testing
2 Analysis of Variance (ANOVA)
- One-Way ANOVA: Two Populations
- ANOVA with More than Two Populations
- Two-Way ANOVA with Interactions
3 Regression
- Exploratory Data Analysis
- Simple Linear Regression
- Concepts of Multiple Regressions
- Model Building and Interpretation
4 Regression Diagnostics
- Examining Residuals
- Influential Observations
- Collinearity
5 Categorical Data Analysis
- Describing Categorical Data
- Tests of Association
- Introduction to Logistic Regression
- Multiple Logistic Regressions
- Logit Plots (Self-Study)
Module II: Introduction to predictive modeling using Regression Model
1. Predictive Modeling
- Introduction
- Analytical Challenges
2. Fitting the Model
- The Model
- Adjustments for Oversampling
3. Preparing the Input Variables
- Missing Values
- Categorical Inputs
- Variable Clustering
- Subset Selection
4. Classifier Performance
- Honest Assessment
- Misclassification
- Allocation Rules
- Overall Predictive Power
5. Nonlinearities and Interactions
- Detection
- Polynomials
- Multilayer Perceptions