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Predictive Modeling Using Logistic Regression Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression

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Reference: https://www.sas.com/en_us/certification/credentials/advanced-analytics/statistical-business-analyst.html

SASInstitute A00-240 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Linear Regression	20%	- Model Selection and Diagnostics 1. Perform model selection using REG or GLMSELECT 2. Assess model validity 3. Conduct residual and diagnostic analysis - Build Regression Models 1. Use REG and GLM procedures 2. Fit multiple linear regression models - Interpret Regression Results 1. Interpret REG, PLM and GLM output 2. Evaluate parameter estimates
Topic 2: ANOVA	10%	- ANOVA Analysis 1. Analyze differences between population means using GLM and TTEST procedures 2. Detect and analyze factor interactions 3. Perform post hoc tests 4. Evaluate treatment effects - ANOVA Concepts and Assumptions 1. Identify appropriate ANOVA methods 2. Verify ANOVA assumptions
Topic 3: Measure Model Performance	25%	- Validation and Selection 1. Compare and select predictive models 2. Perform model validation using training and validation data - Model Assessment 1. Assess classifier performance using confusion matrices 2. Apply principles of honest assessment - Performance Visualization 1. Create and interpret gains charts 2. Create and interpret lift charts 3. Create and interpret ROC charts - Decision Optimization 1. Establish effective decision cutoff values 2. Evaluate scoring strategies
Topic 4: Prepare Inputs for Predictive Model Performance	20%	- Variable Engineering and Screening 1. Improve predictive power of categorical variables 2. Identify nonlinear associations using CORR procedure 3. Use empirical logit plots for nonlinearity detection 4. Screen variables for irrelevance - Data Preparation 1. Manipulate data using DATA step programming 2. Use loops, arrays, functions and conditional statements 3. Identify challenges in preparing model input data
Topic 5: Logistic Regression	25%	- Interpret and Deploy Models 1. Score new data sets using LOGISTIC and PLM procedures 2. Interpret LOGISTIC procedure output - Develop Logistic Models 1. Optimize model performance through input selection 2. Perform logistic regression using the LOGISTIC procedure