SAS Certified Statistical Business Analyst Using SAS9: Regression and Modeling
This SAS Certified Statistical Business Analyst Using SAS9: Regression and Modeling is appropriate for SAS Enterprise Miner from release 5.3 up to 14.2. The training covers the skills that are required to analyze flow diagrams using the toolset of SAS Enterprise Miner for both pattern discovery and predictive modeling.
This course is designed for SAS professionals who use SAS/STAT software to conduct and interpret difficult statistical data analysis. It covers an analysis of variance, linear and the logistic regression, preparing inputs for the predictive models, and measuring model performance. fit a multiple logistic regression model.
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Module 1: ANOVA - 10%
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Module 2: Linear Regression - 20%
- Lesson 1: Fit a multiple linear regression model using the REG and GLM procedures
- Lesson 2: Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models
- Lesson 3: Use the REG or GLMSELECT procedure to perform model selection
- Lesson 4: Assess the validity of a given regression model through the use of diagnostic and residual analysis
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Module 3: Logistic Regression - 25%
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Module 4: Prepare Inputs for Predictive Model Performance - 20%
- Lesson 1: Identify the potential challenges when preparing input data for a model
- Lesson 2: Use the DATA step to manipulate data with loops, arrays, conditional statements and functions
- Lesson 3: Improve the predictive power of categorical inputs
- Lesson 4: Screen variables for irrelevance and non-linear association using the CORR procedure
- Lesson 5: Screen variables for non-linearity using empirical logit plots
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Module 5: Measure Model Performance - 25%
- Lesson 1: Apply the principles of honest assessment to model performance measurement
- Lesson 2: Assess classifier performance using the confusion matrix
- Lesson 3: Model selection and validation using training and validation data
- Lesson 5: Establish effective decision cut-off values for scoring
- Lesson 4: Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
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