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- Assess the validity of a given regression model through the use of diagnostic and residual analysis
- Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models
- Fit a multiple linear regression model using the REG and GLM procedures
- Use the REG or GLMSELECT procedure to perform model selection
3. Logistic Regression - 25%
- Score new data sets using the LOGISTIC and PLM procedures
- Optimize model performance through input selection
- Perform logistic regression with the LOGISTIC procedure
- Interpret the output of the LOGISTIC procedure
4. Prepare Inputs for Predictive Model Performance - 20%
- Identify the potential challenges when preparing input data for a model
- Use the DATA step to manipulate data with loops, arrays, conditional statements and functions
- Screen variables for irrelevance and non-linear association using the CORR procedure
- Screen variables for non-linearity using empirical logit plots
- Improve the predictive power of categorical inputs
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- Model selection and validation using training and validation data
- Assess classifier performance using the confusion matrix
- Establish effective decision cut-off values for scoring
- Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
- Apply the principles of honest assessment to model performance measurement
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