A00-240: SAS Statistical Business Analysis Using SAS 9: Regression and Modeling

10m 1. ANOVA 0 3m 2. Using Proc Univariate to Test the Normality Assumption Using the K-S Test 10m 3. ANOVA 1 7m 4. ANOVA 2 4m 5. ANOVA 3 4m 6. ANOVA 4 3m 7. ANOVA 5 4m 8. ANOVA 6 12m 9. ANOVA 7 10m 10. ANOVA 8 16m 11. ANOVA 9 3m 12. ANOVA 10 3m 13. ANOVA 11 5m 14. ANOVA 12 8m 15. ANOVA 13 11m 16. ANOVA 14 3m 17. ANOVA 15 3m 18. ANOVA 16

6m 1. Prepare Inputs Vars_1 13m 2. Prepare Inputs Vars_2 5m 3. Prepare Inputs Vars_3.Categorical Input Variable_1.Knowledge points 7m 4. Prepare Inputs Vars_3 11m 5. Prepare Inputs Vars_4 5m 6. Prepare Inputs Vars_5

10m 1. Exploring the Relationship between Two Continuous Variables using Scatter Plots 15m 2. Producing Correlation Coefficients Using the CORR Procedure 10m 3. Multiple Linear Regression: fit multiple regression with Proc REG 6m 4. Multiple Linear Regression: Measures of fit 3m 5. Multiple Linear Regression: Quantifying the Relative Impact of a Predictor 11m 6. Multiple Linear Regression: Check Collinearity Using VIF, COLLIN, and COLLINOINT 15m 7. fit simple linear regression with Proc GLM 12m 8. Multiple Linear Reg: Var Selection With Proc REG:all possible subset: adjust R2 6m 9. Multiple Linear Reg: Var Selection With Proc REG:all possible subset: Mallows Cp 8m 10. Multiple Linear Regression:Variable Selection With Proc REG:Backward Elimination 9m 11. Multiple Linear Regression:Variable Selection With Proc REG: Forward selection 4m 12. Multiple Linear Regression:Variable Selection With Proc REG: Stepwise selection 15m 13. Multiple Linear Regression:Variable Selection With Proc GLMSELECT 8m 14. Multiple Linear Regression: PowerPoint Slides on regression assumptions 13m 15. Multiple Linear Regression: regression assumptions 11m 16. Multiple Linear Regression: PowerPoint Slides on influential observations 18m 17. Multiple Linear Regression: Using statistics to identify influential observation

10m 1. Logistic Regression Analysis: Overview 5m 2. logistic regression with a continuous numeric predictor Part 1 15m 3. logistic regression with a continuous numeric predictor Part 2 5m 4. Plots for Probabilities of an Event 6m 5. Plots of the Odds Ratio 10m 6. logistic regression with a categorical predictor: Effect Coding Parameterization 5m 7. logistic reg with categorical predictor: Reference Cell Coding Parameterization 8m 8. Multiple Logistic Regression: full model SELECTION=NONE 8m 9. Multiple Logistic Regression: Backward Elimination 6m 10. Multiple Logistic Regression: Forward Selection 7m 11. Multiple Logistic Regression: Stepwise Selection 12m 12. Multiple Logistic Regression: Customized Options 5m 13. Multiple Logistic Regression: Best Subset Selection 14m 14. Multiple Logistic Regression: model interaction 6m 15. Multiple Logistic Reg: Scoring New Data: SCORE Statement with PROC LOGISTIC 5m 16. Multiple Logistic Reg: Scoring New Data: Using the PLM Procedure 4m 17. Multiple Logistic Reg: Scoring New Data: the CODE Statement within PROC LOGISTIC 5m 18. Multiple Logistic Reg: Score New Data: OUTMODEL & INMODEL Options with Logistic

10m 1. Measure of Model Performance: Overview 10m 2. PROC SURVEYSELECT for Creating Training and Validation Data Sets 7m 3. Measures of Performance Using the Classification Table: PowerPoint Presentation 10m 4. Using The CTABLE Option in Proc Logistic for Producing Classification Results 4m 5. Assessing the Performance & Generalizability of a Classifier: PowerPoint slides 11m 6. The Effect of Cutoff Values on Sensitivity and Specificity Estimates 7m 7. Measure of Performance Using the Receiver-Operator-Characteristic (ROC) Curve 5m 8. Model Comparison Using the ROC and ROCCONTRAST Statements 11m 9. Measures of Performance Using the Gains Charts 4m 10. Measures of Performance Using the Lift Charts 16m 11. Adjust for Oversample: PEVENT Option for Priors & Manually adjust Classification 5m 12. Manually Adjusting Posterior Probabilities to Account for Oversampling 7m 13. Manually Adjusted Intercept Using the Offset to account for oversampling 6m 14. Automatically Adjusted Posterior Probabilities to Account for Oversampling 12m 15. Decision Theory: Decision Cutoffs and Expected Profits for Model Selection 5m 16. Decision Theory: Using Estimated Posterior Probabilities to Determine Cutoffs