A00-240: SAS Statistical Business Analysis Using SAS 9: Regression and Modeling
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79 Lectures
10h 29m
Prepare for your SAS Institute examination with our training course. The A00-240 course contains a complete batch of videos that will provide you with profound and thorough knowledge related to SAS Institute certification exam. Pass the SAS Institute A00-240 test with flying colors.
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Curriculum For This Course
- 1. Create a SAS account to access SAS ondemand for Academics 3m
- 2. Upload course data files and SAS programs into SAS ondemand for academics 6m
- 3. change file path/directory in SAS ondemand for academics 7m
- 4. examples: update and run SAS programs in SAS ondemand for academics 7m
- 1. ANOVA 0 10m
- 2. Using Proc Univariate to Test the Normality Assumption Using the K-S Test 3m
- 3. ANOVA 1 10m
- 4. ANOVA 2 7m
- 5. ANOVA 3 4m
- 6. ANOVA 4 4m
- 7. ANOVA 5 3m
- 8. ANOVA 6 4m
- 9. ANOVA 7 12m
- 10. ANOVA 8 10m
- 11. ANOVA 9 16m
- 12. ANOVA 10 3m
- 13. ANOVA 11 3m
- 14. ANOVA 12 5m
- 15. ANOVA 13 8m
- 16. ANOVA 14 11m
- 17. ANOVA 15 3m
- 18. ANOVA 16 3m
- 1. Prepare Inputs Vars_1 6m
- 2. Prepare Inputs Vars_2 13m
- 3. Prepare Inputs Vars_3.Categorical Input Variable_1.Knowledge points 5m
- 4. Prepare Inputs Vars_3 7m
- 5. Prepare Inputs Vars_4 11m
- 6. Prepare Inputs Vars_5 5m
- 1. Exploring the Relationship between Two Continuous Variables using Scatter Plots 10m
- 2. Producing Correlation Coefficients Using the CORR Procedure 15m
- 3. Multiple Linear Regression: fit multiple regression with Proc REG 10m
- 4. Multiple Linear Regression: Measures of fit 6m
- 5. Multiple Linear Regression: Quantifying the Relative Impact of a Predictor 3m
- 6. Multiple Linear Regression: Check Collinearity Using VIF, COLLIN, and COLLINOINT 11m
- 7. fit simple linear regression with Proc GLM 15m
- 8. Multiple Linear Reg: Var Selection With Proc REG:all possible subset: adjust R2 12m
- 9. Multiple Linear Reg: Var Selection With Proc REG:all possible subset: Mallows Cp 6m
- 10. Multiple Linear Regression:Variable Selection With Proc REG:Backward Elimination 8m
- 11. Multiple Linear Regression:Variable Selection With Proc REG: Forward selection 9m
- 12. Multiple Linear Regression:Variable Selection With Proc REG: Stepwise selection 4m
- 13. Multiple Linear Regression:Variable Selection With Proc GLMSELECT 15m
- 14. Multiple Linear Regression: PowerPoint Slides on regression assumptions 8m
- 15. Multiple Linear Regression: regression assumptions 13m
- 16. Multiple Linear Regression: PowerPoint Slides on influential observations 11m
- 17. Multiple Linear Regression: Using statistics to identify influential observation 18m
- 1. Logistic Regression Analysis: Overview 10m
- 2. logistic regression with a continuous numeric predictor Part 1 5m
- 3. logistic regression with a continuous numeric predictor Part 2 15m
- 4. Plots for Probabilities of an Event 5m
- 5. Plots of the Odds Ratio 6m
- 6. logistic regression with a categorical predictor: Effect Coding Parameterization 10m
- 7. logistic reg with categorical predictor: Reference Cell Coding Parameterization 5m
- 8. Multiple Logistic Regression: full model SELECTION=NONE 8m
- 9. Multiple Logistic Regression: Backward Elimination 8m
- 10. Multiple Logistic Regression: Forward Selection 6m
- 11. Multiple Logistic Regression: Stepwise Selection 7m
- 12. Multiple Logistic Regression: Customized Options 12m
- 13. Multiple Logistic Regression: Best Subset Selection 5m
- 14. Multiple Logistic Regression: model interaction 14m
- 15. Multiple Logistic Reg: Scoring New Data: SCORE Statement with PROC LOGISTIC 6m
- 16. Multiple Logistic Reg: Scoring New Data: Using the PLM Procedure 5m
- 17. Multiple Logistic Reg: Scoring New Data: the CODE Statement within PROC LOGISTIC 4m
- 18. Multiple Logistic Reg: Score New Data: OUTMODEL & INMODEL Options with Logistic 5m
- 1. Measure of Model Performance: Overview 10m
- 2. PROC SURVEYSELECT for Creating Training and Validation Data Sets 10m
- 3. Measures of Performance Using the Classification Table: PowerPoint Presentation 7m
- 4. Using The CTABLE Option in Proc Logistic for Producing Classification Results 10m
- 5. Assessing the Performance & Generalizability of a Classifier: PowerPoint slides 4m
- 6. The Effect of Cutoff Values on Sensitivity and Specificity Estimates 11m
- 7. Measure of Performance Using the Receiver-Operator-Characteristic (ROC) Curve 7m
- 8. Model Comparison Using the ROC and ROCCONTRAST Statements 5m
- 9. Measures of Performance Using the Gains Charts 11m
- 10. Measures of Performance Using the Lift Charts 4m
- 11. Adjust for Oversample: PEVENT Option for Priors & Manually adjust Classification 16m
- 12. Manually Adjusting Posterior Probabilities to Account for Oversampling 5m
- 13. Manually Adjusted Intercept Using the Offset to account for oversampling 7m
- 14. Automatically Adjusted Posterior Probabilities to Account for Oversampling 6m
- 15. Decision Theory: Decision Cutoffs and Expected Profits for Model Selection 12m
- 16. Decision Theory: Using Estimated Posterior Probabilities to Determine Cutoffs 5m
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