A00-240: SAS Statistical Business Analysis Using SAS 9: Regression and Modeling
Get ready for your exam by enrolling in our comprehensive training course. This course includes a full set of instructional videos designed to equip you with in-depth knowledge essential for passing the certification exam with flying colors.
What’s included
- 79 : Lectures
- 10h 29m : Duration
$14.99/24.99
Free cloud-based SAS software option for learning: SAS OnDemand for Academics
| Lectures |
|---|
| 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 |
Analysis of Variance (ANOVA)
| Lectures |
|---|
| 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 |
Prepare Inputs Vars for predictive Modeling
| Lectures |
|---|
| 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 |
| 8. Prepare Inputs Vars_4- 11m |
| 10. Prepare Inputs Vars_5- 5m |
Linear Regression Analysis
| Lectures |
|---|
| 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 |
Logistic Regression Analysis
| Lectures |
|---|
| 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 |
Measure of Model Performance
| Lectures |
|---|
| 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 |
