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

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Curriculum for A00-240 Video Course

Free cloud-based SAS software option for learning: SAS OnDemand for Academics

Video Name Time
1. Create a SAS account to access SAS ondemand for Academics 3:00
2. Upload course data files and SAS programs into SAS ondemand for academics 6:00
3. change file path/directory in SAS ondemand for academics 7:00
4. examples: update and run SAS programs in SAS ondemand for academics 7:00
Video Name Time
1. ANOVA 0. Using TTEST to compare means 10:00
2. Using Proc Univariate to Test the Normality Assumption Using the K-S Test 3:00
3. ANOVA 1. One-factor ANOVA model and Test Statistic in PowerPoint Presentation 10:00
4. ANOVA 2. The GLM Procedure for Investigating Mean Differences 7:00
5. ANOVA 3. generate Predicted Values & Residuals Use OUTPUT Statement in Proc GLM 4:00
6. ANOVA 4. Measures of fit: output explanation of one-way ANOVA 4:00
7. ANOVA 5. The Normality Assumption and the PLOTS Option in Proc GLM 3:00
8. ANOVA 6. Levene’s Test for Equal Variances and the MEANS Statement in Proc GLM 4:00
9. ANOVA 7. Post Hoc Tests: The Tukey-Kramer Procedure and the MEANS Statement 12:00
10. ANOVA 8. Other Post Hoc Procedures, the LSMEANS Statement, and the Diffogram 10:00
11. ANOVA 9. the Randomized Block Design with example and Interpretation 16:00
12. ANOVA 10. Randomized block design: Post Hoc Tests Using the LSMEANS Statement 3:00
13. ANOVA 11. Assess Assumptions of a Randomized Block Design Using the PLOTS Option 3:00
14. ANOVA 12. Unbalanced Designs, the LSMEANS Statement and Type III Sums of Squares 5:00
15. ANOVA 13. Two factor ANOVA: overview in PowerPoint Presentation 8:00
16. ANOVA 14. Example and Interpretation of the Two-Factor ANOVA 11:00
17. ANOVA 15. Analyze Simple Effects When Interaction Exists Use LSMEANS with Slice 3:00
18. ANOVA 16. Assessing the Assumptions of a Two-Factor Analysis of Variance 3:00
Video Name Time
1. Prepare Inputs Vars_1. Chapter Overview 6:00
2. Prepare Inputs Vars_2. Missing values and imputation 13:00
3. Prepare Inputs Vars_3.Categorical Input Variable_1.Knowledge points 5:00
4. Prepare Inputs Vars_3. Categorical Input Variables_2. Proc freq and Proc Means 7:00
5. Prepare Inputs Vars_3. Categorical Input Variables_3. Proc Cluster 8:00
6. Prepare Inputs Vars_3. Categorical Input Variables_4. Cut off point 6:00
7. Prepare Inputs Vars_3. Categorical Input Variables_5. cluster var 10:00
8. Prepare Inputs Vars_4. Variable Cluster_1. Slides on VARCLUS for redundancy 11:00
9. Prepare Inputs Vars_4. Variable Cluster_2. Proc VARCLUS for reduce redundancy 19:00
10. Prepare Inputs Vars_5. Variable Screening_1. Overview on Knowledge Points 5:00
11. Prepare Inputs Vars_5. Variable Screening_2. Proc CORR detect Association_Part A 8:00
12. Prepare Inputs Vars_5. Variable Screening_3. Proc CORR detect Association_Part B 6:00
13. Prepare Inputs Vars_5. Variable Screening_4. Proc CORR detect Association_Part C 7:00
14. Prepare Inputs Vars_5. Variable Screening_5. Empirical Logit detect Non-Linear 10:00
Video Name Time
1. Exploring the Relationship between Two Continuous Variables using Scatter Plots 10:00
2. Producing Correlation Coefficients Using the CORR Procedure 15:00
3. Multiple Linear Regression: fit multiple regression with Proc REG 10:00
4. Multiple Linear Regression: Measures of fit 6:00
5. Multiple Linear Regression: Quantifying the Relative Impact of a Predictor 3:00
6. Multiple Linear Regression: Check Collinearity Using VIF, COLLIN, and COLLINOINT 11:00
7. fit simple linear regression with Proc GLM 15:00
8. Multiple Linear Reg: Var Selection With Proc REG:all possible subset: adjust R2 12:00
9. Multiple Linear Reg: Var Selection With Proc REG:all possible subset: Mallows Cp 6:00
10. Multiple Linear Regression:Variable Selection With Proc REG:Backward Elimination 8:00
11. Multiple Linear Regression:Variable Selection With Proc REG: Forward selection 9:00
12. Multiple Linear Regression:Variable Selection With Proc REG: Stepwise selection 4:00
13. Multiple Linear Regression:Variable Selection With Proc GLMSELECT 15:00
14. Multiple Linear Regression: PowerPoint Slides on regression assumptions 8:00
15. Multiple Linear Regression: regression assumptions 13:00
16. Multiple Linear Regression: PowerPoint Slides on influential observations 11:00
17. Multiple Linear Regression: Using statistics to identify influential observation 18:00
Video Name Time
1. Logistic Regression Analysis: Overview 10:00
2. logistic regression with a continuous numeric predictor Part 1 5:00
3. logistic regression with a continuous numeric predictor Part 2 15:00
4. Plots for Probabilities of an Event 5:00
5. Plots of the Odds Ratio 6:00
6. logistic regression with a categorical predictor: Effect Coding Parameterization 10:00
7. logistic reg with categorical predictor: Reference Cell Coding Parameterization 5:00
8. Multiple Logistic Regression: full model SELECTION=NONE 8:00
9. Multiple Logistic Regression: Backward Elimination 8:00
10. Multiple Logistic Regression: Forward Selection 6:00
11. Multiple Logistic Regression: Stepwise Selection 7:00
12. Multiple Logistic Regression: Customized Options 12:00
13. Multiple Logistic Regression: Best Subset Selection 5:00
14. Multiple Logistic Regression: model interaction 14:00
15. Multiple Logistic Reg: Scoring New Data: SCORE Statement with PROC LOGISTIC 6:00
16. Multiple Logistic Reg: Scoring New Data: Using the PLM Procedure 5:00
17. Multiple Logistic Reg: Scoring New Data: the CODE Statement within PROC LOGISTIC 4:00
18. Multiple Logistic Reg: Score New Data: OUTMODEL & INMODEL Options with Logistic 5:00
Video Name Time
1. Measure of Model Performance: Overview 10:00
2. PROC SURVEYSELECT for Creating Training and Validation Data Sets 10:00
3. Measures of Performance Using the Classification Table: PowerPoint Presentation 7:00
4. Using The CTABLE Option in Proc Logistic for Producing Classification Results 10:00
5. Assessing the Performance & Generalizability of a Classifier: PowerPoint slides 4:00
6. The Effect of Cutoff Values on Sensitivity and Specificity Estimates 11:00
7. Measure of Performance Using the Receiver-Operator-Characteristic (ROC) Curve 7:00
8. Model Comparison Using the ROC and ROCCONTRAST Statements 5:00
9. Measures of Performance Using the Gains Charts 11:00
10. Measures of Performance Using the Lift Charts 4:00
11. Adjust for Oversample: PEVENT Option for Priors & Manually adjust Classification 16:00
12. Manually Adjusting Posterior Probabilities to Account for Oversampling 5:00
13. Manually Adjusted Intercept Using the Offset to account for oversampling 7:00
14. Automatically Adjusted Posterior Probabilities to Account for Oversampling 6:00
15. Decision Theory: Decision Cutoffs and Expected Profits for Model Selection 12:00
16. Decision Theory: Using Estimated Posterior Probabilities to Determine Cutoffs 5:00

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