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

Curriculum For This Course

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

4Lectures

Time 00:23:00

Analysis of Variance (ANOVA)

18Lectures

Time 01:59:00

Prepare Inputs Vars for predictive Modeling

14Lectures

Time 02:01:00

Linear Regression Analysis

17Lectures

Time 02:54:00

Logistic Regression Analysis

18Lectures

Time 02:16:00

Measure of Model Performance

16Lectures

Time 02:10:00

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

Analysis of Variance (ANOVA)
	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

Prepare Inputs Vars for predictive Modeling
	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

Linear Regression Analysis
	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

Logistic Regression Analysis
	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

Measure of Model Performance
	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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