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
video-file-formats

$14.99/24.99


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

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

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

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

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

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

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