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

What’s included

$14.99 / $24.99

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.

4.59

Pay once, own it forever

Video Courses

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

Lectures	Duration
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. Create a SAS account to access SAS ondemand for Academics

2. Upload course data files and SAS programs into SAS ondemand for academics

3. change file path/directory in SAS ondemand for academics

4. examples: update and run SAS programs in SAS ondemand for academics

Analysis of Variance (ANOVA)

Lectures	Duration
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. ANOVA 0

10m

2. Using Proc Univariate to Test the Normality Assumption Using the K-S Test

3. ANOVA 1

10m

4. ANOVA 2

5. ANOVA 3

6. ANOVA 4

7. ANOVA 5

8. ANOVA 6

9. ANOVA 7

12m

10. ANOVA 8

10m

11. ANOVA 9

16m

12. ANOVA 10

13. ANOVA 11

14. ANOVA 12

15. ANOVA 13

16. ANOVA 14

11m

17. ANOVA 15

18. ANOVA 16

Prepare Inputs Vars for predictive Modeling

Lectures	Duration
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

1. Prepare Inputs Vars_1

2. Prepare Inputs Vars_2

13m

3. Prepare Inputs Vars_3.Categorical Input Variable_1.Knowledge points

4. Prepare Inputs Vars_3

8. Prepare Inputs Vars_4

11m

10. Prepare Inputs Vars_5

Linear Regression Analysis

Lectures	Duration
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. 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

5. Multiple Linear Regression: Quantifying the Relative Impact of a Predictor

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

10. Multiple Linear Regression:Variable Selection With Proc REG:Backward Elimination

11. Multiple Linear Regression:Variable Selection With Proc REG: Forward selection

12. Multiple Linear Regression:Variable Selection With Proc REG: Stepwise selection

13. Multiple Linear Regression:Variable Selection With Proc GLMSELECT

15m

14. Multiple Linear Regression: PowerPoint Slides on regression assumptions

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	Duration
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. Logistic Regression Analysis: Overview

10m

2. logistic regression with a continuous numeric predictor Part 1

3. logistic regression with a continuous numeric predictor Part 2

15m

4. Plots for Probabilities of an Event

5. Plots of the Odds Ratio

6. logistic regression with a categorical predictor: Effect Coding Parameterization

10m

7. logistic reg with categorical predictor: Reference Cell Coding Parameterization

8. Multiple Logistic Regression: full model SELECTION=NONE

9. Multiple Logistic Regression: Backward Elimination

10. Multiple Logistic Regression: Forward Selection

11. Multiple Logistic Regression: Stepwise Selection

12. Multiple Logistic Regression: Customized Options

12m

13. Multiple Logistic Regression: Best Subset Selection

14. Multiple Logistic Regression: model interaction

14m

15. Multiple Logistic Reg: Scoring New Data: SCORE Statement with PROC LOGISTIC

16. Multiple Logistic Reg: Scoring New Data: Using the PLM Procedure

17. Multiple Logistic Reg: Scoring New Data: the CODE Statement within PROC LOGISTIC

18. Multiple Logistic Reg: Score New Data: OUTMODEL & INMODEL Options with Logistic

Measure of Model Performance

Lectures	Duration
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

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

4. Using The CTABLE Option in Proc Logistic for Producing Classification Results

10m

5. Assessing the Performance & Generalizability of a Classifier: PowerPoint slides

6. The Effect of Cutoff Values on Sensitivity and Specificity Estimates

11m

7. Measure of Performance Using the Receiver-Operator-Characteristic (ROC) Curve

8. Model Comparison Using the ROC and ROCCONTRAST Statements

9. Measures of Performance Using the Gains Charts

11m

10. Measures of Performance Using the Lift Charts

11. Adjust for Oversample: PEVENT Option for Priors & Manually adjust Classification

16m

12. Manually Adjusting Posterior Probabilities to Account for Oversampling

13. Manually Adjusted Intercept Using the Offset to account for oversampling

14. Automatically Adjusted Posterior Probabilities to Account for Oversampling

15. Decision Theory: Decision Cutoffs and Expected Profits for Model Selection

12m

16. Decision Theory: Using Estimated Posterior Probabilities to Determine Cutoffs