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- A00-240 (SAS Statistical Business Analysis Using SAS 9: Regression and Modeling)
A00-240: SAS Statistical Business Analysis Using SAS 9: Regression and Modeling
Your SAS Institute A00-240 exam is just around the corner, right? So, it's high time to find an effective preparation tool! Our training course is what you really need! This is a series of videos led by the experienced IT instructors who will provide you with a detailed overview of the A00-240 certification test. Ace your SAS Institute A00-240 at the first attempt and obtain the SAS Statistical Business Analysis Using SAS 9: Regression and Modeling credential with ease.
135
87
11 h
5.0
Price $27.49
Today $24.99
Curriculum for A00-240 Video Course
1. Free cloud-based SAS...
4 Lectures
00:23:00
2. Analysis of Variance...
18 Lectures
01:59:00
3. Prepare Inputs Vars...
14 Lectures
02:01:00
4. Linear Regression...
17 Lectures
02:54:00
5. Logistic Regression...
18 Lectures
02:16:00
6. Measure of Model...
16 Lectures
02:10:00
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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