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A00-240: SAS Statistical Business Analysis Using SAS 9: Regression and Modeling Certification Video Training Course
The complete solution to prepare for for your exam with A00-240: SAS Statistical Business Analysis Using SAS 9: Regression and Modeling certification video training course. The A00-240: SAS Statistical Business Analysis Using SAS 9: Regression and Modeling certification video training course contains a complete set of videos that will provide you with thorough knowledge to understand the key concepts. Top notch prep including SAS Institute A00-240 exam dumps, study guide & practice test questions and answers.
90 Students Enrolled
11:43:00 Hours
A00-240: SAS Statistical Business Analysis Using SAS 9: Regression and Modeling Certification Video Training Course Exam Curriculum
1
Free cloud-based SAS software option for learning: SAS OnDemand for Academics
4 Lectures
Time 00:23:00
2
Analysis of Variance (ANOVA)
18 Lectures
Time 01:59:00
3
Prepare Inputs Vars for predictive Modeling
14 Lectures
Time 02:01:00
4
Linear Regression Analysis
17 Lectures
Time 02:54:00
5
Logistic Regression Analysis
18 Lectures
Time 02:16:00
6
Measure of Model Performance
16 Lectures
Time 02:10:00
Free cloud-based SAS software option for learning: SAS OnDemand for Academics
-
3:00
1. Create a SAS account to access SAS ondemand for Academics
-
6:00
2. Upload course data files and SAS programs into SAS ondemand for academics
-
7:00
3. change file path/directory in SAS ondemand for academics
-
7:00
4. examples: update and run SAS programs in SAS ondemand for academics
Analysis of Variance (ANOVA)
-
10:00
1. ANOVA 0. Using TTEST to compare means
-
3:00
2. Using Proc Univariate to Test the Normality Assumption Using the K-S Test
-
10:00
3. ANOVA 1. One-factor ANOVA model and Test Statistic in PowerPoint Presentation
-
7:00
4. ANOVA 2. The GLM Procedure for Investigating Mean Differences
-
4:00
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
-
3:00
7. ANOVA 5. The Normality Assumption and the PLOTS Option in Proc GLM
-
4:00
8. ANOVA 6. Levene’s Test for Equal Variances and the MEANS Statement in Proc GLM
-
12:00
9. ANOVA 7. Post Hoc Tests: The Tukey-Kramer Procedure and the MEANS Statement
-
10:00
10. ANOVA 8. Other Post Hoc Procedures, the LSMEANS Statement, and the Diffogram
-
16:00
11. ANOVA 9. the Randomized Block Design with example and Interpretation
-
3:00
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
-
5:00
14. ANOVA 12. Unbalanced Designs, the LSMEANS Statement and Type III Sums of Squares
-
8:00
15. ANOVA 13. Two factor ANOVA: overview in PowerPoint Presentation
-
11:00
16. ANOVA 14. Example and Interpretation of the Two-Factor ANOVA
-
3:00
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
Prepare Inputs Vars for predictive Modeling
-
6:00
1. Prepare Inputs Vars_1. Chapter Overview
-
13:00
2. Prepare Inputs Vars_2. Missing values and imputation
-
5:00
3. Prepare Inputs Vars_3.Categorical Input Variable_1.Knowledge points
-
7:00
4. Prepare Inputs Vars_3. Categorical Input Variables_2. Proc freq and Proc Means
-
8:00
5. Prepare Inputs Vars_3. Categorical Input Variables_3. Proc Cluster
-
6:00
6. Prepare Inputs Vars_3. Categorical Input Variables_4. Cut off point
-
10:00
7. Prepare Inputs Vars_3. Categorical Input Variables_5. cluster var
-
11:00
8. Prepare Inputs Vars_4. Variable Cluster_1. Slides on VARCLUS for redundancy
-
19:00
9. Prepare Inputs Vars_4. Variable Cluster_2. Proc VARCLUS for reduce redundancy
-
5:00
10. Prepare Inputs Vars_5. Variable Screening_1. Overview on Knowledge Points
-
8:00
11. Prepare Inputs Vars_5. Variable Screening_2. Proc CORR detect Association_Part A
-
6:00
12. Prepare Inputs Vars_5. Variable Screening_3. Proc CORR detect Association_Part B
-
7:00
13. Prepare Inputs Vars_5. Variable Screening_4. Proc CORR detect Association_Part C
-
10:00
14. Prepare Inputs Vars_5. Variable Screening_5. Empirical Logit detect Non-Linear
Linear Regression Analysis
-
10:00
1. Exploring the Relationship between Two Continuous Variables using Scatter Plots
-
15:00
2. Producing Correlation Coefficients Using the CORR Procedure
-
10:00
3. Multiple Linear Regression: fit multiple regression with Proc REG
-
6:00
4. Multiple Linear Regression: Measures of fit
-
3:00
5. Multiple Linear Regression: Quantifying the Relative Impact of a Predictor
-
11:00
6. Multiple Linear Regression: Check Collinearity Using VIF, COLLIN, and COLLINOINT
-
15:00
7. fit simple linear regression with Proc GLM
-
12:00
8. Multiple Linear Reg: Var Selection With Proc REG:all possible subset: adjust R2
-
6:00
9. Multiple Linear Reg: Var Selection With Proc REG:all possible subset: Mallows Cp
-
8:00
10. Multiple Linear Regression:Variable Selection With Proc REG:Backward Elimination
-
9:00
11. Multiple Linear Regression:Variable Selection With Proc REG: Forward selection
-
4:00
12. Multiple Linear Regression:Variable Selection With Proc REG: Stepwise selection
-
15:00
13. Multiple Linear Regression:Variable Selection With Proc GLMSELECT
-
8:00
14. Multiple Linear Regression: PowerPoint Slides on regression assumptions
-
13:00
15. Multiple Linear Regression: regression assumptions
-
11:00
16. Multiple Linear Regression: PowerPoint Slides on influential observations
-
18:00
17. Multiple Linear Regression: Using statistics to identify influential observation
Logistic Regression Analysis
-
10:00
1. Logistic Regression Analysis: Overview
-
5:00
2. logistic regression with a continuous numeric predictor Part 1
-
15:00
3. logistic regression with a continuous numeric predictor Part 2
-
5:00
4. Plots for Probabilities of an Event
-
6:00
5. Plots of the Odds Ratio
-
10:00
6. logistic regression with a categorical predictor: Effect Coding Parameterization
-
5:00
7. logistic reg with categorical predictor: Reference Cell Coding Parameterization
-
8:00
8. Multiple Logistic Regression: full model SELECTION=NONE
-
8:00
9. Multiple Logistic Regression: Backward Elimination
-
6:00
10. Multiple Logistic Regression: Forward Selection
-
7:00
11. Multiple Logistic Regression: Stepwise Selection
-
12:00
12. Multiple Logistic Regression: Customized Options
-
5:00
13. Multiple Logistic Regression: Best Subset Selection
-
14:00
14. Multiple Logistic Regression: model interaction
-
6:00
15. Multiple Logistic Reg: Scoring New Data: SCORE Statement with PROC LOGISTIC
-
5:00
16. Multiple Logistic Reg: Scoring New Data: Using the PLM Procedure
-
4:00
17. Multiple Logistic Reg: Scoring New Data: the CODE Statement within PROC LOGISTIC
-
5:00
18. Multiple Logistic Reg: Score New Data: OUTMODEL & INMODEL Options with Logistic
Measure of Model Performance
-
10:00
1. Measure of Model Performance: Overview
-
10:00
2. PROC SURVEYSELECT for Creating Training and Validation Data Sets
-
7:00
3. Measures of Performance Using the Classification Table: PowerPoint Presentation
-
10:00
4. Using The CTABLE Option in Proc Logistic for Producing Classification Results
-
4:00
5. Assessing the Performance & Generalizability of a Classifier: PowerPoint slides
-
11:00
6. The Effect of Cutoff Values on Sensitivity and Specificity Estimates
-
7:00
7. Measure of Performance Using the Receiver-Operator-Characteristic (ROC) Curve
-
5:00
8. Model Comparison Using the ROC and ROCCONTRAST Statements
-
11:00
9. Measures of Performance Using the Gains Charts
-
4:00
10. Measures of Performance Using the Lift Charts
-
16:00
11. Adjust for Oversample: PEVENT Option for Priors & Manually adjust Classification
-
5:00
12. Manually Adjusting Posterior Probabilities to Account for Oversampling
-
7:00
13. Manually Adjusted Intercept Using the Offset to account for oversampling
-
6:00
14. Automatically Adjusted Posterior Probabilities to Account for Oversampling
-
12:00
15. Decision Theory: Decision Cutoffs and Expected Profits for Model Selection
-
5:00
16. Decision Theory: Using Estimated Posterior Probabilities to Determine Cutoffs
About A00-240: SAS Statistical Business Analysis Using SAS 9: Regression and Modeling Certification Video Training Course
A00-240: SAS Statistical Business Analysis Using SAS 9: Regression and Modeling certification video training course by prepaway along with practice test questions and answers, study guide and exam dumps provides the ultimate training package to help you pass.
Prepaway's A00-240: SAS Statistical Business Analysis Using SAS 9: Regression and Modeling video training course for passing certification exams is the only solution which you need.
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A00-240 Premium Bundle
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- Premium File 98 Questions & Answers. Last update: Apr 17, 2024
- Training Course 87 Lectures
- Study Guide 895 Pages
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