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

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

BEST SELLER 79 Lectures 10h 29m

Prepare for your SAS Institute examination with our training course. The A00-240 course contains a complete batch of videos that will provide you with profound and thorough knowledge related to SAS Institute certification exam. Pass the SAS Institute A00-240 test with flying colors.

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Curriculum For This Course

4 lectures 23m
  • 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

18 lectures 1h 59m
  • 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

6 lectures 47m
  • 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

17 lectures 2h 54m
  • 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

18 lectures 2h 16m
  • 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

16 lectures 2h 10m
  • 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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