The Complete SAS Prep Course: Statistical Business Analyst using SAS 9.4 on Regression and Modeling (exam ID A00-240)


What you'll learn
the most essential data analyses topics (ANOVA, Linear Regression , Logistic Regression, predictive modeling )
predictive modeling (data prep for predictive modeling, sampling for training & validation data, modeling, validation, scoring, measuring model performance)
Write SAS programs to generate and make conclusions and interpretations on major statistical outputs and results
Be completely prepared for to obtain the SAS certification: SAS® Certified Statistical Business Analyst Using SAS®9: Regression and Modeling (exam ID A00-240).

Requirements
basic SAS programming skills; basic statistics knowledge

Description
This course is for anyone who wants to move up their careers by equipping themselves with the critical analytical skills.Course Highlights:includes the most essential data analyses topics ( Analysis of Variance, Prepare data for predictive Modeling, Linear Regression, Logistic Regression, Predictive Modeling & Measure of Model Performance )utilizes step by step/ code by code explanations for all SAS programs; presents statistical knowledges in PowerPoint presentations; provides detailed explanations on all statistical outputsshows the complete process of predictive modeling (data preparation for predictive modeling, sampling for training and validation data, modeling, validation, scoring and measuring model performance)It is also a Complete Prep Course for SAS® Certified Statistical Business Analyst Using SAS®9: Regression and Modeling (exam ID A00-240).Data, SAS programs and PowerPoint slides used in the course are downloadable in lecture 4 (the course materials are ONLY for practice, they are protected by copyright)Quizzes at the end of each section to test what you have learnedA Note on Course ratings and reviews:Please be sincere and considerate when you provide ratings and reviews. As you may know, this is crucial to an online instructor like me. And it will encourage me providing more contents to the course and better service to you! So Please provide fair ratings to this course with the consideration of the comparison among other available SAS courses. Thank you!References:SAS Certification Prep Guide, Statistical Business Analysis Using SAS9Note: The course was created with SAS software license for the SAS University Edition (the downloadable SAS studio version).The course is also suitable to use with SAS OnDemand for Academics (the web-based SAS studio version). The software interface/appearance and functionalities in the two SAS studio versions are the same. Section 2 has all the details for using SAS OnDemand for Academics with this courses.

Overview
Section 1: Course Overview and downloadable course materials
Lecture 1 Course Overview
Lecture 2 Downloadable course materials
Section 2: Use the free web-based SAS studio "SAS OnDemand for Academics" with this course
Lecture 3 Access free SAS software "SAS OnDemand for Academics" step by step instruction
Lecture 4 Upload course data files and SAS programs into SAS ondemand for academics
Lecture 5 change file path/directory in SAS ondemand for academics
Lecture 6 examples: update and run SAS programs in SAS ondemand for academics
Section 3: Analysis of Variance (ANOVA)
Lecture 7 ANOVA 0. Using TTEST to compare means
Lecture 8 Using Proc Univariate to Test the Normality Assumption Using the K-S Test
Lecture 9 ANOVA 1. One-factor ANOVA model and Test Statistic in PowerPoint Presentation
Lecture 10 ANOVA 2. The GLM Procedure for Investigating Mean Differences
Lecture 11 ANOVA 3. generate Predicted Values & Residuals Use OUTPUT Statement in Proc GLM
Lecture 12 ANOVA 4. Measures of fit: output explanation of one-way ANOVA
Lecture 13 ANOVA 5. The Normality Assumption and the PLOTS Option in Proc GLM
Lecture 14 ANOVA 6. Levene's Test for Equal Variances and the MEANS Statement in Proc GLM
Lecture 15 ANOVA 7. Post Hoc Tests: The Tukey-Kramer Procedure and the MEANS Statement
Lecture 16 ANOVA 8. Other Post Hoc Procedures, the LSMEANS Statement, and the Diffogram
Lecture 17 ANOVA 9. the Randomized Block Design with example and Interpretation
Lecture 18 ANOVA 10. Randomized block design: Post Hoc Tests Using the LSMEANS Statement
Lecture 19 ANOVA 11. Assess Assumptions of a Randomized Block Design Using the PLOTS Option
Lecture 20 ANOVA 12. Unbalanced Designs, the LSMEANS Statement and Type III Sums of Squares
Lecture 21 ANOVA 13. Two factor ANOVA: overview in PowerPoint Presentation
Lecture 22 ANOVA 14. Example and Interpretation of the Two-Factor ANOVA
Lecture 23 ANOVA 15. Analyze Simple Effects When Interaction Exists Use LSMEANS with Slice
Lecture 24 ANOVA 16. Assessing the Assumptions of a Two-Factor Analysis of Variance
Section 4: Prepare Inputs Vars for predictive Modeling
Lecture 25 Prepare Inputs Vars_1. Chapter Overview
Lecture 26 Prepare Inputs Vars_2. Missing values and imputation
Lecture 27 Prepare Inputs Vars_3.Categorical Input Variable_1.Knowledge points
Lecture 28 Prepare Inputs Vars_3. Categorical Input Variables_2. Proc freq and Proc Means
Lecture 29 Prepare Inputs Vars_3. Categorical Input Variables_3. Proc Cluster
Lecture 30 Prepare Inputs Vars_3. Categorical Input Variables_4. Cut off point
Lecture 31 Prepare Inputs Vars_3. Categorical Input Variables_5. cluster var
Lecture 32 Prepare Inputs Vars_4. Variable Cluster_1. Slides on VARCLUS for redundancy
Lecture 33 Prepare Inputs Vars_4. Variable Cluster_2. Proc VARCLUS for reduce redundancy
Lecture 34 Prepare Inputs Vars_5. Variable Screening_1. Overview on Knowledge Points
Lecture 35 Prepare Inputs Vars_5. Variable Screening_2. Proc CORR detect Association_Part A
Lecture 36 Prepare Inputs Vars_5. Variable Screening_3. Proc CORR detect Association_Part B
Lecture 37 Prepare Inputs Vars_5. Variable Screening_4. Proc CORR detect Association_Part C
Lecture 38 Prepare Inputs Vars_5. Variable Screening_5. Empirical Logit detect Non-Linear
Section 5: Linear Regression Analysis
Lecture 39 Exploring the Relationship between Two Continuous Variables using Scatter Plots
Lecture 40 Producing Correlation Coefficients Using the CORR Procedure
Lecture 41 Multiple Linear Regression: fit multiple regression with Proc REG
Lecture 42 Multiple Linear Regression: Measures of fit
Lecture 43 Multiple Linear Regression: Quantifying the Relative Impact of a Predictor
Lecture 44 Multiple Linear Regression: Check Collinearity Using VIF, COLLIN, and COLLINOINT
Lecture 45 fit simple linear regression with Proc GLM
Lecture 46 Multiple Linear Reg: Var Selection With Proc REG:all possible subset: adjust R2
Lecture 47 Multiple Linear Reg: Var Selection With Proc REG:all possible subset: Mallows Cp
Lecture 48 Multiple Linear Regression:Variable Selection With Proc REG:Backward Elimination
Lecture 49 Multiple Linear Regression:Variable Selection With Proc REG: Forward selection
Lecture 50 Multiple Linear Regression:Variable Selection With Proc REG: Stepwise selection
Lecture 51 Multiple Linear Regression:Variable Selection With Proc GLMSELECT
Lecture 52 Multiple Linear Regression: PowerPoint Slides on regression assumptions
Lecture 53 Multiple Linear Regression: regression assumptions
Lecture 54 Multiple Linear Regression: PowerPoint Slides on influential observations
Lecture 55 Multiple Linear Regression: Using statistics to identify influential observation
Section 6: Logistic Regression Analysis
Lecture 56 Logistic Regression Analysis: Overview
Lecture 57 logistic regression with a continuous numeric predictor Part 1
Lecture 58 logistic regression with a continuous numeric predictor Part 2
Lecture 59 Plots for Probabilities of an Event
Lecture 60 Plots of the Odds Ratio
Lecture 61 logistic regression with a categorical predictor: Effect Coding Parameterization
Lecture 62 logistic reg with categorical predictor: Reference Cell Coding Parameterization
Lecture 63 Multiple Logistic Regression: full model SELECTION=NONE
Lecture 64 Multiple Logistic Regression: Backward Elimination
Lecture 65 Multiple Logistic Regression: Forward Selection
Lecture 66 Multiple Logistic Regression: Stepwise Selection
Lecture 67 Multiple Logistic Regression: Customized Options
Lecture 68 Multiple Logistic Regression: Best Subset Selection
Lecture 69 Multiple Logistic Regression: model interaction
Lecture 70 Multiple Logistic Reg: Scoring New dаta: SCORE Statement with PROC LOGISTIC
Lecture 71 Multiple Logistic Reg: Scoring New dаta: Using the PLM Procedure
Lecture 72 Multiple Logistic Reg: Scoring New dаta: the CODE Statement within PROC LOGISTIC
Lecture 73 Multiple Logistic Reg: Score New dаta: OUTMODEL & INMODEL Options with Logistic
Section 7: Measure of Model Performance
Lecture 74 Measure of Model Performance: Overview
Lecture 75 PROC SURVEYSELECT for Creating Training and Validation Data Sets
Lecture 76 Measures of Performance Using the Classification Table: PowerPoint Presentation
Lecture 77 Using The CTABLE Option in Proc Logistic for Producing Classification Results
Lecture 78 Assessing the Performance & Generalizability of a Classifier: PowerPoint slides
Lecture 79 The Effect of Cutoff Values on Sensitivity and Specificity Estimates
Lecture 80 Measure of Performance Using the Receiver-Operator-Characteristic (ROC) Curve
Lecture 81 Model Comparison Using the ROC and ROCCONTRAST Statements
Lecture 82 Measures of Performance Using the Gains Charts
Lecture 83 Measures of Performance Using the Lift Charts
Lecture 84 Adjust for Oversample: PEVENT Option for Priors & Manually adjust Classification
Lecture 85 Manually Adjusting Posterior Probabilities to Account for Oversampling
Lecture 86 Manually Adjusted Intercept Using the Offset to account for oversampling
Lecture 87 Automatically Adjusted Posterior Probabilities to Account for Oversampling
Lecture 88 Decision Theory: Decision Cutoffs and Expected Profits for Model Selection
Lecture 89 Decision Theory: Using Estimated Posterior Probabilities to Determine Cutoffs
anyone who wants to move up their careers by equipping themselves with the critical analytical skills,anyone who is interested in learning the most essential data analyses topics (ANOVA, Linear Regression , Logistic Regression, predictive modeling ),anyone who wants to master the complete process of predictive modeling (data preparation for predictive modeling, sampling for training & validation data, modeling, validation, scoring, measuring model performance),anyone who wants to be able to write SAS programs to generate and make conclusions and interpretations on major statistical outputs and results,anyone who wants to be completely prepared for to obtain the SAS certification: SAS® Certified Statistical Business Analyst Using SAS®9: Regression and Modeling (exam ID A00-240)
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