Last updated 10/2022

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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)

**Homepage**

https://www.udemy.com/course/data-analysis-and-predictive-modeling-using-sas/

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