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SAS programming Statistical Analyst Certification Course

Name: SAS programming Statistical Analyst Certification Course
Rating: 4.6 (166 reviews)

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

Created bySharon Cheng

Last updated 4/2025

English

What you'll learn

This course will prepare you for SAS certification (exam ID A00-240): SAS® Certified Statistical Business Analyst Using SAS®9: Regression and Modeling
Analysis of variance
Linear and logistic regression
Preparing inputs for predictive models
Measuring model performance

Course content

7 sections • 89 lectures • 11h 56m total length

Course Overview2:28
Downloadable course materials1:26

ANOVA 0. Using TTEST to compare means9:42
Using Proc Univariate to Test the Normality Assumption Using the K-S Test2:39
ANOVA 1. One-factor ANOVA model and Test Statistic in PowerPoint Presentation10:07
ANOVA 2. The GLM Procedure for Investigating Mean Differences7:01
ANOVA 3. generate Predicted Values & Residuals Use OUTPUT Statement in Proc GLM3:55
ANOVA 4. Measures of fit: output explanation of one-way ANOVA4:01
ANOVA 5. The Normality Assumption and the PLOTS Option in Proc GLM3:21
ANOVA 6. Levene’s Test for Equal Variances and the MEANS Statement in Proc GLM4:08
ANOVA 7. Post Hoc Tests: The Tukey-Kramer Procedure and the MEANS Statement11:36
ANOVA 8. Other Post Hoc Procedures, the LSMEANS Statement, and the Diffogram9:30
ANOVA 9. the Randomized Block Design with example and Interpretation16:27
ANOVA 10. Randomized block design: Post Hoc Tests Using the LSMEANS Statement3:14
ANOVA 11. Assess Assumptions of a Randomized Block Design Using the PLOTS Option3:04
ANOVA 12. Unbalanced Designs, the LSMEANS Statement and Type III Sums of Squares4:37
ANOVA 13. Two factor ANOVA: overview in PowerPoint Presentation7:47
ANOVA 14. Example and Interpretation of the Two-Factor ANOVA10:42
ANOVA 15. Analyze Simple Effects When Interaction Exists Use LSMEANS with Slice3:23
the complete description for this lecture:
Analyzing Simple Effects When Interaction Exists Using the LSMEANS Statement with the SLICE Option
ANOVA 16. Assessing the Assumptions of a Two-Factor Analysis of Variance2:50
Quiz: Analysis of Variance (ANOVA)

Prepare Inputs Vars_1. Chapter Overview6:04
Prepare Inputs Vars_2. Missing values and imputation13:18
Prepare Inputs Vars_3.Categorical Input Variable_1.Knowledge points4:56
Prepare Inputs Vars_3. Categorical Input Variables_2. Proc freq and Proc Means7:29
Prepare Inputs Vars_3. Categorical Input Variables_3. Proc Cluster8:19
Prepare Inputs Vars_3. Categorical Input Variables_4. Cut off point5:42
Prepare Inputs Vars_3. Categorical Input Variables_5. cluster var9:36
Prepare Inputs Vars_4. Variable Cluster_1. Slides on VARCLUS for redundancy10:39
Prepare Inputs Vars_4. Variable Cluster_2. Proc VARCLUS for reduce redundancy18:39
Prepare Inputs Vars_5. Variable Screening_1. Overview on Knowledge Points4:45
Prepare Inputs Vars_5. Variable Screening_2. Proc CORR detect Association_Part A8:21
Prepare Inputs Vars_5. Variable Screening_3. Proc CORR detect Association_Part B6:14
Prepare Inputs Vars_5. Variable Screening_4. Proc CORR detect Association_Part C7:25
Prepare Inputs Vars_5. Variable Screening_5. Empirical Logit detect Non-Linear9:31
Quiz: Prepare Inputs Vars

Exploring the Relationship between Two Continuous Variables using Scatter Plots9:58
Producing Correlation Coefficients Using the CORR Procedure14:45
Multiple Linear Regression: fit multiple regression with Proc REG10:17
Multiple Linear Regression: Measures of fit6:17
Multiple Linear Regression: Quantifying the Relative Impact of a Predictor3:26
Multiple Linear Regression: Check Collinearity Using VIF, COLLIN, and COLLINOINT11:05
fit simple linear regression with Proc GLM15:22
Multiple Linear Reg: Var Selection With Proc REG:all possible subset: adjust R212:28
Multiple Linear Reg: Var Selection With Proc REG:all possible subset: Mallows Cp6:27
Multiple Linear Regression:Variable Selection With Proc REG:Backward Elimination8:06
Multiple Linear Regression: Variable Selection With Proc REG: Forward selection8:31
Multiple Linear Regression:Variable Selection With Proc REG: Stepwise selection3:44
Multiple Linear Regression: Variable Selection With Proc GLMSELECT15:07
Multiple Linear Regression: PowerPoint Slides on regression assumptions7:45
Multiple Linear Regression: regression assumptions13:07
Multiple Linear Regression: PowerPoint Slides on influential observations11:23
Multiple Linear Regression: Using statistics to identify influential observation18:22
Quiz: Linear Regression Analysis

Logistic Regression Analysis: Overview9:54
logistic regression with a continuous numeric predictor Part 15:20
logistic regression with a continuous numeric predictor Part 215:14
Plots for Probabilities of an Event4:40
Plots of the Odds Ratio5:33
logistic regression with a categorical predictor: Effect Coding Parameterization10:29
logistic reg with categorical predictor: Reference Cell Coding Parameterization4:52
Multiple Logistic Regression: full model SELECTION=NONE7:48
Multiple Logistic Regression: Backward Elimination7:57
Multiple Logistic Regression: Forward Selection6:29
Multiple Logistic Regression: Stepwise Selection6:39
Multiple Logistic Regression: Customized Options12:18
Multiple Logistic Regression: Best Subset Selection5:05
Multiple Logistic Regression: model interaction14:24
Multiple Logistic Reg: Scoring New Data: SCORE Statement with PROC LOGISTIC6:20
Multiple Logistic Reg: Scoring New Data: Using the PLM Procedure5:25
Multiple Logistic Reg: Scoring New Data: the CODE Statement within PROC LOGISTIC4:28
Multiple Logistic Reg: Score New Data: OUTMODEL & INMODEL Options with Logistic5:18
Quiz: Logistic Regression Analysis

Measure of Model Performance: Overview9:31
PROC SURVEYSELECT for Creating Training and Validation Data Sets10:20
Measures of Performance Using the Classification Table: PowerPoint Presentation7:08
Using The CTABLE Option in Proc Logistic for Producing Classification Results9:58
Assessing the Performance & Generalizability of a Classifier: PowerPoint slides3:52
The Effect of Cutoff Values on Sensitivity and Specificity Estimates10:45
Measure of Performance Using the Receiver-Operator-Characteristic (ROC) Curve7:23
Model Comparison Using the ROC and ROCCONTRAST Statements5:17
Measures of Performance Using the Gains Charts10:53
Measures of Performance Using the Lift Charts3:32
Adjust for Oversample: PEVENT Option for Priors & Manually adjust Classification15:48
The contents of this video is (title does not enough space):
Adjustment to Performance Estimates When Oversampling Rare Events:
The PEVENT Option for Defining Prior Probabilities
Manual Adjustment of the Classification Matrix
Manually Adjusting Posterior Probabilities to Account for Oversampling5:20
Manually Adjusted Intercept Using the Offset to account for oversampling6:44
Automatically Adjusted Posterior Probabilities to Account for Oversampling6:18
Decision Theory: Decision Cutoffs and Expected Profits for Model Selection11:32
Decision Theory: Using Estimated Posterior Probabilities to Determine Cutoffs5:06
Quiz: Measure of Model Performance

Requirements

basic SAS programming skills; basic statistics knowledge

Description

This course is designed for anyone preparing to pass the SAS Certified Statistical Business Analyst Using SAS 9.4: Regression and Modeling exam (Exam ID: A00-240). Whether you’re looking to enhance your analytical skills or advance your career, this course provides the essential knowledge and hands-on experience needed to succeed.

Course Highlights

Comprehensive Exam Preparation: A complete guide for the SAS Certified Statistical Business Analyst Using SAS 9.4: Regression and Modeling (Exam ID: A00-240).
Industry-Recognized Certification: This SAS certification is one of the featured credentials offered by SAS, adding value to your professional portfolio.

What You Will Learn

By taking this course, you will gain mastery in:

1. Analysis of Variance (ANOVA)
2. Linear and Logistic Regression
3. Preparing Inputs for Predictive Models
4. Measuring Model Performance

Course Methodology

Step-by-Step Code Explanations: Each SAS program is thoroughly explained, ensuring clarity and understanding.
Structured Learning: Statistical concepts are presented through engaging PowerPoint presentations.
Comprehensive Output Analysis: Detailed explanations of all statistical outputs help solidify your understanding.
End-to-End Predictive Modeling: Covers data preparation, sampling, model building, validation, scoring, and performance measurement.

Additional Course Features

Downloadable Resources: All datasets, SAS programs, and PowerPoint slides used in the course are available for download in Lecture 4 (Note: Materials are for practice only and are protected by copyright).
Quizzes & Assessments: Test your knowledge with quizzes at the end of each section.

Prerequisites

Basic knowledge of statistical analysis is highly recommended for an optimal learning experience.

SAS Software for This Course

This course was originally developed using SAS University Edition but has been fully updated for SAS OnDemand for Academics—a free, web-based version of SAS Studio. The software’s interface, functionality, and appearance remain consistent, ensuring a seamless learning experience.

Detailed instructions for using SAS OnDemand for Academics are provided in Section 2.

Course Ratings & Reviews – A Note from the Instructor

Your honest and thoughtful feedback is highly appreciated! Your ratings help improve course content and enhance your learning experience.

This course is regularly updated to reflect the latest SAS certification changes and incorporate student suggestions, ensuring it remains relevant and valuable. Fair and constructive reviews are essential to improving the course’s quality, and I truly appreciate your honest input. In this field, course ratings above 4.5 are widely regarded as indicators of high-quality content.

Thank you for your support!

References

SAS Certification Prep Guide: Statistical Business Analysis Using SAS 9

Who this course is for:

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)

SAS programming Statistical Analyst Certification Course

What you'll learn

Explore related topics

Course content

Course Overview and downloadable course materials2 lectures • 4min

Use the free web-based SAS studio "SAS OnDemand for Academics" with this course4 lectures • 30min

Analysis of Variance (ANOVA)18 lectures • 1hr 58min

Prepare Inputs Vars for predictive Modeling14 lectures • 2hr 1min

Linear Regression Analysis17 lectures • 2hr 56min

Logistic Regression Analysis18 lectures • 2hr 18min

Measure of Model Performance16 lectures • 2hr 9min

Requirements

Description

Who this course is for: