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The registration for the A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam follows the steps given below.
Preparation Guide for A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam
Introduction
SAS (previously “Statistical Analysis System”) may be a package suite designed by SAS Institute for advanced analytics, variable analyses, business intelligence, knowledge management, and prognosticative analytics.
A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling exam is administered by SAS and Pearson VUE. has created a track for IT professionals to certify as business analyst on SAS. This certification program provides SAS professionals a way to demonstrate their skills. The assessment is based on a rigorous exam using industry standard methodology to determine whether a candidate meets SAS's proficiency standards.
SAS® Certified Statistical Business Analyst for SAS®9 this credential is for professionals who use SAS/STAT® software to conduct and interpret complex statistical data analysis.
In this guide, we will cover the A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam, A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Certified professional's salary and all aspects of the A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Certification.
The SAS international Certification Program currently issues versioned accreditation that don't lapse.The version is indicated in the credential name.
Predictive Modeling Using Logistic Regression Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression
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| Section | Objectives |
|---|---|
| ANOVA and General Linear Models | - GLM procedures in SAS - One-way and multi-way ANOVA |
| Simple Linear Regression | - Residual analysis - Estimation and inference - Model assumptions and interpretation |
| Multiple Regression | - Multicollinearity diagnostics - Model building and selection - Interaction effects |
| Model Diagnostics and Validation | - Goodness-of-fit measures - Influence and leverage |
| Logistic Regression | - Binary logistic regression - Model fit and diagnostics |
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