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- Business Analyst
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How to Prepare For A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam
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.
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SASInstitute A00-240 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Logistic Regression | - Model fit and diagnostics - Binary logistic regression |
| Topic 2: Simple Linear Regression | - Residual analysis - Model assumptions and interpretation - Estimation and inference |
| Topic 3: Model Diagnostics and Validation | - Goodness-of-fit measures - Influence and leverage |
| Topic 4: ANOVA and General Linear Models | - One-way and multi-way ANOVA - GLM procedures in SAS |
| Topic 5: Multiple Regression | - Interaction effects - Model building and selection - Multicollinearity diagnostics |








