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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.
- Step 1 : Visit to website Pearson VUE
- Step 2 : Navigate To Certifications
- Step 3 : Search for the exam name A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling
- Step 4 : Book the exam.
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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.
SASInstitute A00-240 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Linear Regression | 20% | - Model Selection and Diagnostics
|
| Prepare Inputs for Predictive Model Performance | 20% | - Data Preparation
|
| Measure Model Performance | 25% | - Model Assessment
|
| ANOVA | 10% | - ANOVA Analysis
|
| Logistic Regression | 25% | - Develop Logistic Models
|
