SASInstitute SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential : A00-240 exam

A00-240
  • Exam Code: A00-240
  • Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
  • Updated: Aug 18, 2026
  • Q & A: 100 Questions and Answers

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About SASInstitute SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential : A00-240 Exam Questions

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A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Certification Path

The A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Certification is one of the highest level of certification mainly focussing to the Advanced statistical analysis.

if you pass your exam and meet all requirements for a credential, you will receive an e-mail from SAS within 72 business hours with instructions providing access to your certificate and logo through the SAS Certification Manager. You will also receive an email from Acclaim with access to your digital badge. For questions regarding access to either of these, please visit SAS Website.

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A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Certified Professional salary

The average salary of a A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Certified Expert in

  • Europe - 45,347 EURO
  • United State - 70,247 USD
  • England - 46,632 POUND
  • India - 8,42,327 INR

Reference: https://www.sas.com/en_us/certification/credentials/advanced-analytics/statistical-business-analyst.html

SASInstitute A00-240 Exam Syllabus Topics:

SectionObjectives
Topic 1: ANOVA and General Linear Models- One-way and multi-way ANOVA
- GLM procedures in SAS
Topic 2: Logistic Regression- Model fit and diagnostics
- Binary logistic regression
Topic 3: Multiple Regression- Interaction effects
- Multicollinearity diagnostics
- Model building and selection
Topic 4: Model Diagnostics and Validation- Goodness-of-fit measures
- Influence and leverage
Topic 5: Simple Linear Regression- Estimation and inference
- Model assumptions and interpretation
- Residual analysis

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