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

  • Exam Code: A00-240
  • Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
  • Updated: Sep 12, 2026
  • Q & A: 100 Questions and Answers

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  • Anyone writing & interested in Statistical Business Analysis
  • Business Analyst
  • Stastical Analyst, or

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

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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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SASInstitute A00-240 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Linear Regression20%- Perform model selection with GLMSELECT
- Interpret REG, GLM, and PLM procedure output
- Fit multiple linear regression models
- Conduct residual and diagnostic analysis
Topic 2: Analysis of Variance (ANOVA)10%- Analyze factor interactions
- Use PROC GLM for ANOVA
- Assess ANOVA assumptions
- Perform post-hoc tests
Topic 3: Logistic Regression25%- Score new datasets with LOGISTIC and PLM
- Interpret odds ratios and model coefficients
- Select and optimize input variables
- Build models using PROC LOGISTIC
Topic 4: Measure and Evaluate Model Performance25%- Create and interpret ROC, lift, and gains charts
- Apply honest assessment principles
- Validate models with training/validation data
- Determine optimal decision cut-off values
- Use confusion matrices and classification metrics
Topic 5: Prepare Inputs for Predictive Modeling20%- Identify data preparation challenges
- Screen variables with CORR procedure and empirical logit plots
- Transform categorical variables
- Manipulate data using DATA step

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