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

  • Exam Code: A00-240
  • Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
  • Updated: Oct 19, 2026
  • Q&As: 100 Questions and Answers

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Value Pack Total: $149.97  $69.99

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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.

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

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

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

How much A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam cost

A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam cost is $180.

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SASInstitute A00-240 Exam Syllabus Topics:

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

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