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

  • Exam Code: A00-240
  • Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
  • Updated: Jul 24, 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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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.

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

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SASInstitute A00-240 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Logistic Regression25%- Interpret and Deploy Models
  • 1. Score new data sets using LOGISTIC and PLM procedures
  • 2. Interpret LOGISTIC procedure output
- Develop Logistic Models
  • 1. Optimize model performance through input selection
  • 2. Perform logistic regression using the LOGISTIC procedure
Topic 2: Prepare Inputs for Predictive Model Performance20%- Variable Engineering and Screening
  • 1. Screen variables for irrelevance
  • 2. Improve predictive power of categorical variables
  • 3. Use empirical logit plots for nonlinearity detection
  • 4. Identify nonlinear associations using CORR procedure
- Data Preparation
  • 1. Identify challenges in preparing model input data
  • 2. Use loops, arrays, functions and conditional statements
  • 3. Manipulate data using DATA step programming
Topic 3: Measure Model Performance25%- Validation and Selection
  • 1. Compare and select predictive models
  • 2. Perform model validation using training and validation data
- Model Assessment
  • 1. Assess classifier performance using confusion matrices
  • 2. Apply principles of honest assessment
- Performance Visualization
  • 1. Create and interpret gains charts
  • 2. Create and interpret ROC charts
  • 3. Create and interpret lift charts
- Decision Optimization
  • 1. Evaluate scoring strategies
  • 2. Establish effective decision cutoff values
Topic 4: ANOVA10%- ANOVA Analysis
  • 1. Evaluate treatment effects
  • 2. Perform post hoc tests
  • 3. Analyze differences between population means using GLM and TTEST procedures
  • 4. Detect and analyze factor interactions
- ANOVA Concepts and Assumptions
  • 1. Verify ANOVA assumptions
  • 2. Identify appropriate ANOVA methods
Topic 5: Linear Regression20%- Model Selection and Diagnostics
  • 1. Conduct residual and diagnostic analysis
  • 2. Perform model selection using REG or GLMSELECT
  • 3. Assess model validity
- Build Regression Models
  • 1. Fit multiple linear regression models
  • 2. Use REG and GLM procedures
- Interpret Regression Results
  • 1. Interpret REG, PLM and GLM output
  • 2. Evaluate parameter estimates

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