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

"SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential", also known as A00-240 exam, is a SASInstitute Certification. With the complete collection of questions and answers, ExamcollectionPass has assembled to take you through 100 Q&As to your A00-240 Exam preparation. In the A00-240 exam resources, you will cover every field and category in SAS Institute Systems Certification Certification helping to ready you for your successful SASInstitute Certification.

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  • Exam Code: A00-240
  • Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
  • Certification Provider: SASInstitute
  • Corresponding Certification: SAS Institute Systems Certification
  • Updated: Aug 30, 2026
  • No. of Questions: 100 Questions & Answers with Testing Engine
  • Download Limit: Unlimited
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How to book A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exams

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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SASInstitute A00-240 Exam Syllabus Topics:

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

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