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

Pass A00-240 Exam Cram

Exam Code: A00-240

Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential

Updated: Sep 08, 2026

Q & A: 100 Questions and Answers

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What is the duration, language, and format of A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam
  • Length of Examination: 120 minutes
  • Number of Questions: 60 multiple-choice and short-answer questions(In addition to the 60 scored items, there may be up to five unscored items)
  • Passing score: 68% (Must achieve score of 68 percent correct to pass)
  • Format: Multiple choices, multiple answers , short answers
  • Language: Czech, simplified Chinese, traditional Chinese, Dutch, English (UK), English (US), French, German, Indonesian, Italian, Japanese, Korean, Polish, Portuguese, Portuguese (Brazil), Russian, Slovak, Spanish, Spanish (Latin America), Thai, Turkish, and Vietnamese
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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

SASInstitute A00-240 Exam Syllabus Topics:
SectionWeightObjectives
Topic 1: Linear Regression20%- Interpret Regression Results
  • 1. Interpret REG, PLM and GLM output
  • 2. Evaluate parameter estimates
- Model Selection and Diagnostics
  • 1. Conduct residual and diagnostic analysis
  • 2. Assess model validity
  • 3. Perform model selection using REG or GLMSELECT
- Build Regression Models
  • 1. Fit multiple linear regression models
  • 2. Use REG and GLM procedures
Topic 2: 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 3: 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 4: Prepare Inputs for Predictive Model Performance20%- Data Preparation
  • 1. Identify challenges in preparing model input data
  • 2. Manipulate data using DATA step programming
  • 3. Use loops, arrays, functions and conditional statements
- Variable Engineering and Screening
  • 1. Identify nonlinear associations using CORR procedure
  • 2. Use empirical logit plots for nonlinearity detection
  • 3. Improve predictive power of categorical variables
  • 4. Screen variables for irrelevance
Topic 5: Measure Model Performance25%- Validation and Selection
  • 1. Perform model validation using training and validation data
  • 2. Compare and select predictive models
- Performance Visualization
  • 1. Create and interpret ROC charts
  • 2. Create and interpret gains charts
  • 3. Create and interpret lift charts
- Decision Optimization
  • 1. Establish effective decision cutoff values
  • 2. Evaluate scoring strategies
- Model Assessment
  • 1. Apply principles of honest assessment
  • 2. Assess classifier performance using confusion matrices
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