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

A00-240
  • Exam Code: A00-240
  • Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
  • Updated: Aug 27, 2026
  • Q & A: 100 Questions and Answers
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Predictive Modeling Using Logistic Regression Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression

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What is the duration, language, and format of A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam

  • Passing score: 68% (Must achieve score of 68 percent correct to pass)
  • 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)
  • Format: Multiple choices, multiple answers , short answers
  • Length of Examination: 120 minutes
  • 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

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 Analysis
  • 1. Perform post hoc tests
  • 2. Evaluate treatment effects
  • 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 2: Logistic Regression25%- Develop Logistic Models
  • 1. Perform logistic regression using the LOGISTIC procedure
  • 2. Optimize model performance through input selection
- Interpret and Deploy Models
  • 1. Score new data sets using LOGISTIC and PLM procedures
  • 2. Interpret LOGISTIC procedure output
Topic 3: Prepare Inputs for Predictive Model Performance20%- Variable Engineering and Screening
  • 1. Identify nonlinear associations using CORR procedure
  • 2. Improve predictive power of categorical variables
  • 3. Use empirical logit plots for nonlinearity detection
  • 4. Screen variables for irrelevance
- 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
Topic 4: Linear Regression20%- Build Regression Models
  • 1. Use REG and GLM procedures
  • 2. Fit multiple linear regression models
- Model Selection and Diagnostics
  • 1. Perform model selection using REG or GLMSELECT
  • 2. Assess model validity
  • 3. Conduct residual and diagnostic analysis
- Interpret Regression Results
  • 1. Interpret REG, PLM and GLM output
  • 2. Evaluate parameter estimates
Topic 5: Measure Model Performance25%- Decision Optimization
  • 1. Evaluate scoring strategies
  • 2. Establish effective decision cutoff values
- Performance Visualization
  • 1. Create and interpret lift charts
  • 2. Create and interpret ROC charts
  • 3. Create and interpret gains charts
- Model Assessment
  • 1. Apply principles of honest assessment
  • 2. Assess classifier performance using confusion matrices
- Validation and Selection
  • 1. Compare and select predictive models
  • 2. Perform model validation using training and validation data

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