Learn with SASInstitute : A00-240 training material for 100% pass

Updated: Aug 27, 2026

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How to study the A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam

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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.

SASInstitute A00-240 Exam Overview:

Certification Vendor:SAS Institute
Exam Name:SAS Statistical Business Analysis Using SAS 9: Regression and Modeling
Exam Number:A00-240
Real Exam Qty:60 scored questions (+ up to 5 unscored questions)
Exam Price:USD 180
Certificate Validity Period:No expiration specified by SAS
Related Certifications:SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling
Exam Format:Short Answer, Multiple Choice
Available Languages:English
Exam Duration:110 minutes
Passing Score:68%
Sample Questions:SASInstitute A00-240 Sample Questions
Exam Way:Pearson VUE testing centers and SAS-authorized online exam delivery where available.
Pre Condition:No formal prerequisite exam is required. Candidates should have experience using SAS/STAT software for statistical analysis, regression modeling and predictive analytics.
Official Syllabus URL:https://www.sas.com/en_gb/certification/credentials/advanced-analytics/statistical-business-analyst.html

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.

For more info visit:

Predictive Modeling Using Logistic Regression Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression

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

SASInstitute A00-240 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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
- Validation and Selection
  • 1. Compare and select predictive models
  • 2. Perform model validation using training and validation data
- Performance Visualization
  • 1. Create and interpret lift charts
  • 2. Create and interpret ROC charts
  • 3. Create and interpret gains charts
Topic 2: Prepare Inputs for Predictive Model Performance20%- 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
- Variable Engineering and Screening
  • 1. Screen variables for irrelevance
  • 2. Use empirical logit plots for nonlinearity detection
  • 3. Improve predictive power of categorical variables
  • 4. Identify nonlinear associations using CORR procedure
Topic 3: 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. Perform logistic regression using the LOGISTIC procedure
  • 2. Optimize model performance through input selection
Topic 4: Linear Regression20%- 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
- Build Regression Models
  • 1. Fit multiple linear regression models
  • 2. Use REG and GLM procedures
Topic 5: ANOVA10%- ANOVA Concepts and Assumptions
  • 1. Verify ANOVA assumptions
  • 2. Identify appropriate ANOVA methods
- ANOVA Analysis
  • 1. Detect and analyze factor interactions
  • 2. Analyze differences between population means using GLM and TTEST procedures
  • 3. Evaluate treatment effects
  • 4. Perform post hoc tests

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