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

A00-240 actual test
  • Exam Code: A00-240
  • Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
  • Updated: Jul 30, 2026
  • Q & A: 100 Questions and Answers
  • PDF Demo
  • PC Test Engine
  • Online Test Engine
  • Total Price: $49.99  

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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

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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 Syllabus Topics:

SectionWeightObjectives
Topic 1: ANOVA10%- ANOVA Analysis
  • 1. Analyze differences between population means using GLM and TTEST procedures
  • 2. Detect and analyze factor interactions
  • 3. Perform post hoc tests
  • 4. Evaluate treatment effects
- ANOVA Concepts and Assumptions
  • 1. Verify ANOVA assumptions
  • 2. Identify appropriate ANOVA methods
Topic 2: Linear Regression20%- Interpret Regression Results
  • 1. Interpret REG, PLM and GLM output
  • 2. Evaluate parameter estimates
- Model Selection and Diagnostics
  • 1. Perform model selection using REG or GLMSELECT
  • 2. Assess model validity
  • 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. Identify nonlinear associations using CORR procedure
  • 3. Screen variables for irrelevance
  • 4. Improve predictive power of categorical variables
- Data Preparation
  • 1. Use loops, arrays, functions and conditional statements
  • 2. Identify challenges in preparing model input data
  • 3. Manipulate data using DATA step programming
Topic 4: Measure Model Performance25%- Performance Visualization
  • 1. Create and interpret gains charts
  • 2. Create and interpret lift charts
  • 3. Create and interpret ROC charts
- Validation and Selection
  • 1. Compare and select predictive models
  • 2. Perform model validation using training and validation data
- Model Assessment
  • 1. Apply principles of honest assessment
  • 2. Assess classifier performance using confusion matrices
- Decision Optimization
  • 1. Evaluate scoring strategies
  • 2. Establish effective decision cutoff values
Topic 5: 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

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