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

  • Exam Code: A00-240
  • Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
  • Updated: Sep 04, 2026
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Predictive Modeling Using Logistic Regression Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression

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  • India - 8,42,327 INR
  • England - 46,632 POUND
  • Europe - 45,347 EURO

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

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

According to SAS, SAS® Certified Statistical Business Analyst for SAS®9 this credentials signifies the upper echelon of SAS programmers who demonstrate a high level of proficiency in SAS® programming expertise.

A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam is a certification exam that is conducted by Google to validates candidate knowledge and skills of working as a Statistical Business Analyst in the Company.

A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling exam covers the basic and advanced concepts of variance analysis, linear and logistic regression, preparing inputs for predictive models and measuring model performance.

After passing this exam, candidates get a certificate from SAS that helps them to demonstrate their proficiency in A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling to their clients and employers.

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SASInstitute A00-240 Exam Syllabus Topics:

SectionWeightObjectives
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
Prepare Inputs for Predictive Model Performance20%- 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
- 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
Linear Regression20%- Build Regression Models
  • 1. Fit multiple linear regression models
  • 2. Use REG and GLM procedures
- Model Selection and Diagnostics
  • 1. Conduct residual and diagnostic analysis
  • 2. Perform model selection using REG or GLMSELECT
  • 3. Assess model validity
- Interpret Regression Results
  • 1. Evaluate parameter estimates
  • 2. Interpret REG, PLM and GLM output
Measure Model Performance25%- 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
- Performance Visualization
  • 1. Create and interpret lift charts
  • 2. Create and interpret ROC charts
  • 3. Create and interpret gains charts
ANOVA10%- ANOVA Analysis
  • 1. Perform post hoc tests
  • 2. Analyze differences between population means using GLM and TTEST procedures
  • 3. Detect and analyze factor interactions
  • 4. Evaluate treatment effects
- ANOVA Concepts and Assumptions
  • 1. Identify appropriate ANOVA methods
  • 2. Verify ANOVA assumptions

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