SASInstitute A00-240 Exam Details & Actual Exam Questions

Exam Code/Number: A00-240
Exam Name/Title: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
Certification Provider: SASInstitute
Corresponding Certification: SAS Institute Systems Certification

Exam Questions: 100
Updated On: Jul,15 2026
Certification Level: Professional

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

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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
Exam Duration:	110 minutes
Related Certifications:	SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling
Available Languages:	English
Passing Score:	68%
Exam Format:	Multiple Choice, Short Answer
Exam Price:	USD 180
Real Exam Qty:	60 scored questions (+ up to 5 unscored questions)
Certificate Validity Period:	No expiration specified by SAS
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

SASInstitute A00-240 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Linear Regression	20%	- Model Selection and Diagnostics 1. Assess model validity 2. Perform model selection using REG or GLMSELECT 3. Conduct residual and diagnostic analysis - Interpret Regression Results 1. Evaluate parameter estimates 2. Interpret REG, PLM and GLM output - Build Regression Models 1. Use REG and GLM procedures 2. Fit multiple linear regression models
Topic 2: Logistic Regression	25%	- 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 3: Prepare Inputs for Predictive Model Performance	20%	- Variable Engineering and Screening 1. Use empirical logit plots for nonlinearity detection 2. Identify nonlinear associations using CORR procedure 3. Improve predictive power of categorical variables 4. Screen variables for irrelevance - Data Preparation 1. Manipulate data using DATA step programming 2. Identify challenges in preparing model input data 3. Use loops, arrays, functions and conditional statements
Topic 4: ANOVA	10%	- 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. Verify ANOVA assumptions 2. Identify appropriate ANOVA methods
Topic 5: Measure Model Performance	25%	- Validation and Selection 1. Compare and select predictive models 2. Perform model validation using training and validation data - Model Assessment 1. Assess classifier performance using confusion matrices 2. Apply principles of honest assessment - Decision Optimization 1. Establish effective decision cutoff values 2. Evaluate scoring strategies - Performance Visualization 1. Create and interpret lift charts 2. Create and interpret gains charts 3. Create and interpret ROC charts