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

Exam Code: A00-240
Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
Updated: Jul 16, 2026
Q&As: 100 Questions and Answers

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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:

Section	Weight	Objectives
Topic 1: Measure Model Performance	25%	- Decision Optimization 1. Establish effective decision cutoff values 2. Evaluate scoring strategies - 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 - Performance Visualization 1. Create and interpret ROC charts 2. Create and interpret lift charts 3. Create and interpret gains charts
Topic 2: Linear Regression	20%	- 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 - Model Selection and Diagnostics 1. Conduct residual and diagnostic analysis 2. Assess model validity 3. Perform model selection using REG or GLMSELECT
Topic 3: ANOVA	10%	- ANOVA Concepts and Assumptions 1. Identify appropriate ANOVA methods 2. Verify ANOVA assumptions - ANOVA Analysis 1. Perform post hoc tests 2. Detect and analyze factor interactions 3. Analyze differences between population means using GLM and TTEST procedures 4. Evaluate treatment effects
Topic 4: Prepare Inputs for Predictive Model Performance	20%	- Variable Engineering and Screening 1. Screen variables for irrelevance 2. Identify nonlinear associations using CORR procedure 3. Use empirical logit plots for nonlinearity detection 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 5: Logistic Regression	25%	- Develop Logistic Models 1. Perform logistic regression using the LOGISTIC procedure 2. Optimize model performance through input selection - Interpret and Deploy Models 1. Interpret LOGISTIC procedure output 2. Score new data sets using LOGISTIC and PLM procedures