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

SASInstitute A00-240 Actual PDF
  • Exam Code: A00-240
  • Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
  • Updated: Jul 31, 2026
  • Q & A: 100 Questions and Answers
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A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam cost is $180.

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

SASInstitute A00-240 Exam Syllabus Topics:

SectionWeightObjectives
Linear Regression20%- Build Regression Models
  • 1. Use REG and GLM procedures
  • 2. Fit multiple linear regression models
- 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
ANOVA10%- ANOVA Analysis
  • 1. Evaluate treatment effects
  • 2. Detect and analyze factor interactions
  • 3. Perform post hoc tests
  • 4. Analyze differences between population means using GLM and TTEST procedures
- ANOVA Concepts and Assumptions
  • 1. Verify ANOVA assumptions
  • 2. Identify appropriate ANOVA methods
Measure Model Performance25%- 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
- Performance Visualization
  • 1. Create and interpret lift charts
  • 2. Create and interpret ROC charts
  • 3. Create and interpret gains charts
- Decision Optimization
  • 1. Evaluate scoring strategies
  • 2. Establish effective decision cutoff values
Prepare Inputs for Predictive Model Performance20%- Variable Engineering and Screening
  • 1. Improve predictive power of categorical variables
  • 2. Screen variables for irrelevance
  • 3. Use empirical logit plots for nonlinearity detection
  • 4. Identify nonlinear associations using CORR procedure
- Data Preparation
  • 1. Manipulate data using DATA step programming
  • 2. Use loops, arrays, functions and conditional statements
  • 3. Identify challenges in preparing model input data
Logistic Regression25%- Develop Logistic Models
  • 1. Optimize model performance through input selection
  • 2. Perform logistic regression using the LOGISTIC procedure
- Interpret and Deploy Models
  • 1. Interpret LOGISTIC procedure output
  • 2. Score new data sets using LOGISTIC and PLM procedures

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