SASInstitute A00-240 Valid Q&A - in .pdf

  • A00-240 pdf
  • Exam Code: A00-240
  • Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
  • Updated: Sep 09, 2026
  • Q & A: 100 Questions and Answers
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SASInstitute A00-240 Valid Q&A - Testing Engine

  • A00-240 Testing Engine
  • Exam Code: A00-240
  • Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
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  • Q & A: 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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Predictive Modeling Using Logistic Regression Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression

SASInstitute A00-240 Exam Syllabus Topics:

SectionWeightObjectives
ANOVA10%- ANOVA Analysis
  • 1. Detect and analyze factor interactions
  • 2. Perform post hoc tests
  • 3. Evaluate treatment effects
  • 4. Analyze differences between population means using GLM and TTEST procedures
- ANOVA Concepts and Assumptions
  • 1. Verify ANOVA assumptions
  • 2. Identify appropriate ANOVA methods
Prepare Inputs for Predictive Model Performance20%- Variable Engineering and Screening
  • 1. Use empirical logit plots for nonlinearity detection
  • 2. Improve predictive power of categorical variables
  • 3. Screen variables for irrelevance
  • 4. Identify nonlinear associations using CORR procedure
- Data Preparation
  • 1. Identify challenges in preparing model input data
  • 2. Use loops, arrays, functions and conditional statements
  • 3. Manipulate data using DATA step programming
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
Measure Model Performance25%- Model Assessment
  • 1. Assess classifier performance using confusion matrices
  • 2. Apply principles of honest assessment
- 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
- Validation and Selection
  • 1. Perform model validation using training and validation data
  • 2. Compare and select predictive models
Linear Regression20%- Model Selection and Diagnostics
  • 1. Perform model selection using REG or GLMSELECT
  • 2. Conduct residual and diagnostic analysis
  • 3. Assess model validity
- 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
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