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SASInstitute A00-273 Exam Syllabus Topics:

SectionWeightObjectives
Building and Assessing Segmentation Models32%- Cluster Analysis
  • 1. Configure k-means clustering
  • 2. Assign analysis roles
  • 3. Create and use cluster identifiers
  • 4. Perform unsupervised segmentation
  • 5. Modify cluster properties
- Model Assessment
  • 1. Analyze ROC and Lift charts
  • 2. Evaluate misclassification results
  • 3. Interpret fit statistics
  • 4. Review residual and influence diagnostics
- Logistic Regression Modeling
  • 1. Select appropriate link functions
  • 2. Build logistic regression models
  • 3. Apply variable selection methods
  • 4. Configure event levels and response variables
Model Comparison and Scoring9%- Model Comparison
  • 1. Compare candidate models
  • 2. Select champion models
  • 3. Interpret comparison statistics
- Model Scoring
  • 1. Export score code
  • 2. Apply models to new data
  • 3. Generate score code
SAS Visual Statistics Cross-Functional Tasks18%- Data Filtering and Group-By Analysis
  • 1. Review measure details
  • 2. Interpret group-by model results
  • 3. Use interactive group-by modeling
  • 4. Apply filters to models and visualizations
- Data Preparation
  • 1. Create aggregated measures
  • 2. Impute variables
  • 3. Manage explorations and visualizations
  • 4. Combine category levels
  • 5. Replace invalid data with missing values
  • 6. Transform variables
  • 7. Create dummy variables
Building and Assessing Regression-Type Models41%- Generalized Linear Models
  • 1. Evaluate predictive performance
  • 2. Interpret model results
  • 3. Select distributions and link functions
- Advanced Regression Techniques
  • 1. Perform generalized additive modeling
  • 2. Perform nonparametric logistic regression
  • 3. Use interaction effects
- Linear Regression
  • 1. Interpret parameter estimates
  • 2. Build linear regression models
  • 3. Assess model fit

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