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Exam Code: A00-273
Exam Name: Interactive Model Building using SAS Visual Statistics 7.5
Certification Provider: SASInstitute
Corresponding Certification: SASInstitute Certification
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SASInstitute A00-273 Exam Syllabus Topics:

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

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