SASInstitute A00-273 : Interactive Model Building using SAS Visual Statistics 7.5

  • Exam Code: A00-273
  • Exam Name: Interactive Model Building using SAS Visual Statistics 7.5
  • Updated: Aug 28, 2026
  • Q & A: 0 Questions and Answers

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SASInstitute A00-273 Exam Syllabus Topics:

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

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