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

A00-273
  • Exam Code: A00-273
  • Exam Name: Interactive Model Building using SAS Visual Statistics 7.5
  • Updated: Aug 16, 2026
  • Q & A: 0 Questions and Answers

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SASInstitute A00-273 Exam Syllabus Topics:

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

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