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SASInstitute A00-273 valid - in .pdf

A00-273 pdf
  • Exam Code: A00-273
  • Exam Name: Interactive Model Building using SAS Visual Statistics 7.5
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SASInstitute A00-273 Exam Syllabus Topics:

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

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