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

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A00-273 Braindumps VCE
  • 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
Building and Assessing Regression-Type Models41%- Linear Regression
  • 1. Build linear regression models
  • 2. Interpret parameter estimates
  • 3. Assess model fit
- Advanced Regression Techniques
  • 1. Perform nonparametric logistic regression
  • 2. Use interaction effects
  • 3. Perform generalized additive modeling
- Generalized Linear Models
  • 1. Select distributions and link functions
  • 2. Interpret model results
  • 3. Evaluate predictive performance
Model Comparison and Scoring9%- Model Comparison
  • 1. Select champion models
  • 2. Compare candidate models
  • 3. Interpret comparison statistics
- Model Scoring
  • 1. Generate score code
  • 2. Export score code
  • 3. Apply models to new data
Building and Assessing Segmentation Models32%- Model Assessment
  • 1. Analyze ROC and Lift charts
  • 2. Review residual and influence diagnostics
  • 3. Interpret fit statistics
  • 4. Evaluate misclassification results
- 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. Build logistic regression models
  • 3. Configure event levels and response variables
  • 4. Apply variable selection methods
SAS Visual Statistics Cross-Functional Tasks18%- Data Filtering and Group-By Analysis
  • 1. Interpret group-by model results
  • 2. Use interactive group-by modeling
  • 3. Apply filters to models and visualizations
  • 4. Review measure details
- Data Preparation
  • 1. Transform variables
  • 2. Replace invalid data with missing values
  • 3. Combine category levels
  • 4. Manage explorations and visualizations
  • 5. Create aggregated measures
  • 6. Impute variables
  • 7. Create dummy variables

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