SASInstitute A00-273 dumps - in .pdf

A00-273 pdf
  • 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
  • PDF Price: $888.00

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  • 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
  • PDF Version + PC Test Engine + Online Test Engine
  • Value Pack Total: $1776.00  $908.00

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  • 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
  • Software Price: $888.00
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SASInstitute A00-273 Exam Syllabus Topics:

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

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