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Reference: https://www.sas.com/en_us/certification/credentials/advanced-analytics/statistical-business-analyst.html

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SASInstitute A00-240 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Measure Model Performance25%- Model Assessment
  • 1. Assess classifier performance using confusion matrices
  • 2. Apply principles of honest assessment
- Decision Optimization
  • 1. Establish effective decision cutoff values
  • 2. Evaluate scoring strategies
- Performance Visualization
  • 1. Create and interpret lift charts
  • 2. Create and interpret ROC charts
  • 3. Create and interpret gains charts
- Validation and Selection
  • 1. Perform model validation using training and validation data
  • 2. Compare and select predictive models
Topic 2: Prepare Inputs for Predictive Model Performance20%- Variable Engineering and Screening
  • 1. Screen variables for irrelevance
  • 2. Identify nonlinear associations using CORR procedure
  • 3. Improve predictive power of categorical variables
  • 4. Use empirical logit plots for nonlinearity detection
- Data Preparation
  • 1. Identify challenges in preparing model input data
  • 2. Use loops, arrays, functions and conditional statements
  • 3. Manipulate data using DATA step programming
Topic 3: ANOVA10%- ANOVA Concepts and Assumptions
  • 1. Verify ANOVA assumptions
  • 2. Identify appropriate ANOVA methods
- ANOVA Analysis
  • 1. Analyze differences between population means using GLM and TTEST procedures
  • 2. Perform post hoc tests
  • 3. Evaluate treatment effects
  • 4. Detect and analyze factor interactions
Topic 4: Linear Regression20%- Build Regression Models
  • 1. Fit multiple linear regression models
  • 2. Use REG and GLM procedures
- Interpret Regression Results
  • 1. Interpret REG, PLM and GLM output
  • 2. Evaluate parameter estimates
- Model Selection and Diagnostics
  • 1. Conduct residual and diagnostic analysis
  • 2. Perform model selection using REG or GLMSELECT
  • 3. Assess model validity
Topic 5: Logistic Regression25%- Interpret and Deploy Models
  • 1. Score new data sets using LOGISTIC and PLM procedures
  • 2. Interpret LOGISTIC procedure output
- Develop Logistic Models
  • 1. Optimize model performance through input selection
  • 2. Perform logistic regression using the LOGISTIC procedure

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