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Topics of A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam

Candidates must know the exam topics before they start of preparation. because it will really help them in hitting the core. Our A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Dumps will include the following topics:

1. ANOVA - 10%

  • Analyze differences between population means using the GLM and TTEST procedures
  • Perform ANOVA post hoc test to evaluate treatment effect
  • Verify the assumptions of ANOVA
  • Detect and analyze interactions between factors

2. Linear Regression - 20%

  • Use the REG or GLMSELECT procedure to perform model selection
  • Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models
  • Fit a multiple linear regression model using the REG and GLM procedures
  • Assess the validity of a given regression model through the use of diagnostic and residual analysis

3. Logistic Regression - 25%

  • Score new data sets using the LOGISTIC and PLM procedures
  • Optimize model performance through input selection
  • Interpret the output of the LOGISTIC procedure
  • Perform logistic regression with the LOGISTIC procedure

4. Prepare Inputs for Predictive Model Performance - 20%

  • Use the DATA step to manipulate data with loops, arrays, conditional statements and functions
  • Improve the predictive power of categorical inputs
  • Screen variables for non-linearity using empirical logit plots
  • Identify the potential challenges when preparing input data for a model
  • Screen variables for irrelevance and non-linear association using the CORR procedure

5. Measure Model Performance - 25%

  • Apply the principles of honest assessment to model performance measurement
  • Establish effective decision cut-off values for scoring
  • Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
  • Assess classifier performance using the confusion matrix
  • Model selection and validation using training and validation data

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

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