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The SAS Institute is a leading global software company that provides innovative analytics and business intelligence solutions designed to transform data into actionable insights. One of the most popular products offered by SAS is SAS 9, which provides advanced statistical analysis capabilities. For individuals who want to demonstrate their proficiency in SAS 9, the SAS Institute offers the A00-240 certification exam, also known as the SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential.

The SAS Certified Statistical Business Analyst using SAS 9: Regression and Modeling Credential is a certification program provided by the SAS Institute. It is designed to validate the skills and knowledge of statistical analysts who use SAS software to perform data analysis, model building, and regression analysis. SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential certification exam focuses on essential statistical concepts and SAS programming skills, preparing candidates to work effectively with SAS to analyze data and develop models.

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The SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential certification exam covers important topics such as multiple regression, general linear models, logistic regression, and variable selection techniques. A00-240 exam is structured in such a way that it challenges participants to demonstrate their competence in working with data sets, developing and validating predictive models, and interpreting statistical results. With this certification, you can build a foundation for future SAS certifications, such as the SAS Certified Data Scientist and the SAS Certified Advanced Analytics Professional.

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

SASInstitute A00-240 Exam Syllabus Topics:

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