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Who should take the A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam
Individuals should pursue the A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam if they want to demonstrate their expertise and ability to access and manage data to perform queries and analyses from A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Certification
- Anyone writing & interested in Statistical Business Analysis
- Business Analyst
- Stastical Analyst, or
How to book A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exams
The registration for the A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam follows the steps given below.
- Step 1 : Visit to website Pearson VUE
- Step 2 : Navigate To Certifications
- Step 3 : Search for the exam name A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling
- Step 4 : Book the exam.
SASInstitute A00-240 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Linear Regression | 20% | - Perform model selection with GLMSELECT - Interpret REG, GLM, and PLM procedure output - Fit multiple linear regression models - Conduct residual and diagnostic analysis |
| Analysis of Variance (ANOVA) | 10% | - Use PROC GLM for ANOVA - Assess ANOVA assumptions - Perform post-hoc tests - Analyze factor interactions |
| Measure and Evaluate Model Performance | 25% | - Use confusion matrices and classification metrics - Validate models with training/validation data - Determine optimal decision cut-off values - Create and interpret ROC, lift, and gains charts - Apply honest assessment principles |
| Logistic Regression | 25% | - Score new datasets with LOGISTIC and PLM - Select and optimize input variables - Interpret odds ratios and model coefficients - Build models using PROC LOGISTIC |
| Prepare Inputs for Predictive Modeling | 20% | - Manipulate data using DATA step - Identify data preparation challenges - Screen variables with CORR procedure and empirical logit plots - Transform categorical variables |
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