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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
- Stastical Analyst, or
- Business Analyst
- Anyone writing & interested in Statistical Business Analysis
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Introduction to A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam
According to SAS, SAS® Certified Statistical Business Analyst for SAS®9 this credentials signifies the upper echelon of SAS programmers who demonstrate a high level of proficiency in SAS® programming expertise.
A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam is a certification exam that is conducted by Google to validates candidate knowledge and skills of working as a Statistical Business Analyst in the Company.
A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling exam covers the basic and advanced concepts of variance analysis, linear and logistic regression, preparing inputs for predictive models and measuring model performance.
After passing this exam, candidates get a certificate from SAS that helps them to demonstrate their proficiency in A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling to their clients and employers.
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SASInstitute A00-240 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Linear Regression | 20% | - Interpret REG, GLM, and PLM procedure output - Perform model selection with GLMSELECT - Fit multiple linear regression models - Conduct residual and diagnostic analysis |
| Topic 2: Logistic Regression | 25% | - Interpret odds ratios and model coefficients - Select and optimize input variables - Score new datasets with LOGISTIC and PLM - Build models using PROC LOGISTIC |
| Topic 3: Analysis of Variance (ANOVA) | 10% | - Assess ANOVA assumptions - Perform post-hoc tests - Analyze factor interactions - Use PROC GLM for ANOVA |
| Topic 4: Measure and Evaluate Model Performance | 25% | - Create and interpret ROC, lift, and gains charts - Determine optimal decision cut-off values - Apply honest assessment principles - Use confusion matrices and classification metrics - Validate models with training/validation data |
| Topic 5: Prepare Inputs for Predictive Modeling | 20% | - Manipulate data using DATA step - Transform categorical variables - Identify data preparation challenges - Screen variables with CORR procedure and empirical logit plots |
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