Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
Certification Provider: SASInstitute
Corresponding Certification: SAS Institute Systems Certification
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Predictive Modeling Using Logistic Regression Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression
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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.
SASInstitute A00-240 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Analysis of Variance (ANOVA) | 10% | - Use PROC GLM for ANOVA - Analyze factor interactions - Perform post-hoc tests - Assess ANOVA assumptions |
| Linear Regression | 20% | - Conduct residual and diagnostic analysis - Interpret REG, GLM, and PLM procedure output - Perform model selection with GLMSELECT - Fit multiple linear regression models |
| Logistic Regression | 25% | - Score new datasets with LOGISTIC and PLM - Build models using PROC LOGISTIC - Interpret odds ratios and model coefficients - Select and optimize input variables |
| Measure and Evaluate Model Performance | 25% | - Use confusion matrices and classification metrics - Determine optimal decision cut-off values - Create and interpret ROC, lift, and gains charts - Apply honest assessment principles - Validate models with training/validation data |
| Prepare Inputs for Predictive Modeling | 20% | - Identify data preparation challenges - Transform categorical variables - Manipulate data using DATA step - Screen variables with CORR procedure and empirical logit plots |
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