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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How to Prepare For A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam
Preparation Guide for A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam
Introduction
SAS (previously “Statistical Analysis System”) may be a package suite designed by SAS Institute for advanced analytics, variable analyses, business intelligence, knowledge management, and prognosticative analytics.
A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling exam is administered by SAS and Pearson VUE. has created a track for IT professionals to certify as business analyst on SAS. This certification program provides SAS professionals a way to demonstrate their skills. The assessment is based on a rigorous exam using industry standard methodology to determine whether a candidate meets SAS's proficiency standards.
SAS® Certified Statistical Business Analyst for SAS®9 this credential is for professionals who use SAS/STAT® software to conduct and interpret complex statistical data analysis.
In this guide, we will cover the A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam, A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Certified professional's salary and all aspects of the A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Certification.
The SAS international Certification Program currently issues versioned accreditation that don't lapse.The version is indicated in the credential name.
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SASInstitute A00-240 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Analysis of Variance (ANOVA) | 10% | - Perform post-hoc tests - Use PROC GLM for ANOVA - Assess ANOVA assumptions - Analyze factor interactions |
| Logistic Regression | 25% | - Build models using PROC LOGISTIC - Score new datasets with LOGISTIC and PLM - Select and optimize input variables - Interpret odds ratios and model coefficients |
| Prepare Inputs for Predictive Modeling | 20% | - Screen variables with CORR procedure and empirical logit plots - Transform categorical variables - Manipulate data using DATA step - Identify data preparation challenges |
| Linear Regression | 20% | - Interpret REG, GLM, and PLM procedure output - Fit multiple linear regression models - Perform model selection with GLMSELECT - Conduct residual and diagnostic analysis |
| Measure and Evaluate Model Performance | 25% | - Apply honest assessment principles - 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 |
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