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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A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Certified Professional salary
The average salary of a A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Certified Expert in
- India - 8,42,327 INR
- Europe - 45,347 EURO
- United State - 70,247 USD
- England - 46,632 POUND
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SASInstitute A00-240 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Analysis of Variance (ANOVA) | 10% | - Use PROC GLM for ANOVA - Perform post-hoc tests - Assess ANOVA assumptions - Analyze factor interactions |
| Topic 2: 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 |
| Topic 3: Measure and Evaluate Model Performance | 25% | - Determine optimal decision cut-off values - Use confusion matrices and classification metrics - Apply honest assessment principles - Create and interpret ROC, lift, and gains charts - Validate models with training/validation data |
| Topic 4: Prepare Inputs for Predictive Modeling | 20% | - Manipulate data using DATA step - Identify data preparation challenges - Transform categorical variables - Screen variables with CORR procedure and empirical logit plots |
| Topic 5: 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 |
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