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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.

Reference: https://www.sas.com/en_us/certification/credentials/advanced-analytics/statistical-business-analyst.html

SASInstitute A00-240 Exam Syllabus Topics:

SectionWeightObjectives
Logistic Regression25%- Build models using PROC LOGISTIC
- Score new datasets with LOGISTIC and PLM
- Interpret odds ratios and model coefficients
- Select and optimize input variables
Measure and Evaluate Model Performance25%- Use confusion matrices and classification metrics
- Create and interpret ROC, lift, and gains charts
- Validate models with training/validation data
- Determine optimal decision cut-off values
- Apply honest assessment principles
Prepare Inputs for Predictive Modeling20%- Manipulate data using DATA step
- Transform categorical variables
- Identify data preparation challenges
- Screen variables with CORR procedure and empirical logit plots
Analysis of Variance (ANOVA)10%- Assess ANOVA assumptions
- Perform post-hoc tests
- Analyze factor interactions
- Use PROC GLM for ANOVA
Linear Regression20%- Fit multiple linear regression models
- Perform model selection with GLMSELECT
- Conduct residual and diagnostic analysis
- Interpret REG, GLM, and PLM procedure output
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