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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

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The A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Certification is one of the highest level of certification mainly focussing to the Advanced statistical analysis.

if you pass your exam and meet all requirements for a credential, you will receive an e-mail from SAS within 72 business hours with instructions providing access to your certificate and logo through the SAS Certification Manager. You will also receive an email from Acclaim with access to your digital badge. For questions regarding access to either of these, please visit SAS Website.

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SASInstitute A00-240 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Linear Regression	20%	- Model Selection and Diagnostics 1. Conduct residual and diagnostic analysis 2. Assess model validity 3. Perform model selection using REG or GLMSELECT - Build Regression Models 1. Use REG and GLM procedures 2. Fit multiple linear regression models - Interpret Regression Results 1. Evaluate parameter estimates 2. Interpret REG, PLM and GLM output
Topic 2: Logistic Regression	25%	- Develop Logistic Models 1. Optimize model performance through input selection 2. Perform logistic regression using the LOGISTIC procedure - Interpret and Deploy Models 1. Interpret LOGISTIC procedure output 2. Score new data sets using LOGISTIC and PLM procedures
Topic 3: Prepare Inputs for Predictive Model Performance	20%	- Variable Engineering and Screening 1. Use empirical logit plots for nonlinearity detection 2. Screen variables for irrelevance 3. Identify nonlinear associations using CORR procedure 4. Improve predictive power of categorical variables - Data Preparation 1. Manipulate data using DATA step programming 2. Identify challenges in preparing model input data 3. Use loops, arrays, functions and conditional statements
Topic 4: Measure Model Performance	25%	- Model Assessment 1. Assess classifier performance using confusion matrices 2. Apply principles of honest assessment - Performance Visualization 1. Create and interpret lift charts 2. Create and interpret gains charts 3. Create and interpret ROC charts - Validation and Selection 1. Perform model validation using training and validation data 2. Compare and select predictive models - Decision Optimization 1. Evaluate scoring strategies 2. Establish effective decision cutoff values
Topic 5: ANOVA	10%	- ANOVA Analysis 1. Detect and analyze factor interactions 2. Perform post hoc tests 3. Evaluate treatment effects 4. Analyze differences between population means using GLM and TTEST procedures - ANOVA Concepts and Assumptions 1. Verify ANOVA assumptions 2. Identify appropriate ANOVA methods