Last Updated: Jul 29, 2026
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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.
Individuals should pursue the A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam if they want to demonstrate their expertise and ability to access and manage data to perform queries and analyses from A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Certification
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.
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| Section | Weight | Objectives |
|---|---|---|
| Analysis of Variance (ANOVA) | 10% | - Use PROC GLM for ANOVA - Analyze factor interactions - Assess ANOVA assumptions - Perform post-hoc tests |
| Measure and Evaluate Model Performance | 25% | - Determine optimal decision cut-off values - Apply honest assessment principles - Use confusion matrices and classification metrics - Validate models with training/validation data - Create and interpret ROC, lift, and gains charts |
| Prepare Inputs for Predictive Modeling | 20% | - Manipulate data using DATA step - Identify data preparation challenges - Screen variables with CORR procedure and empirical logit plots - Transform categorical variables |
| Logistic Regression | 25% | - Score new datasets with LOGISTIC and PLM - Interpret odds ratios and model coefficients - Select and optimize input variables - Build models using PROC LOGISTIC |
| Linear Regression | 20% | - Interpret REG, GLM, and PLM procedure output - Fit multiple linear regression models - Conduct residual and diagnostic analysis - Perform model selection with GLMSELECT |
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