SASInstitute A00-240 : SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential

A00-240 pass collection

Exam Code: A00-240

Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential

Updated: Jul 19, 2026

Q & A: 100 Questions and Answers

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About SASInstitute A00-240 Exam

A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Certified Professional salary

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A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Certification Path

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.

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

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Getting a professional certification in the SAS domain is indeed a wise decision as there are immense benefits of this credential.

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  • A00-240: Statistical Business Analysis improves reasoning power and promotes analytical thinking and research ability of appearing professional.

SASInstitute A00-240 Exam Syllabus Topics:

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

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