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

A00-240 real exams

Exam Code: A00-240

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

Updated: Sep 06, 2026

Q & A: 100 Questions and Answers

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Introduction to A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam

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.

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

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SASInstitute A00-240 Exam Syllabus Topics:

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

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