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

A00-240
  • Exam Code: A00-240
  • Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
  • Updated: Jul 25, 2026
  • Q & A: 100 Questions and Answers
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  • PDF Price: $49.99
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About SASInstitute A00-240 Exam

How much A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam cost

A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam cost is $180.

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

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Who should take the A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam

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

  • Business Analyst
  • Anyone writing & interested in Statistical Business Analysis
  • Stastical Analyst, or

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SASInstitute A00-240 Exam Syllabus Topics:

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

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