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

A00-240
  • Exam Code: A00-240
  • Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
  • Updated: Sep 08, 2026
  • Q & A: 100 Questions and Answers

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About SASInstitute A00-240 Exam

What is the duration, language, and format of A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam

  • Passing score: 68% (Must achieve score of 68 percent correct to pass)
  • Number of Questions: 60 multiple-choice and short-answer questions(In addition to the 60 scored items, there may be up to five unscored items)
  • Language: Czech, simplified Chinese, traditional Chinese, Dutch, English (UK), English (US), French, German, Indonesian, Italian, Japanese, Korean, Polish, Portuguese, Portuguese (Brazil), Russian, Slovak, Spanish, Spanish (Latin America), Thai, Turkish, and Vietnamese
  • Format: Multiple choices, multiple answers , short answers
  • Length of Examination: 120 minutes

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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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SASInstitute A00-240 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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
Topic 2: Analysis of Variance (ANOVA)10%- Analyze factor interactions
- Perform post-hoc tests
- Assess ANOVA assumptions
- Use PROC GLM for ANOVA
Topic 3: Measure and Evaluate Model Performance25%- Determine optimal decision cut-off values
- Use confusion matrices and classification metrics
- Validate models with training/validation data
- Apply honest assessment principles
- Create and interpret ROC, lift, and gains charts
Topic 4: Prepare Inputs for Predictive Modeling20%- Screen variables with CORR procedure and empirical logit plots
- Identify data preparation challenges
- Transform categorical variables
- Manipulate data using DATA step
Topic 5: Linear Regression20%- Conduct residual and diagnostic analysis
- Interpret REG, GLM, and PLM procedure output
- Perform model selection with GLMSELECT
- Fit multiple linear regression models

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