A00-240 exam dumps

SASInstitute A00-240 Value Package

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  • Exam Code: A00-240
  • Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
  • No. of Questions: 100 Questions and Answers
  • Updated: Jul 29, 2026

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  • Total Questions: 100
  • Updated on: Jul 29, 2026
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  • Total Questions: 100
  • Updated on: Jul 29, 2026
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  • Total Questions: 100
  • Updated on: Jul 29, 2026
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The SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling credential, also known as the A00-240 exam, is a certification program offered by the SAS Institute. SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential certification is designed for professionals who use SAS software for data analysis, regression modeling, and predictive modeling. The program assesses the candidate's proficiency in SAS programming, statistical analysis, and data management.

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

To prepare for the SASInstitute A00-240 exam, candidates can take advantage of a range of resources provided by SAS, including training courses, practice exams, and study materials. The SAS website also provides a comprehensive list of exam objectives, which candidates can use to focus their study efforts.

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SASInstitute A00-240 Exam Syllabus Topics:

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

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