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

A00-240
  • Exam Code: A00-240
  • Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
  • Updated: Aug 17, 2026
  • Q & A: 100 Questions and Answers

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About SASInstitute A00-240Latest exam Dumps

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

  • 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)
  • Format: Multiple choices, multiple answers , short answers
  • 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
  • Passing score: 68% (Must achieve score of 68 percent correct to pass)
  • Length of Examination: 120 minutes

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  • You'll simply connect with the worldwide network of SAS consultants and users to exchange knowledge and concepts.
  • Freshers will get spectacular pay package and dealing professionals will get guaranteed profession advancement through the SAS accreditation courses.
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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: Prepare Inputs for Predictive Model Performance20%- Variable Engineering and Screening
  • 1. Identify nonlinear associations using CORR procedure
  • 2. Screen variables for irrelevance
  • 3. Improve predictive power of categorical variables
  • 4. Use empirical logit plots for nonlinearity detection
- Data Preparation
  • 1. Manipulate data using DATA step programming
  • 2. Use loops, arrays, functions and conditional statements
  • 3. Identify challenges in preparing model input data
Topic 2: Logistic Regression25%- Interpret and Deploy Models
  • 1. Score new data sets using LOGISTIC and PLM procedures
  • 2. Interpret LOGISTIC procedure output
- Develop Logistic Models
  • 1. Perform logistic regression using the LOGISTIC procedure
  • 2. Optimize model performance through input selection
Topic 3: Linear Regression20%- Model Selection and Diagnostics
  • 1. Assess model validity
  • 2. Perform model selection using REG or GLMSELECT
  • 3. Conduct residual and diagnostic analysis
- Interpret Regression Results
  • 1. Interpret REG, PLM and GLM output
  • 2. Evaluate parameter estimates
- Build Regression Models
  • 1. Use REG and GLM procedures
  • 2. Fit multiple linear regression models
Topic 4: ANOVA10%- ANOVA Analysis
  • 1. Evaluate treatment effects
  • 2. Detect and analyze factor interactions
  • 3. Analyze differences between population means using GLM and TTEST procedures
  • 4. Perform post hoc tests
- ANOVA Concepts and Assumptions
  • 1. Identify appropriate ANOVA methods
  • 2. Verify ANOVA assumptions
Topic 5: Measure Model Performance25%- Model Assessment
  • 1. Apply principles of honest assessment
  • 2. Assess classifier performance using confusion matrices
- Performance Visualization
  • 1. Create and interpret gains charts
  • 2. Create and interpret ROC charts
  • 3. Create and interpret lift charts
- Decision Optimization
  • 1. Evaluate scoring strategies
  • 2. Establish effective decision cutoff values
- Validation and Selection
  • 1. Compare and select predictive models
  • 2. Perform model validation using training and validation data

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