SASInstitute A00-240 Q&A - in .pdf

  • A00-240 pdf
  • Exam Code: A00-240
  • Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
  • Updated: Sep 30, 2026
  • Q & A: 100 Questions and Answers
  • Convenient, easy to study.
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  • Exam Code: A00-240
  • Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
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  • Updated: Sep 30, 2026
  • Q & A: 100 Questions and Answers
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  • Exam Code: A00-240
  • Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
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  • Q & A: 100 Questions and Answers
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A00-240 Practice Dumps

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.

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

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