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SASInstitute SAS Institute Systems Certification A00-240

A00-240

Exam Code: A00-240

Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential

Updated: Aug 01, 2026

Q&A Number: 100 Q&As

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About SASInstitute A00-240 Exam Braindumps

A00-240 exam certification is one of the most important certification recently. When qualified by the A00-240 certification, you will get a good job easily with high salary. Besides, the career opportunities will be open for a certified person. Now, you can get the valid and best useful A00-240 exam training material. A00-240 will help you to strengthen your technical knowledge and allow you pass at your first try.

A00-240 exam dumps

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

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

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

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