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

  • Exam Code: A00-240
  • Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
  • Updated: Aug 07, 2026
  • Q & A: 100 Questions and Answers

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

Topics of A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam

Candidates must know the exam topics before they start of preparation. because it will really help them in hitting the core. Our A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Dumps will include the following topics:

1. ANOVA - 10%

  • Analyze differences between population means using the GLM and TTEST procedures
  • Verify the assumptions of ANOVA
  • Detect and analyze interactions between factors
  • Perform ANOVA post hoc test to evaluate treatment effect

2. Linear Regression - 20%

  • Fit a multiple linear regression model using the REG and GLM procedures
  • Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models
  • Assess the validity of a given regression model through the use of diagnostic and residual analysis
  • Use the REG or GLMSELECT procedure to perform model selection

3. Logistic Regression - 25%

  • Interpret the output of the LOGISTIC procedure
  • Perform logistic regression with the LOGISTIC procedure
  • Score new data sets using the LOGISTIC and PLM procedures
  • Optimize model performance through input selection

4. Prepare Inputs for Predictive Model Performance - 20%

  • Identify the potential challenges when preparing input data for a model
  • Improve the predictive power of categorical inputs
  • Screen variables for non-linearity using empirical logit plots
  • Use the DATA step to manipulate data with loops, arrays, conditional statements and functions
  • Screen variables for irrelevance and non-linear association using the CORR procedure

5. Measure Model Performance - 25%

  • Establish effective decision cut-off values for scoring
  • Assess classifier performance using the confusion matrix
  • Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
  • Model selection and validation using training and validation data
  • Apply the principles of honest assessment to model performance measurement

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

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A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Certified Professional salary

The average salary of a A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Certified Expert in

  • England - 46,632 POUND
  • India - 8,42,327 INR
  • United State - 70,247 USD
  • Europe - 45,347 EURO

SASInstitute A00-240 Exam Syllabus Topics:

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

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