SASInstitute A00-240 dumps - 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 23, 2026
  • Q & A: 100 Questions and Answers
  • PDF Price: $49.99

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  • Exam Code: A00-240
  • Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
  • Updated: Sep 23, 2026
  • Q & A: 100 Questions and Answers
  • PDF Version + PC Test Engine + Online Test Engine
  • Value Pack Total: $99.98  $69.99
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SASInstitute A00-240 dumps - Testing Engine

A00-240 Testing Engine
  • Exam Code: A00-240
  • Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
  • Updated: Sep 23, 2026
  • Q & A: 100 Questions and Answers
  • Software Price: $49.99
  • Testing Engine

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

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

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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

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

SASInstitute A00-240 Exam Syllabus Topics:

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

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