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: Aug 07, 2026
  • Q & A: 100 Questions and Answers
  • PDF Price: $49.99
  • Free Demo

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  • 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
  • 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: Aug 07, 2026
  • Q & A: 100 Questions and Answers
  • Software Price: $49.99
  • Testing Engine

About SASInstitute SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Dumps Question

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Predictive Modeling Using Logistic Regression Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression

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
  • India - 8,42,327 INR
  • Europe - 45,347 EURO
  • England - 46,632 POUND

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

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.

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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. 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
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. Fit multiple linear regression models
  • 2. Use REG and GLM procedures
Topic 3: Prepare Inputs for Predictive Model Performance20%- 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
- Variable Engineering and Screening
  • 1. Screen variables for irrelevance
  • 2. Identify nonlinear associations using CORR procedure
  • 3. Improve predictive power of categorical variables
  • 4. Use empirical logit plots for nonlinearity detection
Topic 4: Measure Model Performance25%- Validation and Selection
  • 1. Perform model validation using training and validation data
  • 2. Compare and select predictive models
- Performance Visualization
  • 1. Create and interpret ROC charts
  • 2. Create and interpret gains charts
  • 3. Create and interpret lift charts
- Decision Optimization
  • 1. Establish effective decision cutoff values
  • 2. Evaluate scoring strategies
- Model Assessment
  • 1. Assess classifier performance using confusion matrices
  • 2. Apply principles of honest assessment
Topic 5: 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. Score new data sets using LOGISTIC and PLM procedures
  • 2. Interpret LOGISTIC procedure output

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