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 09, 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 09, 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 09, 2026
  • Q & A: 100 Questions and Answers
  • Software Price: $49.99
  • Testing Engine

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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
  • Perform ANOVA post hoc test to evaluate treatment effect
  • Verify the assumptions of ANOVA
  • Detect and analyze interactions between factors

2. Linear Regression - 20%

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

3. Logistic Regression - 25%

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

4. Prepare Inputs for Predictive Model Performance - 20%

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

5. Measure Model Performance - 25%

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

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

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SASInstitute A00-240 Exam Syllabus Topics:

SectionObjectives
ANOVA and General Linear Models- GLM procedures in SAS
- One-way and multi-way ANOVA
Model Diagnostics and Validation- Goodness-of-fit measures
- Influence and leverage
Logistic Regression- Model fit and diagnostics
- Binary logistic regression
Multiple Regression- Interaction effects
- Multicollinearity diagnostics
- Model building and selection
Simple Linear Regression- Model assumptions and interpretation
- Estimation and inference
- Residual analysis

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