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

A00-240
  • Exam Code: A00-240
  • Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
  • Updated: Nov 08, 2025
  • Q & A: 100 Questions and Answers
  • PDF Version

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  • PDF Price: $49.98
  • SASInstitute A00-240 Value Pack

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  • PDF Version + PC Test Engine + Online Test Engine (free)
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About SASInstitute A00-240: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential

The benefits of obtaining A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam Certification

Getting a professional certification in the SAS domain is indeed a wise decision as there are immense benefits of this credential.

  • Freshers will get spectacular pay package and dealing professionals will get guaranteed profession advancement through the SAS accreditation courses.
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  • A00-240: Statistical Business Analysis improves reasoning power and promotes analytical thinking and research ability of appearing professional.
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  • Learning SAS won't cause you to abandon the previous data formats acknowledged by you like Oracle and DB2.

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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%

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

2. Linear Regression - 20%

  • Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models
  • 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
  • Fit a multiple linear regression model using the REG and GLM procedures

3. Logistic Regression - 25%

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

4. Prepare Inputs for Predictive Model Performance - 20%

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

5. Measure Model Performance - 25%

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

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

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