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SAS Institute Updated A00-240 Exam Blueprint, Syllabus and Topics

SAS Statistical Business Analysis SAS9: Regression and Model

Last Update Jan 14, 2026
Total Questions : 99

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SAS Institute A00-240 Exam Overview :

Exam Name SAS Statistical Business Analysis SAS9: Regression and Model
Exam Code A00-240
Actual Exam Duration 120 minutes
Expected no. of Questions in Actual Exam 60
Official Information https://www.sas.com/en_us/certification/credentials/advanced-analytics/statistical-business-analyst.html
See Expected Questions SAS Institute A00-240 Expected Questions in Actual Exam
Take Self-Assessment Use SAS Institute A00-240 Practice Test to Assess your preparation - Save Time and Reduce Chances of Failure

SAS A00-240 Exam Topics :

Section Weight Objectives
ANOVA - 10%
  • Verify the assumptions of ANOVA
  • Analyze differences between population means using the GLM and TTEST procedures
  • Perform ANOVA post hoc test to evaluate treatment effect
  • Detect and analyze interactions between factor
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
  • 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
Logistic Regression - 25%
  • Perform logistic regression with the LOGISTIC procedure
  • Optimize model performance through input selection
  • Interpret the output of the LOGISTIC procedure
  • Score new data sets using the LOGISTIC and PLM procedures
Prepare Inputs for Predictive Model Performance - 20%
  • Identify the potential challenges when preparing input data for a model
  • 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
Measure Model Performance - 25%
  • Apply the principles of honest assessment to model performance measurement
  • Assess classifier performance using the confusion matrix
  • Model selection and validation using training and validation data
  • Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
  • Establish effective decision cut-off values for scoring

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