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SAS A00-240 Exam Syllabus

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Before starting your A00-240 exam preparation, it is recommended to review the complete SAS Statistical Business Analysis SAS9: Regression and Model exam syllabus and carefully go through the exam objectives listed below. Once you understand the exam structure and objectives, you should practice using our free A00-240 questions. We also provide premium A00-240 practice test, fully updated according to the latest exam objectives, to help you accurately assess your preparedness for the actual exam.

SAS
Vendor
A00-240
Exam Code
99
Total Questions
5
Total Exam Domains

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A00-240 EXAM QUESTIONS

SAS A00-240 Exam Objectives

Section 1: ANOVA -
Weight:
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
Section 2: Linear Regression -
Weight:
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
Section 3: Logistic Regression -
Weight:
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
Section 4: Prepare Inputs for Predictive Model Performance -
Weight:
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
Section 5: Measure Model Performance -
Weight:
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
Info
https://www.sas.com/en_us/certification/credentials/advanced-analytics/statistical-business-analyst.html