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SAS Global Certification program

SAS Analyst

SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential

Designed for SAS professionals who use SAS/STAT software to conduct and interpret complex statistical data analysis

Successful candidates should have experience in
  • analysis of variance
  • linear and logistic regression
  • preparing inputs for predictive models
  • measuring model performance.

Required Exam

Candidates who earn this credential will have earned a passing score on the SAS Statistical Business Analysis Using SAS 9: Regression and Modeling exam. This exam is administered by SAS and Pearson VUE.
  • 60 scored multiple-choice and short-answer questions (must achieve score of 68% correct to pass)
  • In addition to the 60 scored items, there may be up to 5 unscored items.
  • 2 hours to complete exam
  • Use exam ID A00-240; required when registering with Pearson VUE.

Exam topics include:

  • 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 factors
Linear Regression
  • Fit a multiple linear regression model using the REG and GLM procedures
  • Analyze the output of the REG procedure for multiple linear regression models
  • Use the REG procedure to perform model selection
  • Assess the validity of a given regression model through the use of diagnostic and residual analysis
Logistic Regression
  • 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 SCORE procedures
Prepare Inputs for Predictive Model Performance
  • Identify potential problems with input data
  • Use the DATA step to manipulate data with loops, arrays, conditional statements and functions
  • Reduce the number of categorical levels in a predictive model
  • Screen variables for irrelevance using the CORR procedure
  • Screen variables for non-linearity using empirical logit plots
Measure Model Performance
  • 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