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Exam Code: A00-240
Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
Exam Q&As: 99 Q&As
Last update: May 20, 2024

Product Description

Introduce SAS Institute A00-240 Exam

The SAS Statistical Business Analysis Using SAS 9: Regression and Modeling certification is a great SAS certified credential, which indicates the ability to:

  1. Analysis of variance.
  2. Linear and logistic regression.
  3. Preparing inputs for predictive models.
  4. Measuring model performance.

To gain this certification, a passing score is needed on the A00-240 exam, which has 60 multiple choice and short-answer questions and takes 120 minutes to complete. A minimum score of 68 percent is required to pass.

Learning A00-240 Exam Objectives Is Strongly Recommended

  • 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
  • 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
  • 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
  • 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
  • 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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