A00-240

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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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