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

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What is the duration, language, and format of A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam

• Language: Czech, simplified Chinese, traditional Chinese, Dutch, English (UK), English (US), French, German, Indonesian, Italian, Japanese, Korean, Polish, Portuguese, Portuguese (Brazil), Russian, Slovak, Spanish, Spanish (Latin America), Thai, Turkish, and Vietnamese
• Length of Examination: 120 minutes
• Number of Questions: 60 multiple-choice and short-answer questions(In addition to the 60 scored items, there may be up to five unscored items)
• Format: Multiple choices, multiple answers , short answers
• Passing score: 68% (Must achieve score of 68 percent correct to pass)

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Reference: https://www.sas.com/en_us/certification/credentials/advanced-analytics/statistical-business-analyst.html

Topics of A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam

Candidates must know the exam topics before they start of preparation. because it will really help them in hitting the core. Our A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Dumps will include the following topics:

1. ANOVA - 10%

• Perform ANOVA post hoc test to evaluate treatment effect
• Analyze differences between population means using the GLM and TTEST procedures
• Verify the assumptions of ANOVA
• Detect and analyze interactions between factors

2. Linear Regression - 20%

• Assess the validity of a given regression model through the use of diagnostic and residual analysis
• Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models
• Fit a multiple linear regression model using the REG and GLM procedures
• Use the REG or GLMSELECT procedure to perform model selection

3. Logistic Regression - 25%

• Optimize model performance through input selection
• Interpret the output of the LOGISTIC procedure
• Score new data sets using the LOGISTIC and PLM procedures
• Perform logistic regression with the LOGISTIC procedure

4. Prepare Inputs for Predictive Model Performance - 20%

• Identify the potential challenges when preparing input data for a model
• Screen variables for irrelevance and non-linear association using the CORR procedure
• Use the DATA step to manipulate data with loops, arrays, conditional statements and functions
• Screen variables for non-linearity using empirical logit plots
• Improve the predictive power of categorical inputs

5. Measure Model Performance - 25%

• Assess classifier performance using the confusion matrix
• Apply the principles of honest assessment to model performance measurement
• Model selection and validation using training and validation data
• Establish effective decision cut-off values for scoring
• Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection

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