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SASInstitute A00-240 Exam Syllabus Topics:
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Many candidates are headache about exam SASInstitute A00-240 since some of them find they have no confidence to attend the real test; some of them failed exam again and do not want to fail again. If you are still thinking about how to pass, let our Real test dumps for SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential help you. Every day we hear kinds of problems from candidates about their failure, our professional can always give them wise advice. Our A00-240 exam preparation helps thousands of candidate sail through the examination every year. If you really want to get rid of this situation, please go and follow us, everything will be easy. Below I summarize the questions about A00-240 - SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential exam preparation most candidates may care about for your reference.
How to study the A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam
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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%
- Fit a multiple linear regression model using the REG and GLM procedures
- 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
- Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models
3. Logistic Regression - 25%
- Optimize model performance through input selection
- Perform logistic regression with the LOGISTIC procedure
- Score new data sets using the LOGISTIC and PLM procedures
- Interpret the output of the LOGISTIC procedure
4. Prepare Inputs for Predictive Model Performance - 20%
- Screen variables for irrelevance and non-linear association using the CORR procedure
- Improve the predictive power of categorical inputs
- Use the DATA step to manipulate data with loops, arrays, conditional statements and functions
- Screen variables for non-linearity using empirical logit plots
- Identify the potential challenges when preparing input data for a model
5. Measure Model Performance - 25%
- 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
- Assess classifier performance using the confusion matrix
- Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
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