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

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The A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Certification is one of the highest level of certification mainly focussing to the Advanced statistical analysis.

if you pass your exam and meet all requirements for a credential, you will receive an e-mail from SAS within 72 business hours with instructions providing access to your certificate and logo through the SAS Certification Manager. You will also receive an email from Acclaim with access to your digital badge. For questions regarding access to either of these, please visit SAS Website.

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%

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

2. Linear Regression - 20%

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

3. Logistic Regression - 25%

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

4. Prepare Inputs for Predictive Model Performance - 20%

• Identify the potential challenges when preparing input data for a model
• Screen variables for non-linearity using empirical logit plots
• 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
• Improve the predictive power of categorical inputs

5. Measure Model Performance - 25%

• Establish effective decision cut-off values for scoring
• 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