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The SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential is a certification that is offered by SAS Institute. SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential certification is designed for individuals who work in the field of statistical analysis and modeling using SAS software. SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential certification focuses on regression and modeling techniques and is highly valued by employers in the field.
SASInstitute A00-240 certification exam consists of 65 multiple-choice and short-answer questions that must be completed within 2 hours and 15 minutes. A00-240 exam is computer-based and can be taken at a SAS testing center or remotely with the use of a webcam. To be eligible to take the exam, candidates must have a basic understanding of statistical concepts and have experience using SAS software for data analysis. Earning this certification demonstrates that an individual has the skills and knowledge to use SAS software to perform advanced statistical analysis and modeling in a business environment, making them a valuable asset to any organization.
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SASInstitute A00-240 is a certification exam that focuses on the skills needed to perform statistical analyses using SAS 9. The SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential is designed for professionals who work in the field of business analytics and want to showcase their expertise in SAS statistical software. A00-240 exam covers a range of topics related to regression and modeling, including linear regression, logistic regression, and time series analysis.
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To prepare for the SASInstitute A00-240 exam, candidates should have a strong understanding of SAS programming and data analysis. They should also have experience working with SAS software and be familiar with SAS procedures and functions. There are a variety of resources available to help candidates prepare for the exam, including SAS training courses, study guides, and practice exams.
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| Section | Weight | Objectives |
|---|---|---|
| Measure and Evaluate Model Performance | 25% | - Use confusion matrices and classification metrics - Determine optimal decision cut-off values - Create and interpret ROC, lift, and gains charts - Validate models with training/validation data - Apply honest assessment principles |
| Logistic Regression | 25% | - Interpret odds ratios and model coefficients - Build models using PROC LOGISTIC - Score new datasets with LOGISTIC and PLM - Select and optimize input variables |
| Analysis of Variance (ANOVA) | 10% | - Assess ANOVA assumptions - Perform post-hoc tests - Analyze factor interactions - Use PROC GLM for ANOVA |
| Linear Regression | 20% | - Conduct residual and diagnostic analysis - Fit multiple linear regression models - Interpret REG, GLM, and PLM procedure output - Perform model selection with GLMSELECT |
| Prepare Inputs for Predictive Modeling | 20% | - Identify data preparation challenges - Manipulate data using DATA step - Transform categorical variables - Screen variables with CORR procedure and empirical logit plots |
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