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Introduction to A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam
According to SAS, SAS® Certified Statistical Business Analyst for SAS®9 this credentials signifies the upper echelon of SAS programmers who demonstrate a high level of proficiency in SAS® programming expertise.
A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam is a certification exam that is conducted by Google to validates candidate knowledge and skills of working as a Statistical Business Analyst in the Company.
A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling exam covers the basic and advanced concepts of variance analysis, linear and logistic regression, preparing inputs for predictive models and measuring model performance.
After passing this exam, candidates get a certificate from SAS that helps them to demonstrate their proficiency in A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling to their clients and employers.
What is the duration, language, and format of A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam
- Passing score: 68% (Must achieve score of 68 percent correct to pass)
- Length of Examination: 120 minutes
- Format: Multiple choices, multiple answers , short answers
- 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
- 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)
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SASInstitute A00-240 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Measure and Evaluate Model Performance | 25% | - Validate models with training/validation data - Apply honest assessment principles - Create and interpret ROC, lift, and gains charts - Determine optimal decision cut-off values - Use confusion matrices and classification metrics |
| Topic 2: Linear Regression | 20% | - Perform model selection with GLMSELECT - Interpret REG, GLM, and PLM procedure output - Conduct residual and diagnostic analysis - Fit multiple linear regression models |
| Topic 3: Analysis of Variance (ANOVA) | 10% | - Assess ANOVA assumptions - Use PROC GLM for ANOVA - Analyze factor interactions - Perform post-hoc tests |
| Topic 4: Logistic Regression | 25% | - Build models using PROC LOGISTIC - Select and optimize input variables - Interpret odds ratios and model coefficients - Score new datasets with LOGISTIC and PLM |
| Topic 5: Prepare Inputs for Predictive Modeling | 20% | - Screen variables with CORR procedure and empirical logit plots - Identify data preparation challenges - Transform categorical variables - Manipulate data using DATA step |
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