Who should take the A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam
Individuals should pursue the A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam if they want to demonstrate their expertise and ability to access and manage data to perform queries and analyses from A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Certification
- Business Analyst
- Stastical Analyst, or
- Anyone writing & interested in Statistical Business Analysis
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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
- Format: Multiple choices, multiple answers , short answers
- Passing score: 68% (Must achieve score of 68 percent correct to pass)
- 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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A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Certification Path
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.
SASInstitute A00-240 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: ANOVA | 10% | - ANOVA Concepts and Assumptions
|
| Topic 2: Measure Model Performance | 25% | - Validation and Selection
|
| Topic 3: Linear Regression | 20% | - Model Selection and Diagnostics
|
| Topic 4: Prepare Inputs for Predictive Model Performance | 20% | - Variable Engineering and Screening
|
| Topic 5: Logistic Regression | 25% | - Interpret and Deploy Models
|




