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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.
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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.
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SASInstitute A00-240 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Analysis of Variance (ANOVA) | 10% | - Analyze factor interactions - Use PROC GLM for ANOVA - Perform post-hoc tests - Assess ANOVA assumptions |
| Topic 2: Linear Regression | 20% | - Conduct residual and diagnostic analysis - Interpret REG, GLM, and PLM procedure output - Fit multiple linear regression models - Perform model selection with GLMSELECT |
| Topic 3: Measure and Evaluate Model Performance | 25% | - Apply honest assessment principles - Validate models with training/validation data - Use confusion matrices and classification metrics - Create and interpret ROC, lift, and gains charts - Determine optimal decision cut-off values |
| Topic 4: Prepare Inputs for Predictive Modeling | 20% | - Screen variables with CORR procedure and empirical logit plots - Identify data preparation challenges - Manipulate data using DATA step - Transform categorical variables |
| Topic 5: Logistic Regression | 25% | - Score new datasets with LOGISTIC and PLM - Select and optimize input variables - Interpret odds ratios and model coefficients - Build models using PROC LOGISTIC |
