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SASInstitute A00-240 certification exam is an excellent way for professionals to demonstrate their knowledge and expertise in statistical analysis and modeling using SAS 9. SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential certification is recognized globally, and can help professionals advance their careers by demonstrating their skills to potential employers. With the right preparation and study, candidates can successfully pass the exam and earn the SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential.

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

The A00-240 exam is intended for professionals who work with data and use SAS software for analyzing and modeling data. This includes business analysts, data analysts, statisticians, and other professionals who work in industries such as finance, healthcare, marketing, and retail. A00-240 exam covers a broad range of topics, including statistical analysis, regression modeling, predictive modeling, and data visualization.

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SASInstitute A00-240 exam is designed to assess the candidate's knowledge and skills in regression and modeling using SAS 9. This credential is intended for individuals who want to demonstrate their expertise in statistical analysis and modeling using SAS software. A00-240 exam covers a wide range of topics, including data preparation, regression analysis, model selection, and model assessment.

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SASInstitute A00-240 Exam Syllabus Topics:

SectionWeightObjectives
Analysis of Variance (ANOVA)10%- Analyze factor interactions
- Assess ANOVA assumptions
- Perform post-hoc tests
- Use PROC GLM for ANOVA
Prepare Inputs for Predictive Modeling20%- Transform categorical variables
- Screen variables with CORR procedure and empirical logit plots
- Identify data preparation challenges
- Manipulate data using DATA step
Measure and Evaluate Model Performance25%- Determine optimal decision cut-off values
- Use confusion matrices and classification metrics
- Create and interpret ROC, lift, and gains charts
- Validate models with training/validation data
- Apply honest assessment principles
Logistic Regression25%- Interpret odds ratios and model coefficients
- Select and optimize input variables
- Build models using PROC LOGISTIC
- Score new datasets with LOGISTIC and PLM
Linear Regression20%- Perform model selection with GLMSELECT
- Conduct residual and diagnostic analysis
- Interpret REG, GLM, and PLM procedure output
- Fit multiple linear regression models

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