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What is the duration, language, and format of A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam

  • Length of Examination: 120 minutes
  • 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)
  • 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

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

To prepare for the SASInstitute A00-240 certification exam, candidates can take advantage of various resources, such as SAS training courses, online tutorials, practice exams, and study guides. These resources can help candidates gain the knowledge and skills needed to pass the exam and earn the certification. Additionally, candidates can benefit from hands-on experience in using SAS software to perform data analysis and modeling, which can help them better understand the concepts and techniques covered in the exam.

SASInstitute A00-240 certification is a valuable credential for professionals who work in the fields of business analysis, data analysis, and data management. SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential certification demonstrates the candidate's proficiency in statistical analysis and modeling using SAS 9, and it can help to enhance their career prospects. SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential certification is recognized globally and is highly respected within the industry.

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SASInstitute A00-240 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Logistic Regression25%- Develop Logistic Models
  • 1. Optimize model performance through input selection
  • 2. Perform logistic regression using the LOGISTIC procedure
- Interpret and Deploy Models
  • 1. Score new data sets using LOGISTIC and PLM procedures
  • 2. Interpret LOGISTIC procedure output
Topic 2: Measure Model Performance25%- Decision Optimization
  • 1. Evaluate scoring strategies
  • 2. Establish effective decision cutoff values
- Performance Visualization
  • 1. Create and interpret lift charts
  • 2. Create and interpret gains charts
  • 3. Create and interpret ROC charts
- Model Assessment
  • 1. Apply principles of honest assessment
  • 2. Assess classifier performance using confusion matrices
- Validation and Selection
  • 1. Compare and select predictive models
  • 2. Perform model validation using training and validation data
Topic 3: ANOVA10%- ANOVA Concepts and Assumptions
  • 1. Verify ANOVA assumptions
  • 2. Identify appropriate ANOVA methods
- ANOVA Analysis
  • 1. Analyze differences between population means using GLM and TTEST procedures
  • 2. Perform post hoc tests
  • 3. Detect and analyze factor interactions
  • 4. Evaluate treatment effects
Topic 4: Prepare Inputs for Predictive Model Performance20%- Data Preparation
  • 1. Manipulate data using DATA step programming
  • 2. Identify challenges in preparing model input data
  • 3. Use loops, arrays, functions and conditional statements
- Variable Engineering and Screening
  • 1. Screen variables for irrelevance
  • 2. Improve predictive power of categorical variables
  • 3. Use empirical logit plots for nonlinearity detection
  • 4. Identify nonlinear associations using CORR procedure
Topic 5: Linear Regression20%- Build Regression Models
  • 1. Use REG and GLM procedures
  • 2. Fit multiple linear regression models
- Interpret Regression Results
  • 1. Evaluate parameter estimates
  • 2. Interpret REG, PLM and GLM output
- Model Selection and Diagnostics
  • 1. Assess model validity
  • 2. Perform model selection using REG or GLMSELECT
  • 3. Conduct residual and diagnostic analysis

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