SASInstitute A00-240 Q&A - in .pdf

  • Exam Code: A00-240
  • Exam Name: SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential
  • Q & A: 100 Questions and Answers
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Predictive Modeling Using Logistic Regression Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression

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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.

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

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SASInstitute A00-240 Exam Syllabus Topics:

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

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