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Predictive Modeling Using Logistic Regression Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression

The benefits of obtaining A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam Certification

Getting a professional certification in the SAS domain is indeed a wise decision as there are immense benefits of this credential.

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  • A00-240: Statistical Business Analysis improves reasoning power and promotes analytical thinking and research ability of appearing professional.

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

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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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SASInstitute A00-240 Exam Syllabus Topics:

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

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