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Predictive Modeling Using Logistic Regression
Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression
Reference: https://www.sas.com/en_us/certification/credentials/advanced-analytics/statistical-business-analyst.html
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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.
SASInstitute A00-240 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Measure Model Performance | 25% | - Model Assessment
- 1. Assess classifier performance using confusion matrices
- 2. Apply principles of honest assessment
- Validation and Selection
- 1. Compare and select predictive models
- 2. Perform model validation using training and validation data
- Decision Optimization
- 1. Establish effective decision cutoff values
- 2. Evaluate scoring strategies
- Performance Visualization
- 1. Create and interpret lift charts
- 2. Create and interpret ROC charts
- 3. Create and interpret gains charts
|
| Linear Regression | 20% | - Model Selection and Diagnostics
- 1. Assess model validity
- 2. Perform model selection using REG or GLMSELECT
- 3. Conduct residual and diagnostic analysis
- 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
|
| ANOVA | 10% | - ANOVA Concepts and Assumptions
- 1. Identify appropriate ANOVA methods
- 2. Verify ANOVA assumptions
- ANOVA Analysis
- 1. Detect and analyze factor interactions
- 2. Evaluate treatment effects
- 3. Perform post hoc tests
- 4. Analyze differences between population means using GLM and TTEST procedures
|
| Prepare Inputs for Predictive Model Performance | 20% | - Data Preparation
- 1. Manipulate data using DATA step programming
- 2. Use loops, arrays, functions and conditional statements
- 3. Identify challenges in preparing model input data
- Variable Engineering and Screening
- 1. Improve predictive power of categorical variables
- 2. Use empirical logit plots for nonlinearity detection
- 3. Identify nonlinear associations using CORR procedure
- 4. Screen variables for irrelevance
|
| Logistic Regression | 25% | - Interpret and Deploy Models
- 1. Interpret LOGISTIC procedure output
- 2. Score new data sets using LOGISTIC and PLM procedures
- Develop Logistic Models
- 1. Perform logistic regression using the LOGISTIC procedure
- 2. Optimize model performance through input selection
|