Linear regression is inappropriate to model binary responses such as pass/fail, alive/dead.
Learn the principle of logistic regression, its similarities with linear regression and its specific tools. Good practices for model-building are presented.
- Goal: To Study the Relationship between a Categorical Variable and a Set of Explanatory Variables
- Why Does Ordinary Multiple Linear Regression Fail for the Analysis of a Categorical Response Variable?
- Definition and Estimation of the Model
- Interpretation of model coefficients
- Goodness-of-Fit and Validation Techniques
- Basic Principle: Modeling the probability of observing a given value of the reponse variable
- Interpretation of Statistical Software Output: Coefficients and Mathematical Transformations, Odds Ratios, Statistical Testing of Model Coefficients
- Comparison of Logistic Regression Software Output with Multiple Linear Regression
- Goodness-of-Fit Measures: Nested Models, Cross-Validation Techniques
- Using the Model for Predictive Purposed
- Procedures Available in Statistical Software
- Implementation and Interpretation
- Understand the context of use of logistic regression
- Understand why ordinary regression fails for the modeling of categorical variables
- Construct a logistic regression model
- Assess the goodness-of-fit of the model to the data
- Identify common issues in logistic regression, diagnose problems and fix them
- Interpret statistical software output
Recommended Duration: 1 day(s)