From the course: Predictive Analytics with Categorical Data: Advanced Regression Methods for Real-World Applications
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Understanding marginal effects
From the course: Predictive Analytics with Categorical Data: Advanced Regression Methods for Real-World Applications
Understanding marginal effects
- [Instructor] Regression models are only useful if their results can be interpreted and applied in the real world. And this is often done with marginal effects. Marginal effects show the relationship between independent variables and the dependent variable. They linearize the result and allow you to understand how changes in X affect percentage changes in Y. It's important because humans are quite bad at understanding nonlinear processes. With marginal effects, you can make sense of such complex nonlinear processes. Marginal effects are closely related to slope coefficients in regression models. They indicate the change in the dependent variable for a one-unit increase in an independent variable. In linear regression models, marginal effects are constant and equal to the slope coefficient. And that's because linear coefficients are already linear. Transforming them results in the same outcome. Here's an example. This is a very simple regression of one explanatory variable X on a…
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