Bayesian Analyis with Python Chapter 4, Understanding and Predicting Data with Linear Regression Models
Simple Linear Regression Continuous variable - a variable using real numbers or floats (dependent, predicted, outcome) Independent variable - can be continous or categorical (predictor, input) We can can model this relationship with linear regression. With multiple independent variables, we will use multiple regression models. The machine learning connection Machine learning (ml) is the umbrella term for a collection of methods to automatically learn patterns in data. Regression is a supervised learning problem because we know the x and y values. The question is how to generalize these observations to any future observation. The core of linear regression models Beta is the slope of the line, changer per unit change in x. Alpha is the value of y when x = 0. When we try to solve this problem we use the least squares model. We can also use a Bayesian framework. This has several advantages: we can obtain the best values of alpha and beta capture u...