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Multiple regression

Python notebook: https://github.com/daviskregers/data-science-recap/blob/main/11-multiple-regression.ipynb

  • What if more than one variable influences the one you're interested in?
  • Example: predicting a price for a car based on it's many attributes (body style, brand, mileage, etc)
  • If you also have multiple dependent variables - things you're trying to predict - that's a "multivariate regression"

It sill uses least squares

  • We just end up with coefficients for each factor.
    • For example, price \(\alpha+\beta_1mileage + \beta_2age + \beta_3doors\)
    • These coefficients imply how important each factor is (if the data is all normalized!)
    • Get rid of ones that doesn't matter!
  • Can still measure fit with r-squared
  • Need to assume the different factors are not themselves dependent on each other.