just keep hitting run to go through each step and you can see the it' effect as our third-degree polynomial is definitely not as good fit as the fourth-degree polynomial if you actually measure the -squared errorit would actually turn out worsequantitativelybut if go too highyou might start to see overfitting so just have some fun with thatplay around different valuesand get sense of what different orders of polynomials do to your regression go get your hands dirty and try to learn something so that' polynomial regression againyou need to make sure that you don' put more degrees at the problem than you need to use just the right amount to find what looks like an intuitive fit to your data too many can lead to overfittingwhile too few can lead to poor fit so you can use both your eyeballs for nowand the -squared metricto figure out what the right number of degrees are for your data let' move on multivariate regression and predicting car prices what happens thenif we're trying to predict some value that is based on more than one other attributelet' say that the height of people not only depends on their weightbut also on their genetics or some other things that might factor into it wellthat' where multivariate analysis comes in you can actually build regression models that take more than one factor into account at once it' actually pretty easy to do with python