polynomial regression we've talked about linear regression where we fit straight line to set of observations polynomial regression is our next topicand that' using higher order polynomials to fit your data sosometimes your data might not really be appropriate for straight line that' where polynomial regression comes in polynomial regression is more general case of regression so why limit yourself to straight linemaybe your data doesn' actually have linear relationshipor maybe there' some sort of curve to itrightthat happens pretty frequently not all relationships are linearbut the linear regression is just one example of whole class of regressions that we can do if you remember the linear regression line that we ended up with was of the form mx bwhere we got back the values and from our linear regression analysis from ordinary least squaresor whatever method you choose now this is just first order or first-degree polynomial the order or the degree is the power of that you see so that' the first-order polynomial now if we wantedwe could also use second-order polynomialwhich would look like ax^ bx if we were doing regression using second-order polynomialwe would get back values for aband or we could do third-order polynomial that has the form ax^ bx^ cx the higher the orders getthe more complex the curves you can represent sothe more powers of you have blended togetherthe more complicated shapes and relationships you can get but more degrees aren' always better usually there' some natural relationship in your data that isn' really all that complicatedand if you find yourself throwing very large degrees at fitting your datayou might be overfittingbeware of overfittingdon' use more degrees than you need visualize your data first to see how complex of curve there might really be visualize the fit and check if your curve going out of its way to accommodate outliers high -squared simply means your curve fits your training data wellit may or may not be good predictor