![]() With this, we can capture nonlinear relationships in our data using polynomial functions, instead of just However, we can perform a small adjustment and use polynomial regression instead. No plain linear regression model can capture this In this case, we need to use a different model. We can’t just cut off one half of the dataset, so what can we do instead? But the data does not seem to follow the same trend throughout the entire dataset. The two features (number of hours studied) and the target (number of points achieved). With linear regression, we can only draw a straight line (a linear function) to model the relationship between More of a downward trend, in contrast to the interval between 40 and 60 hours studied. When you take a look at the interval between 0 and 30 hours studied, it does seem to have However, the linear relationship is not that apparent throughout the entire graph. ![]() ![]() This relationship does seem somewhat linear,Įspecially when considering the segment between 40 and 60 hours studied. Number of hours studied and the number of points achieved. There definitely seems to be a relationship between the You look at the plot and notice a couple of things. ![]()
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