We call the output of the model a point estimate because it is a point on the continuum of possibilities. This model equation gives a line of best fit, which can be used to produce estimates of a response variable based on any value of the predictors ( within reason). The most noticeable aspect of a regression model is the equation it produces. ( Not that any model will be perfect for this!) Furthermore:įitting a model to your data can tell you how one variable increases or decreases as the value of another variable changes.įor example, if we have a dataset of houses that includes both their size and selling price, a regression model can help quantify the relationship between the two. There are all sorts of applications, but the point is this: If we have a dataset of observations that links those variables together for each item in the dataset, we can regress the response on the predictors.
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