Linear regression
Ordinary Least Squares (OLS).
We will start with the simplest model of linear regression, where we will simply try to fit the best straight line through the data points we have available. Recall that the formula for linear regression is:
Where y is a vector of n responses we are trying to predict, X is a vector of our input variable also of length n, and β is the slope response (how much the response y increases for each 1-unit increase in the value of X). However, we rarely have only a single input; rather, X will represent a set of input variables, and the response y is a linear combination of these inputs. In this case, known as multiple linear regression, X is a matrix of n rows (observations) and m columns (features), and β is a vector set of slopes or coefficients which, when multiplied by the features, gives the output. In essence, it is just the trend line incorporating many inputs, but will also allow us to compare the magnitude effect of different...
