In the previous post we have analyzed an example of simple linear regression: a set of machine learning algorithms and techniques able to predict an output variable given a single independent variable, therefore through a linear function like Y = c1 + c2X.
Today we are going to see its advanced extension, that is: how to predict Y as a function of multiple linear independent variables (X1, X2, X3 etc … etc …). This type of model is also called multiple linear regression (MLR).
We can reuse the dataset relating to blood tests carried out on Australian professional athletes related to various sports few years ago: reference Telford, R.D. and Cunningham, R.B. 1991 – sex, sport and dependence of hematology on body dimensions in highly trained athletes. Sports medicine and science 23: 788-794.
The dataset contains 13 features related to 202 observations.
Here is the feature description:
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