chapter-03/3.1-introduction.ipynb

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    "# 3.1 Introduction"
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    "A linear regression model assumes that the regression function E(Y|X) is linear in the inputs $X_1,...,X_p$.\n",
    "For prediction purposes they can sometimes outperform nonlinear models, e.g in situations with small numbers of training cases, low signal-to-noise ratio or sparse data. Finally, linear methods can be applied to transformations of inputs and this considerably expands their scope."
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