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Tweak model outline

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hadley 2016-07-24 14:48:23 -05:00
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@ -12,10 +12,10 @@ The goal of a model is to provide a simple low-dimensional summary of a dataset.
This book is not going to give you a deep understanding of the mathematical theory that underlies models. It will, however, build your intution about how statisitcal models work, and give you a family of useful tools that allow you to use models to better understand your data: This book is not going to give you a deep understanding of the mathematical theory that underlies models. It will, however, build your intution about how statisitcal models work, and give you a family of useful tools that allow you to use models to better understand your data:
* In [model basics], you'll learn how models work, focussing on the important * In [model basics], you'll learn how models work mechanistically, focussing on
family of linear models. You'll learn general tools for gaining insight the important family of linear models. You'll learn general tools for gaining
into what a predictive model tells you about your data, focussing on simple insight into what a predictive model tells you about your data, focussing on
simulated datasets. simple simulated datasets.
* In [model building], you'll learn how to use models to pull out known * In [model building], you'll learn how to use models to pull out known
patterns in real data. Once you have recognised an important pattern patterns in real data. Once you have recognised an important pattern
@ -26,11 +26,12 @@ This book is not going to give you a deep understanding of the mathematical theo
understand complex datasets. This is a powerful technique, but to access understand complex datasets. This is a powerful technique, but to access
it you'll need to combine modelling and programming tools. it you'll need to combine modelling and programming tools.
* In [model assessment], you'll learn a little a bit about how you might * In [model assessment], you'll learn more about the statistical side of
quantitatively assess whether a model is good or not. You'll learn two modelling. Ideally, you don't just want a model that works just with the
powerful techniques, cross-validation and bootstrapping, that are built data that you've observe, but also generalises to new situations. You'll
on the idea of generating many random datasets which you fit many learn two powerful techniques, cross-validation and bootstrapping, built
models to. on the powerful idea of random resamples. These will help you understand
how your model will behave on new datasets.
In this book, we are going to use models as a tool for exploration, completing the trifacta of EDA tools introduced in Part 1. This is not how models are usually taught, but they make for a particularly useful tool in this context. Every exploratory analysis will involve some transformation, modelling, and visualisation. In this book, we are going to use models as a tool for exploration, completing the trifacta of EDA tools introduced in Part 1. This is not how models are usually taught, but they make for a particularly useful tool in this context. Every exploratory analysis will involve some transformation, modelling, and visualisation.