From fecd77847afb3795e245591a6379ef4392516860 Mon Sep 17 00:00:00 2001 From: hadley Date: Sun, 24 Jul 2016 14:48:23 -0500 Subject: [PATCH] Tweak model outline --- model.Rmd | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/model.Rmd b/model.Rmd index b9ac9cb..6e76dba 100644 --- a/model.Rmd +++ b/model.Rmd @@ -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: -* In [model basics], you'll learn how models work, focussing on the important - family of linear models. You'll learn general tools for gaining insight - into what a predictive model tells you about your data, focussing on simple - simulated datasets. +* In [model basics], you'll learn how models work mechanistically, focussing on + the important family of linear models. You'll learn general tools for gaining + insight into what a predictive model tells you about your data, focussing on + simple simulated datasets. * 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 @@ -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 it you'll need to combine modelling and programming tools. -* In [model assessment], you'll learn a little a bit about how you might - quantitatively assess whether a model is good or not. You'll learn two - powerful techniques, cross-validation and bootstrapping, that are built - on the idea of generating many random datasets which you fit many - models to. +* In [model assessment], you'll learn more about the statistical side of + modelling. Ideally, you don't just want a model that works just with the + data that you've observe, but also generalises to new situations. You'll + learn two powerful techniques, cross-validation and bootstrapping, built + 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.