Posted on Categories Expository Writing, Mathematics, Opinion, Statistics, Tutorials1 Comment on Relative error distributions, without the heavy tail theatrics

## Relative error distributions, without the heavy tail theatrics

Nina Zumel prepared an excellent article on the consequences of working with relative error distributed quantities (such as wealth, income, sales, and many more) called “Living in A Lognormal World.” The article emphasizes that if you are dealing with such quantities you are already seeing effects of relative error distributions (so it isn’t an exotic idea you bring to analysis, it is a likely fact about the world that comes at you). The article is a good example of how to plot and reason about such situations.

I am just going to add a few additional references (mostly from Nina) and some more discussion on log-normal distributions versus Zipf-style distributions or Pareto distributions. Continue reading Relative error distributions, without the heavy tail theatrics

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## Did she know we were writing a book?

Writing a book is a sacrifice. It takes a lot of time, represents a lot of missed opportunities, and does not (directly) pay very well. If you do a good job it may pay back in good-will, but producing a serious book is a great challenge.

Nina Zumel and I definitely troubled over possibilities for some time before deciding to write Practical Data Science with R, Nina Zumel, John Mount, Manning 2014.

In the end we worked very hard to organize and share a lot of good material in what we feel is a very readable manner. But I think the first-author may have been signaling and preparing a bit earlier than I was aware we were writing a book. Please read on to see some of her prefiguring work. Continue reading Did she know we were writing a book?

Posted on Categories Statistics, Tutorials5 Comments on Variables can synergize, even in a linear model

## Introduction

Suppose we have the task of predicting an outcome `y` given a number of variables `v1,..,vk`. We often want to “prune variables” or build models with fewer than all the variables. This can be to speed up modeling, decrease the cost of producing future data, improve robustness, improve explain-ability, even reduce over-fit, and improve the quality of the resulting model.

For some informative discussion on such issues please see the following:

In this article we are going to deliberately (and artificially) find and test one of the limits of the technique. We recommend simple variable pruning, but also think it is important to be aware of its limits.