Numerical Analysis for Statisticians


Numerical Analysis for Statisticians by Kenneth Lange 2010
Although this book doesn’t have any code in in it, it is still useful. The theory and equations are well defined and easy enough to read.
I went to a talk on FFT and Python at OSCON 2013. Sound Analysis with the Fourier Transform and Python, given by Caleb Madrigal.
Chapter 19 on Fourier Transforms goes along nicely with the talk. Caleb presented the formulas and talked about which ones to use. This book gives you all the details you need for choosing formulas and libraries when implementing Fourier Transforms.

A Short History of Random Numbers, and Why You Need to Care given by Matthew Garrett, was another talk that I went to. Chapter 22 Generating Random Deviates is a nice over view of some of the material covered in the talk.

In general this is a good book, I just wish that it had some code examples, pseudo code, algorithms etc. It is not easy to take equations and turn them into code.

Instant PostgreSQL Starter

Author Daniel K Lyons published by Pakct Publishing
I wish that his book would of been available when I first started using PostgreSQL, it would of saved me a lot of trouble.
The Installation instructions are straight forward. The quick start section has clear SQL instructions.
Top 9 features you need to know about covers, things like properly storing passwords, encryption using pgcrypto and backup and restore which are necessary for all databases.


Wow this works sweet. Thank you Six Sigma with R.
I have an Excel worksheet that I need to analyze. They are not always to smoothest thing to read into R.
I just downloaded and used XLConnect. First try exactly is what I wanted.

dummy code
library (XLConnect)
wb <- loadWorkbook(“toyprob.xls”)
data.toyprob <- readWorksheet(wb, sheet = 3)
str (data.toyprob)

this side is the object <- what it is assigned to

R Error Messages

I spent a good part of yesterday trying to figure what an error message meant. I was trying to draw a classification tree. I kept getting an environmental error message. I couldn’t figure out what was wrong. I searched for answer, only to find nothing useful. Then I remembered about vectorization and turning my data into a data object. I didn’t think I needed it here since I was following the example exactly. But I did.
Useful information on Data objects is in Six Sigma with R, Emilio Cano, Javier Moguerza and Andres Redchuk; Chapter 2.4.

Useful information on subsetting is in R Cookbook, Paul Teetor; Chapter 5.24

examples of what worked.
toycat <-subset(datatoycat, select= c(animal,eye,fur, legs))

toy <- rpart(toycat, method = "class")

Ignite OSCON

I am presenting at Ignite OSCON 2013. Is There a Cat in Here, Data Mining with Toys. I am busy working on my slides. It is difficult to condense data mining into 20 slides in five minutes. I am having fun doing this. I have lots of great pictures for my slides. Books that I have been using for the theory and practice of data mining are: The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman, Springer Press. And one that I now owe the library fines on, Introduction to Algorithms by Thomas Cormen