Last year, I wrote two blog posts about power analysis for designed experiments. Since then, JMP 11 was released, and the user interface for power analysis in the Custom Designer changed. This post introduces the new interface and shows how to use it profitably. Why did you change the interface?
For college football, the regular season is coming to an end, and in a few days we’ll know which teams are going to which end-of-season bowl games. Though some bowl assignments are determined by formula, each bowl often has a choice of several teams to invite to their game. Some
In celebration of the International Year of Statistics, the final statistician we celebrate is Karl Pearson. His work in the late 19th and early 20th centuries laid the structure of mathematical statistics. Born March 27, 1857, in London, England, Pearson was raised in an upper-middle class family. He studied mathematics
A penalty can seriously ruin your day. Forget to pay a bill on time, and a late penalty will cost you a few more dollars. When a yellow flag hits the football field, a penalty can cost your favorite team field position, momentum and maybe even some points. But a penalty
To benchmark computer performance on statistical methods with big data, we can just generate random data and measure performance on that, right? Well, it could be that simulated data may not act the same as real data. Let’s find out. Logistic Regression Suppose that we are benchmarking logistic regression. So
Statistics wouldn’t be what it is today without Johann Carl Friedrich Gauss, a German scientist. He was born April 30, 1777, in Braunschweig and died Feb. 21, 1855, in Göttingen. Born as the only son of a poor worker’s family with illiterate parents, he was a child prodigy. Luckily, teachers,
Sometimes emptiness is meaningful. If a loan applicant leaves his debt and salary fields empty, don’t you think that emptiness is meaningful? If a job applicant leaves this previous job field empty, don’t you think that emptiness is meaningful? If a political candidate fills out a form that has an
When you have millions of observations of real data and do a simple fit across two variables, if you don’t get a significant test, then it is strong evidence of fraud. The one kind of data that is reliably non-significant for very tall data tables is simulated data. We live
In semiconductor data, it is common for probe measurements that encounter an electrical short to exhibit measurements that are far out in the distribution, i.e., they are outliers. When we test that means are the same, these outlying values inflate our estimate of the standard deviation [sigma]. Remember that the
Purely random data has a 5% chance of being significant. Choose the most significant p-values from many tests of random data, and you will filter out the tests that are significant by chance alone. Suppose we have a process that we know is stable and consistent. We measure lots of