Statistical Software Matters
This is a picture of all the genetic associations found in genome-wide association studies, sorted by chromosome. You can find more detail at the NHGRI GWAS catalog     There are two chromosomes with many fewer associations. One is the Y chromosome. There isn’t much there because there isn’t much... Read more
Partition numbers and Ramanujan’s approximation
The partition function p(n) counts the number of ways n unlabeled things can be partitioned into non-empty sets. (Contrast with Bell numbers that count partitions of labeled things.) There’s no simple expression for p(n), but Ramanujan discovered a fairly simple asymptotic approximation: How accurate is this approximation? Here’s a little Matheamtica code to see. p := PartitionsP... Read more
Talking About Clinical Significance
In statistical work in the age of big data we often get hung up on differences that are statistically significant (reliable enough to show up again and again in repeated measurements), but clinically insignificant (visible in aggregation, but too small to make any real difference to individuals). An example would be: a diet... Read more
Stirling Numbers, Including Negative Arguments
Stirling numbers are something like binomial coefficients. They come in two varieties, imaginatively called the first kind and second kind. Unfortunately it is the second kind that are simpler to describe and that come up more often in applications, so we’ll start there. Stirling numbers of the second kind... Read more
Fixed Points of Logistic Function
Here’s an interesting problem that came out of a logistic regression application. The input variable was between 0 and 1, and someone asked when and where the logistic transformation f(x) = 1/(1 + exp(a + bx)) has a fixed point, i.e. f(x) = x. So given logistic regression parameters a and b, when does the logistic curve... Read more
Relative Error in the Central Limit Theorem
If you average a large number independent versions of the same random variable, the central limit theorem says the average will be approximately normal. That is the absolute error in approximating the density of the average by the density of a normal random variable will be small. (Terms and conditions apply.... Read more
Quantifying Uncertainty with Bayesian Statistics
Whenever we’re working with data, there is necessarily uncertainty in our results. Firstly, we can’t collect all the possible data, so instead we randomly sample from a population. Accordingly, there is a natural variance and uncertainty in any data we collect. There is also uncertainty from missing data, systematic... Read more
Robustness and Tests for Equal Variance
The two-sample t-test is a way to test whether two data sets come from distributions with the same mean. I wrote a few days ago about how the test performs under ideal circumstances, as well as less than ideal circumstances. This is an analogous post for testing whether two data sets come... Read more
“I hate math!” – Education and Artificial Intelligence to find a meaning
Well, what you hate is the way that math was taught to you. That soup of equations, abstractions, and solutions to problems that we don’t know, It’s hard to enjoy the things we don’t feel part of. But how about relating some math techniques from the world that surrounds... Read more
Least Squares Solutions to Over- or Underdetermined Systems
It often happens in applications that a linear system of equations Ax = b either does not have a solution or has infinitely many solutions. Applications often use least squares to create a problem that has a unique solution. Overdetermined systems Suppose the matrix A has dimensions m by n and the right hand side vector b has dimension m. Then the solution x, if... Read more