The Challenge I want to explore the statistics of RNA sequencing (RNA-seq) on next-generation sequencing (NGS) platforms in greater detail, so I thought I’d start by simulating read counts to experiment with. This post details how I constructed a simulated set of read counts, examines its concordance with the expected negative binomial distribution of the […]

# Tag: statistics

## reporting negative results

Two of my recent posts have reported negative results, meaning that no meaningful effects were found during the investigations. Had these investigations been framed as hypothesis tests, we would have failed to reject the null hypotheses. Sounds boring. However there are good reasons to report these results. The first is that negative results still generate […]

## fuzzy logic toolkit in C++

I recently came across some old C++ code I wrote about 10 years ago to assist fuzzy logic reasoning. This program is now posted on GitHub at https://github.com/badassdatascience/fuzzy-logic-toolkit and is described below. An example of the tool in action (with code) follows the description. Fuzzy Logic Suppose we have a numerical value for distance to […]

## Apache Spark and stock price causality

The Challenge I wanted to compute Granger causality (described below) for each pair of stocks listed in the New York Stock Exchange. Moreover, I wanted to analyze between one and thirty lags for each pair’s comparison. Needless to say, this requires massive computing power. I used Amazon EC2 as the computing platform, but needed a […]

## hacking the stock market (part 1)

Caveat: I am not a technical investor–just a hobbyist, so take this analysis with a grain of salt. I am also just beginning with my Master’s work in statistics. I wanted to examine the correlation between changes in the daily closing price of the Dow Jones Industrial Average (DJIA) and lags of those changes, to […]

## Kaplan-Meier estimator in Python

The following Python class computes and draws Kaplan-Meier product limit estimators for given data. An example of how to use the class follows the code. Code Usage Example

## GNU Octave: a free, open source MATLAB-like language for numerical computing

I tend to use Python with the Numpy, SciPy, and Matplotlib stack whenever I have to do scientific computing. For statistical computing I use R whenever this Python stack does not provide the necessary features. However, I want to draw readers’ attention to another tool for free, open-source numerical computing: GNU Octave (hereafter called “Octave”), […]

## maximized entropy of a finite distribution

I received the following tweet yesterday from @ProbFact and decided to check it out in more detail: Two-Dimensional Case I generated the following test to investigate the claim: Create four category discrete distributions where two of the categories have 0.25 probability each, and the third category probability varies between 0.1 and 0.4. The fourth category’s […]

## selecting travel trailers by regression

Data Scientist has been thinking recently of moving into a used travel trailer. However, the weight of the trailer to be purchased is limited by that which our hero’s truck can pull. But most online used travel trailer listings only specify length of the vehicle, not its weight. So Data Scientist needed a quick way […]

## sorting out R’s distribution functions

R’s distribution functions come in four flavors: “d”, “p”, “q”, and “r” (e.g., “dnorm”, “pnorm”, “qnorm”, and “rnorm”). I regularly get them mixed up, so am writing down here what they do for future reference. Density “d” produces the density curve as a function of a random variable. For example, “dnorm” produces: Cumulative Distribution Function […]