A brief note on overdispersion Assumptions Poisson distribution assume variance is equal to the mean. Quasi-poisson model assumes variance is a linear function of mean. Negative binomial model assumes variance is a quadratic function of the mean. rstats implementation #to test you need to fit a poisson GLM then apply ... [Read more...]

Sinew is a R package that generates a roxygen2 skeletons populated with information scraped from within the function script. The goal of the package is to automate nearly all of the mundane tasks needed to document functions, properly set up the import fields for oxygenation, and make it easier to ... [Read more...]

Operations need to have demand forecasts in order to establish optimal resource allocation policies. But, when we make predictions the only thing that we assure is the occurrence of prediction errors. Fortunately, there is no need to be 100% accurate to succeed, we just need to perform better than our competitors. ... [Read more...]

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On 21-22 April, the London School of Economics hosted the Text Analysis Package Developers' Workshop, a two-day event held in London that brought together developers of R packages for working with text and text-related data. This included a wide range of applications, including string handling (stringi) and tokenization (the rOpenSci-onboarded ... [Read more...]

A R named list is not a python dictionary¶In the previous post, mpiktas posted a comment that included this question "Isn't Python dictionary a named list in R?". The short answer is no, but the reason why is worth some more explanation. At first sight, a named list does ... [Read more...]

Do you know Lucy? She is a very talented biostatistics PhD candidate that I had the chance to e-meet thanks to R-Ladies. One maybe superficial reason to admire her, on top of her other achievements, is her emoji game in git commits. Looking at Lucy’s... [Read more...]

I regularly get asked why I don’t consider monthly seasonality in my models for daily or sub-daily time series. For example, this recent comment on my post on seasonal periods, or this comment on my post on daily data. The fact is, I’ve never seen a time series ... [Read more...]

Do you know Lucy? She is a very talented biostatistics PhD candidate that I had the chance to e-meet thanks to R-Ladies. One maybe superficial reason to admire her, on top of her other achievements, is her emoji game in git commits. Looking at Lucy’s... [Read more...]

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I regularly get asked why I don’t consider monthly seasonality in my models for daily or sub-daily time series. For example, this recent comment on my post on seasonal periods, or this comment on my post on daily data. The fact is, I’ve never seen a time series ... [Read more...]

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Uncertainty is the biggest enemy of a profitable business. That is especially true of small business who don’t have enough resources to survive an unexpected diminution of revenue or to capitalize on a sudden increase of demand. In this context, it is especially important to be able to predict ... [Read more...]

There's a reason why data scientists spend so much time exploring data using graphics. Relying only on data summaries like means, variances, and correlations can be dangerous, because wildly different data sets can give similar results. This is a principle that has been demonstrated in statistics classes for decades with ... [Read more...]

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Around 8:30pm I saw this tweet and duly retweeted https://t.co/WuyU9D6npK — Richie Cotton (@richierocks) May 1, 2017 It turns out awesome folks, George and Justin, had made a process whereby they can generate different distributions of The post The making of datasauRus appeared first on Locke Data. Locke ... [Read more...]

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An oft asked question is what resources can I recommend for getting started with data science? Here are my recommendations, and if you have others, please put them in the comments! NB Links in this post may be affiliate links The post Getting started with data science – recommended resources appeared ... [Read more...]