- The picture above is a very well-known mathematical construction called the fractal cat. Brian Lee Yung Rowe shows how to construct fractal artworks using R.
- Arthur Charpentier of Freakonometrics explains how to construct ROC (
rate of changeReceiver Operating Characteristic) curves in R, as well as how to interpret and plot them. This is a useful for those in fields that frequently encounter longitudinal data, such as finance, engineering or biostatistics.
- There are many kinds of intervals in statistics. To name a few of the common ones: confidence intervals, prediction intervals, credible intervals, and tolerance intervals. Each are useful and serve their own purpose. You should not only know their names, but also when to use them and why.
- A map of the most visited website for every country in the world (source: Alexa.com), as well as the internet population of each country.
- Suppose that you drop 5 blue marbles and 5 red marbles randomly (and uniformly) on the interval [0,1]. What is the probability that the marbles will interleave each other?
- Given P(X = E(X)) = 1, does that mean Var(X) = 0?
- An interesting analysis of US high school graduation rates, conducted using R and googleVis.
- Do you have a unisex name? The following series of visuals tells us the most common unisex names in US history, and how the ratio of boys to girls changes over time.
- Most of us know what instrumental variables are (if not, here’s the Wikipedia page), but do you know what weak instruments are? The diffuseprior blog has a tutorial and tells you how to find them using R.
- This week, we found a number of useful webinars and presentations for statisticians and data scientists on R. Feel free to check out the following opportunities: Online course on forecasting using R by Prof. Hyndman of Monash University, Coursera’s free R courses, Why use R for Data Analysis by Vivek H. Patil of Gonzaga University, and two workshops on R by Bob Muenchen.
- If I roll five dice, what’s the chance that exactly two of the die show the same number?
- Did you know that even famous mathematicians like Paul Erdős had a hard time believing the result of the Monty Hall Problem? It was a computer simulation that eventually convinced him. Here’s a simulation of the Monty Hall Problem, and my own take on the how the problem is often poorly presented.
- During the 2013 JSM (Joint Statistics Meetings) Conference in Montreal, Revolution Analytics conducted a survey of attendees from August 5 to August 8. The 865 respondents gave their opinions on the privacy and ethics related to data collection, and on their familiarity with statistical software used for the analysis of such data. Out of the 865 statisticians surveyed…
- Larry Wasserman, Professor at Carnegie Mellon University, is a graduate of University of Toronto, a COPSS Award winner, and a leading statistician in Bayesian analysis and inference. In this post, he discusses his views on the question Is Bayesian Inference a Religion?
- Two people will each spend 15 consecutive minutes in a bar between 12:00pm – 1:00pm. Assuming uniform and independent arrival times, what is the probability that they will have a chance to clink glasses?
- Have you ever wondered which statistical package gives the fastest computational speeds? This quick comparison of Julia, Python, R and pqR provides some guidence.
- An interesting analysis of the most popular porn searches in the US.
- A quiz for everyone in the data visualization industry: Identify at least three problems with this chart and explain what you can do to make it better.
- R user groups continue to thrive worldwide. Joseph Rickert from Revolution Analytics prepares the following compilation of the locations of 127 R user groups around the world.
- Data science is emerging as a new, hot field, but is it really different from statistics? Wesley from statistical-research.com discusses why data science is more than just a title.
- Are you in the market research industry? If you ever run into incomplete data, here is how machine learning can help to fill in the gaps.
- This year, more than 6,000 people attended the Joint Statistical Meetings, the largest statistical meeting in the world. If you missed the 2013 JSM, this summary will bring you up to speed.
- Why an infinite number of monkeys (or even just one monkey!) will eventually crank out a complete play every bit as melodramatic as The Bard’s famous Hamlet.
- Egon Pearson (11 August 1895 – 12 June 1980) is one of the most prominent figures in the history of statistics. His most important contributions include the Neyman-Pearson (1933) theory of hypothesis testing, and promoting statistical methods in industry. However, most people fail to realize that Pearson’s contributions go well beyond hypothesis testing. Here are some early pioneering works of Pearson that have been neglected.