If you’ve ever watched Major League Baseball, one of the feature points of the sport is the batting line-up that each team decides upon before each game. Traditional baseball logic tells us that speedy, reliable hitters like Trea Turner should…

Some of you might have been wondering what the heck I’ve been up to for the past few months. I haven’t been posting much on my blog lately, and I haven’t been working on important problems like solving Where’s Waldo?…

Have you ever found yourself searching for a statistics package in Python, but it just isn’t available? This is the biggest reason I’ve heard when my colleagues say they’re unwilling to make the switch from R to Python for statistical…

I finally got around to finishing up this tutorial on how to use pandas DataFrames and SciPy together to handle any and all of your statistical needs in Python. This is basically an amalgamation of my two previous blog posts…

Tagged with: analysis of variance, ANOVA, bootstrap, confidence interval, data management, ipython, Mann-Whitney-Wilcoxon, MWW, notebook, pandas, plotting data, python, RankSum, research, standard error, statistics, tutorial

Per a recommendation in my previous blog post, I decided to follow up and write a short how-to on how to use pandas to process data from multiple replicate runs in Python. If you do research like mine, you’ll often…

As promised, here’s the IPython Notebook tutorial I mentioned in my introduction to IPython Notebook. Downloading and installing IPython Notebook You can download IPython Notebook with the majority of the other packages you’ll need in the Anaconda Python distribution. From…

Tagged with: analysis of variance, ANOVA, bootstrap, confidence interval, ipython, Mann-Whitney-Wilcoxon, MWW, notebook, python, RankSum, research, standard error, statistics, tutorial

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