If youâ€™ve read our previous R Tip on using sigr with linear models, you might have noticed that the lm() summary object does in fact carry the R-squared and F statistics, both in the printed form: model_lm <- lm(formula = Petal.Length ~ Sepal.Length, data = iris) (smod_lm <- summary(model_lm)) ## […]

Estimated reading time: 4 minutes

R is designed to make working with statistical models fast, succinct, and reliable. For instance building a model is a one-liner: model <- lm(Petal.Length ~ Sepal.Length, data = iris) And producing a detailed diagnostic summary of the model is also a one-liner: summary(model) # Call: # lm(formula = Petal.Length ~ […]

Estimated reading time: 1 minute

Introduction Let’s take a quick look at a very important and common experimental problem: checking if the difference in success rates of two Binomial experiments is statistically significant. This can arise in A/B testing situations such as online advertising, sales, and manufacturing. We already share a free video course on […]

Estimated reading time: 12 minutes

Many data scientists (and even statisticians) often suffer under one of the following misapprehensions: They believe a technique doesn’t work in their current situation (when in fact it does), leading to useless precautions and missed opportunities. They believe a technique does work in their current situation (when in fact it […]

Estimated reading time: 4 minutes

The Win-Vector public R packages now all have new pkgdown documentation sites! (And, a thank-you to Hadley Wickham for developing the pkgdown tool.) Please check them out (hint: vtreat is our favorite).

Estimated reading time: 27 seconds

sigr is a simple R package that conveniently formats a few statistics and their significance tests. This allows the analyst to use the correct test no matter what modeling package or procedure they use.

Estimated reading time: 7 minutes