Statistics
11 steps in 4 sets of R.
The reason R exists. Describing a distribution, then the hypothesis tests, then correlation and regression.
Eleven steps. R's statistical functions read close to the notation in a paper, which is either a delight or a shock depending on where you came from.
Describing
- Descriptive statisticsSummarizing a sample's center and spread.
- DistributionsR's r/d/p/q family for every common distribution.
- Sampling and bootstrappingDrawing samples, with and without replacement.
Hypothesis tests
- t-testsComparing two group means for a significant difference.
- Chi-squared testsTesting independence in a contingency table.
Correlation & regression
- CorrelationMeasuring how two variables move together.
- Linear regressionFitting a linear model with the formula interface.
- Logistic regressionModeling a binary outcome with glm.
- Prediction and model comparisonUsing a fitted model and testing it against a simpler one.
Encore
- regression_report.RFit a multiple regression and print a tidy coefficient table.
- ab_test.RSimulate an A/B test and report the effect with a t-test.