Functions and Datasets to support statistics book(s) by Julian Faraway

I am the author of

Linear Models with R

and

Extending the Linear Model with R

A new book entitled Linear Models with Python will be published soon. This package contains data (and documentation) along with a few functions to support this book.

Data

All the datasets from Linear Models with R are included. These are all small datasets useful for teaching or other example purposes.

You can load a dataset (for example) with

import faraway.datasets.composite
composite = faraway.datasets.composite.load()
composite

and you can see the description with

print(faraway.datasets.composite.DESCR)

The datasets included are

            clot        fat         infmort     peanut      resceram    swiss
aatemp      coagulation fortune     jsp         penicillin  salmonella  teengamb
abrasion    composite   fpe         kanga       pima        sat         truck
air         cornnit     fruitfly    leafburn    pipeline    savings     tvdoctor
alfalfa     corrosion   galamiss    manilius    pressure    seatpos     uswages
anaesthetic crawl       galapagos   mba         prostate    sexab       wafer
butterfat   denim       gammaray    meatspec    pulp        snail       warpbreaks
cars        divusa      generic     newhamp     punting     sono        whiteside
cheddar     eco         globwarm    oatvar      pvc         speedo
chmiss      eggprod     hips        odor        rabbit      star
chredlin    families    hsb         ozone       rats        statedata

This is a subset of those available in my R package on CRAN where you can also see documentation on these datasets.

Functions

Currently, there is only one useful function which is an alternative to the regression summary function used in statsmodels. It is much more compact. Suppose lmod is an OLSresults object, then (note the spelling is sumary())

lmod.sumary()

would produce something like

           coefs stderr tvalues pvalues
Intercept  7.068 19.154    0.37  0.7154
Area      -0.024  0.022   -1.07  0.2963
Elevation  0.319  0.054    5.95  0.0000
Nearest    0.009  1.054    0.01  0.9932
Scruz     -0.241  0.215   -1.12  0.2752
Adjacent  -0.075  0.018   -4.23  0.0003

n=30 p=6 Residual SD=60.975 R-squared=0.77