This repository helps figuring out the best univariate linear model
Switch branches/tags
Nothing to show
Clone or download
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
.gitignore Updated gitignore Nov 14, 2017
README.md Permanent link to article added Dec 14, 2017
Screencast.gif Added screencast Nov 14, 2017
about.html Permanent link to article added Dec 14, 2017
data.csv New datasets Nov 14, 2017
global.r New datasets Nov 14, 2017
server.R New datasets Nov 14, 2017
ui.R Added about page Nov 16, 2017

README.md

ShinyApp to figure out the best univariate linear model

This repository contains the ShinyApp of my Medium article on "How to select the best performing linear regression for univariate models".

screencast

Live Version

How to use it

Use this app as a companion to my article on "How to select the best performing linear regression for univariate models". In addition, you can use it as a framework to evaluate your own dataset or models.

Features

Different performance indicator indicate how well your model performs. I personally use the following for univariate models:

Adjusted R2

The adjusted R2 indicate, how much variation is explained by your model. Instead of the simple R2, the adjusted R2 takes the number of input factors into consideration. It penalises too many input factors in order to favor parsimonious models.

Residuals

The residuals should be equally distributed around zero. Otherwise, the model has an upward or downward bias in certain areas. Use them also to examine whether your dataset exhibits heteroscedacity. Finally, the residuals indicate the bandwidth in which your model errors are.

Build With

Author