The objective of this application is to enable people to explore how least squares fit works in simple linear regression. This method minimizes the sum of the squared differences between each point and the estimation given by the line. While it is hard to mentally visualize squared distances, it is quite easy to compare squares. So this app lets you add points, fit linear regressions, manipulate the parameters of the linear regression, and explore the contribution of each point to the squared error via the associated square in the plot.

This project uses renv to encapsulate packages and versions used to run this application. You can clone this repository, call renv::restore() to install the packages in the renv.lock file, and then run

shiny::shinyAppDir("src")

from the root of this project.

Anoher option is to install dependencies manually and run

shiny::runGitHub("tomicapretto/playwithregression", subdir = "src")

The easiest way to start playing by clicking on the plot area. This will add a point in the position of the cursor. A linear regression fit together with a square that represents the contribution of the point to the sum of squares.

• Add random points adds 5 random points to the plot.
• Shake points shakes all the existing points in the plot, mostly for fun.
• Set Least Squares Fit lets you fit a regression line using Least Squares method. This can be used after manually tuning parameter estimates.
• Parameter estimates represent the intercept and slope in the equation of the line. They are updated as points are modified but you can manipulate them manually.
• Clear plot deletes all the points, squares, and line from the plot.

Finally, the sum of squares is shown below. Do you think you can get a smaller error than least squares method?! Well, have a try and play!

If you prefer to install dependencies manually, make sure you install the following libraries that are used directly by this app:

• here
• htmltools
• jsonlite
• katexR (from https://github.com/timelyportfolio/katexR)
• markdown
• r2d3
• R6
• renv
• shiny
• shiny.semantic
• shinyjs