Let other people play with your logistic regression model.
Predict Getting into Harvard Law
Download/clone the repo.
Open the public/index.html file in a text editor.
Type your model into the config var at the top of the HTML file and fill out a few settings.
Open the HTML file in a browser.
/**************BEGIN EDITING HERE************/
var config = {
headerTitle: "Harvard Law Admit Calcuator",//title at top of page
resultsTitle: "Chance of Getting into Harvard Law",//explains results
likelihoodLabel: "Likelihood of Admission",//shows along y-axis of graph
intercept: -97.13444,//the intercept coefficient from your model
variables: [
{
name: "LSAT",
coefficient: 0.3172556,//your beta coefficient from the logistic regression model
binary: false,//is this a YES/NO 1/0 variable?
min: 120,//minimum that user can select for this variable (remove if binary)
max: 180,//maximum that user can select for this variable (remove if binary)
defaultValue: 170,//value that's already selected when the page loads (use true/false for binary)
selectIncrement: 1,//how much to increment between min and max for user select (remove if binary)
decimalPlaces: 0,//how many decimal places should be displayed (remove if binary)
graphIncrement: 1,//determines the "ticks" on the graph's x-axis (remove if binary)
graphAxisMin: 150,//bottom range for graph x-axis (remove if binary)
graphAxisMax: 180//top range for graph x-axis (remove if binary)
},
{
name: "GPA",
coefficient: 11.05645,
binary: false,
min: 2.50,
max: 4.00,
defaultValue: 3.75,
selectIncrement: 0.01,
decimalPlaces: 2,
graphIncrement: 0.10,
graphAxisMin: 2.5,
graphAxisMax: 4.0
}
]
};
/*************STOP EDITING HERE**************/
{
name: "Male",
coefficient: 0.098,
binary: true,
defaultValue: false,
},