unbiasedN = Ford.length - 1
FordVa = FordSS/unbiasedN - Ford.length/(Ford.length - 1)*Math.pow(Fordavg,2)
r = covariance(Merck,Ford)/(Mercksd*Fordsd)
coeffofDetermination = Math.pow(r,2)
FordVar = variance(Ford);
Fordsd= Math.sqrt(FordVar)
MerckVar = variance(Merck)
Mercksd = Math.sqrt(MerckVar)
//Merck = y in this case, and Ford = x
//So the regression coefficient b = cov (x,y)/ var(x) or //cov(MRK,F)/var(Ford)
b = covariance(Merck,Ford)/FordVar
md` Now we calculate a, using b, and the two averages`
a = Merckavg - b * Fordavg
md`
Next we want to create an array of y-hat values that use x values as input and the equation of y-hat = b x(i) + a`
y_hat = data => data.map((d,i,A)=>(Math.round((a + b * d),3)));
//percentChange = data => data.map((d,i,A)=>(Math.round(100*(d-A[0])/A[0])))
//change1 = percentChange(Ford)
Ypredicted = y_hat(Ford)
function differencesq(arr1, arr2){
let arr3 = new Array(arr1.length).fill(0);
let n = arr1.length
for (let i = 0; i < n; i++) {
arr3 [i] = Math.pow((arr1[i] - arr2[i]),2);
}
return arr3
}
yMinusYhatsq = differencesq(Merck,Ypredicted)
yavgvector = new Array(Merck.length).fill(Merckavg);
yMinusYavgsq = differencesq(Merck, yavgvector)
asum(yMinusYavgsq)
asum(yMinusYhatsq)
rsq_regr= 1 - ((asum(yMinusYhatsq)/asum(yMinusYavgsq)))
r_regr = (Math.sqrt(rsq_regr))
Predicted = y_hat(Ford)
{
let trace1 = {
type: "scatter",
mode: "markers+text",
name: 'Merck',
x: Ford,
y: Merck,
line: {color: '#17BECF'},
text: date0.map(d=>d3.timeFormat("%b %Y")(d)),
textfont : {
family:'Times New Roman'
},
textposition: 'bottom center',
marker: { size: 8 }
}
let trace2 = {
type: "scatter",
mode: "line",
name: "Regression Line",
x: Ford,
y: Predicted,
line: {color: '#7F7F7F'},
}
var data = [trace1,trace2];
var layout = {
title: 'Merck vs. Ford Quarterly Stock Price <br>Coeff of Determination = 0.638 vs r-sq = 0.640<br> Regression parameters: a= 116.2575, b = -4.3997 Merck = a + b(Ford)' ,
xaxis: {
title: "Ford"
},
yaxis: {
title: "Merck"
}
};
const div = DOM.element('div');
Plotly.newPlot(div, data, layout);
return div;
}
//percentChange = data => data.map((d,i,A)=>(Math.round(100*(d.close-A[0].close)/A[0].close)))
percentChange = data => data.map((d,i,A)=>(Math.round(100*(d-A[0])/A[0])))
change = percentChange(Merck)
change1 = percentChange(Ford)
//nMonths = 60;
range = '3mo'
data0=fetchYahooData('MRK',range, '1/1/'+years[0], '12/31/'+(years[0]+4))
data1=fetchYahooData('F',range, '1/1/'+years[1], '12/31/'+(years[1]+4))
years = [2015, 2015, 2015, 2015, 2015, 2015]
Plotly = require("https://cdn.plot.ly/plotly-latest.min.js")
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
