TwoVarRegression, C++ (gcc)

TwoVarRegression

//g++  7.4.0
//Two Variable Regression
//created by Rezaul Hoque on January 04,2021
//contact at jewelmrh@yahoo.com
#include <iostream>
#include <cmath>
using namespace std;
int main()
{
int n=10;
float sumX=0,sumY=0,sumXX=0, sumXY=0,sumYY=0,sumXYD=0,sumYD=0,sumXD2=0,sumYestY=0,sumestY2=0;
float y[n]={70,65,90,95,110,115,120,140,155,150};
float x[n]={80,100,120,140,160,180,200,220,240,260};
float meanX, meanY,sigma2=0,b1,b2,varb1,seb1,varb2,seb2;  
for(int i=0;i<n;i++)
{
sumX += x[i];
sumXX += x[i]*x[i];
sumY += y[i];
sumYY += y[i]*y[i];
sumXY += x[i]*y[i];

}
meanX = sumX/n;
meanY = sumY/n;

for(int i=0; i<n; i++)
{
sumXD2 += (x[i]-meanX)*(x[i]-meanX);
sumXYD += (x[i]-meanX)*(y[i]-meanY);
}
//finding the slope coefficient 
b2 = sumXYD/sumXD2;
//finding the intercept
b1=((sumXX*sumY)-(sumX*sumXY))/(n*sumXX-pow(sumX,2));
//finding the estimated Y
float *estY;
estY = new float[n];

for(int i=0;i<n;i++)
estY[i]=b1+b2*x[i];
//finding the residual 
float *resid;
resid = new float[n];

for(int i=0;i<n;i++)
resid[i]=y[i]-estY[i];

float theF,sumResid2=0;
float gof;
for (int i=0;i<n; i++)
{
sumYestY += (estY[i]-meanY)*(y[i]-meanY);
sumestY2 += (estY[i]-meanY)*(estY[i]-meanY);
sumResid2 += resid[i]* resid[i];
sumYD += (y[i]-meanY)*(y[i]-meanY);
} 
//finding goodness of fit
gof = pow(sumYestY,2)/(sumYD*sumestY2);
//finding the F
theF = (b2*b2*sumXD2)/(sumResid2/(n-2));
//
sigma2=(sumResid2/(n-2));
varb2 = sigma2/sumXD2;
seb2 = sqrt(sigma2)/sqrt(sumXD2);
varb1 = (sumXX*sigma2)/(n*sumXD2);
seb1 = sqrt((sumXX*sigma2)/(n*sumXD2));

cout<<"The estimated regression line is:\n";
cout<<"Y = "<<b1<<" + "<<b2<<"X";
cout <<endl;
cout<<"varb1:\t"<<varb1<<"\tseb1:\t"<<seb1;
cout<<endl;
cout<<"varb2:\t"<<varb2<<"\tseb2:\t"<<seb2;
cout <<endl;

cout<<"Goodness of fit :\t"<<gof;
return 0;
}


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