ThreeVariableRegression, C++ (gcc)

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//g++  7.4.0
//Three Variable Regression
//the code is created by Rezaul Hoque on January 07,2021
//Please contact at jewelmrh@yahoo.com
#include <iostream>
#include <cmath>
using namespace std;
int main()
{
int n=13;
float sumX2=0,sumX3=0,sumY=0,sumsqX2D=0, sumsqX3D=0,sumX2X3D=0,sumX2YD=0,sumX3YD=0,sumYD=0,sumYestY=0,sumestY2=0;
float y[n]={5.92,4.30,3.30,6.23,10.97,9.14,5.77,6.45,7.60,11.47,13.46,10.24,5.99};
float x2[n]={4.9,5.9,5.6,4.9,5.6,8.5,7.7,7.1,6.1,5.8,7.1,7.6,9.7};
float x3[n]={4.78,3.84,3.13,3.44,6.84,9.47,6.51,5.92,6.08,8.09,10.01,10.81,8.00};
float meanX2,meanX3, meanY,sigma2=0,b1,b2,b3,varb1,seb1,varb2,seb2,varb3, seb3;
for(int i=0;i<n;i++)
{

sumX2 += x2[i];
sumY += y[i];
sumX3 += x3[i];
}
meanX2 = sumX2/n;
meanX3 = sumX3/n;
meanY = sumY/n;

for(int i=0; i<n; i++)
{
sumsqX2D += (x2[i] - meanX2)*(x2[i] - meanX2);
sumsqX3D += (x3[i] - meanX3)*(x3[i] - meanX3);
sumX2X3D +=(x2[i] - meanX2)*(x3[i] - meanX3);
 sumX2YD += (x2[i] - meanX2)*(y[i]- meanY);
 sumX3YD += (x3[i] - meanX3)*(y[i]-meanY);

}
//finding the slope coefficient for X2
b2= ((sumX2YD * sumsqX3D) -( sumX3YD*sumX2X3D))/((sumsqX2D*sumsqX3D)-pow(sumX2X3D,2));
//Finding slope coefficient for X3
b3 = ((sumX3YD*sumsqX2D)-(sumX2YD*sumX2X3D))/((sumsqX2D*sumsqX3D)-pow(sumX2X3D,2));

//finding the intercept
b1= meanY - b2*meanX2 -b3*meanX3;
//finding the estimated Y
float *estY;
estY = new float[n];

for(int i=0;i<n;i++)
estY[i]=b1+b2*x2[i]+b3*x3[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 = ((b2*sumX2YD)+(b3*sumX3YD))/(sumYD);
//finding the F
theF =( (b2*sumX2YD)+(b3*sumX3YD))/2/(sumResid2/(n-3));
//finding the variance and standard error 
sigma2=(sumResid2/(n-3));
varb2 = (sumsqX3D*sigma2)/((sumsqX2D*sumsqX3D)-pow(sumX2X3D,2));
seb2 = sqrt(varb2);
varb1 = (1/n+((pow(meanX2,2)*sumsqX3D+pow(meanX3,2)*sumsqX2D-(2*meanX2*meanX3*sumX2X3D))/((sumsqX2D*sumsqX3D)-pow(sumX2X3D,2))))*sigma2;

seb1 = sqrt(varb1);

cout<<"The estimated regression line is:\n";
cout<<"Y = "<<b1<<" + "<<b2<<"X2 + "<<b3<<"X3";
cout <<endl;
cout<<"varb1:\t"<<varb1<<"\tseb1:\t"<<seb1;
cout<<endl;
cout<<"varb2:\t"<<varb2<<"\tseb2:\t"<<seb2;
cout <<endl;
cout<<"varb3:\t"<<varb3<<"\tseb3:\t"<<seb3;
cout<<endl;

cout<<"Goodness of fit :\t"<<gof;
cout<<endl;

cout<<" F :\t"<<theF;

return 0;
}

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Compilation time: 0.63 sec, absolute running time: 0.18 sec, cpu time: 0.02 sec, memory peak: 5 Mb, absolute service time: 0,82 sec