Linear regression using Least Squares

These are my programming notes. I doubt you’ll find anything useful.

Formula from: https://www.mathsisfun.com/data/least-squares-regression.html

/*
*Name:  Least Squares Line (Finding gradient for a set of points) in Y = mX + c
*Role:  Find line of best fit using Least Squares Method
*Input:  
        int xValues[] - Array containing all X coordinates
        int yValues[] - Array containing all Y coordinates 
        numberOfElements - number of elements in the array
*Sample call: 
        int xCoordinates[10] = { 1,2,3,4,5,6,7,8,9,10};
        int yCoordinates[10] = { 1,1,2,3,4,5,6,6,6,7};
        LeastSquaresLine(xCoordinates, yCoordinates, 10);
*Sample output: Y = 0.721212 x + 0.393939
*/
void LeastSquaresLine(int xValues[], int yValues[], int numberOfElements)
{
 //Initialize variables 
 int sumOfX = 0;
 int sumOfY = 0;
 int sumOfXYProducts = 0;
 int sumOfSquareX = 0;
 
 //Loop for summation
 for(int i = 0; i < numberOfElements; i++)
 {
  sumOfX += xValues[i];
  sumOfY += yValues[i];
  sumOfXYProducts += xValues[i] * yValues[i];
  sumOfSquareX += xValues[i] * xValues[i];
 }
 
 //Find the value for m, the gradient
 double mCoefficient = (double) (numberOfElements * sumOfXYProducts - sumOfX * sumOfY) / (numberOfElements * sumOfSquareX - sumOfX * sumOfX);
 
 //Finding value for c in Y = mX + c
 int averageXValues = sumOfX / numberOfElements;
 int averageYValues = sumOfY / numberOfElements;
 double cCoefficient = averageYValues - mCoefficient * averageXValues;
 
 //Print result
 printf("Y = %f x + %f", mCoefficient, cCoefficient); 
}