We were discussing  Bootstrap method, confidence intervals and accuracy of model parameters especially on linearization of non-linear models
The goodness of fit and the residuals plot are useful indicators along with the error function. Each gives helpful information
The correlation will mean that the fit is good but to improve its accuracy, it must also result in a specific distribution of residuals. The residuals should be distributed along the independent axis and normally distributed around zero with no systematic trends. The latter condition makes the difference between the data point and the estimate - the residual, more acceptable.
I want to mention that curve fitting improves with higher degrees such as a quadratic over linear but this does not mean we go to as high a degree as possible. The model we build tried to minimize the residuals. If we can do this with lesser degrees, then  that is acceptable.
#codingexercise
Print Hosoya's triangle
Hosoya's triangle is a triangular arrangement of numbers based on Fibonacci numbers where the sum of the two numbers in the left diagonal or the right diagonal results in the number at the current position.
The recurrence relation is
H(0,0) = H(1,0) =  H(1,1) = H(2,1) = 1
and
H(n,j) = H(n-1,j) + H(n-2, j)
           = H(n-1, j-1) + H(n-2,j-2)
And H(n,i) = F(i+1) x F ( n - i + 1)

int GetHosoya(int r, int c)
{
    if (r == 0 && c == 0) return 1;
    if (r == 1 && c == 0) return 1;
    if (r == 1 && c == 1) return 1;
    if (r == 2 && c == 1) return 1;
    if (r > c)
        return GetHosoya(r - 1, c) + GetHosoya(r - 2, c);
    if (r == c)
        return GetHosoya(r - 1, c - 1) + GetHosoya(r - 2, c - 2);
    return 0;
}