I need help making a C program that uses Linear Least-Squares Fit to carry out the following:

Linear Least-Squares is an attempt to find a straight line that best represents a set of data points. It is a very common problem in modeling and statistics - you have a large set of observed data points, and you want to be able to describe it with a single line that "fits" the data as well as possible. You are given data consisting of N ordered pairs (x_i, V_j) with 1 lessthanorequalto i lessthanorequalto N. One measure of the "best fit" line to this dataset is the line that minimizes the sum of the square of the residues to the model y = mx + b. The residue at each point is given by: r_i (mx_i + b) - y_i The parameters of the model m and b are determined by minimizing the quantity: S^2 sigma^N_i=1 r_i The resulting model parameters m and b are given by the formulas: m = - / - ^2; b = - / - ^2 Where the angle-bracket expressions represent the mean (average) of the quantity enclosed: = 1/N sigma^n_i=1 f(x_i,y_i) Write a program that carries out the following: 1. Data Entry Step: reads N ordered pairs of data (x_iV_j) into a pair of arrays x and y. Query the user for a value for N in advance: N will not be greater than 100. 2. Analysis Step: using the obtained data and the formulas above, calculates the parameters m and b of the least-squares fit to the data and prints those values to the screen in an appropriate way. 3. Predictive Step: reads in a series of x values (number not known in advance, but no more than 100 values) until the user enters the sentinel value -100000. Then prints out a table of values for the least-fit line y=mx+b m two tab-separated columns labeled x and y