2- Use least squares regression to fit polynomials of order 1, 3 and 5 to the data given in table. Compute the correlation coefficient for each fit (Use Gauss elimination with partial pivoting in the solution of linear equation systems). Plot 1st, 3d, and 5th order polynomial fits and the given discrete data. Comment on which of these curves explain better the variability of given data. -0.08782 0.078597 0.084523 0.308436 0.263619 0.62902 0.293331 0.90405 0.472033 0.880547 0.529981 1.072706 0.603711 1.081378 0.783421 0.849039 0.885681 0.669272 0.914265 0.449664 1.006573 0.191648 1.070965 -0.20313 1.232576 -0.42675 1.359112 -0.77451 1.327033 -0.85954 1.477735 -0.99787 1.647759 -1.06124 1.75348 -0.88792 1.882376 -0.73878 1.813837 -0.57943 1.92543 -0.2393 2- Use least squares regression to fit polynomials of order 1, 3 and 5 to the data given in table. Compute the correlation coefficient for each fit (Use Gauss elimination with partial pivoting in the solution of linear equation systems). Plot 1st, 3d, and 5th order polynomial fits and the given discrete data. Comment on which of these curves explain better the variability of given data. -0.08782 0.078597 0.084523 0.308436 0.263619 0.62902 0.293331 0.90405 0.472033 0.880547 0.529981 1.072706 0.603711 1.081378 0.783421 0.849039 0.885681 0.669272 0.914265 0.449664 1.006573 0.191648 1.070965 -0.20313 1.232576 -0.42675 1.359112 -0.77451 1.327033 -0.85954 1.477735 -0.99787 1.647759 -1.06124 1.75348 -0.88792 1.882376 -0.73878 1.813837 -0.57943 1.92543 -0.2393