A2Q2_input_b.txt file is below !!!!

               Guassian quadrature rule
  No of Gauss points                Error  
                                      (%)  
                  1            5.71e+01
                  2            3.21e+00
                  3            6.94e-02

need help with the hand calculations of the coefficients of the three standard curve fitting graphs  
 
 

The Gaussian quadrature rule is only able to yield a numerical/approximate value of an integral. To reduce its numerical error e, one can reduce the grid size h or increase the number of Gauss points n. The integral of function f(x) defined in Assignment 1 is also considered in this assignment. File contains values of h and e where n is set to a fixed value of 1 (i.e. using one Gauss point), while File contains values of n and e where h is set to a fixed value of 10 m (i.e. using a single domain). Your tasks are to find analytical functions representing the relationship between h (the independent variable) and e (the dependent variable) and between n (the independent variable) and (the dependent variable) by means of linear regression, and then compare the effects of h and n one. The following value, which is not to be shared with anyone else, is the error for which you are required to predict the corresponding h and n from the analytical models * = [8.9277 x 107]. Note that the Curve Fitting Toolbox is not allowed to be used in solving this question. Requirements For this assessment item, you must perform hand calculations: 1. Find the coefficients of the three standard curve-fitting functions: linear, exponential and power for the following set of two data points: the kth and (k+ 1)th rows of the numeric data part (i.e. the kth and (k+1)th data values) in File , where k is obtained by using Matlab function to round the following value to the nearest integer: [20+ 0.34902] (this value is not to be shared with anyone else). Report the answers rounded to three significant figures. The purpose of this requirement is to help you get used to regression analysis in this question.