Posted by Anonymous on .
We have created a set of data values that sample a function y(x). The sample points are stored in two arrays: xVals and yVals. These represent measurements of a physical process that is subject to noise so that if a is the i'th entry of xVals (i.e. a = xVals[i]) then yVals[i] is an approximation of y(a).
Write a procedure called findOrder that finds the lowest order polynomial model that fits the data to an accuracy of 1.0e1, as measured by the residual error. findOrder should return the array of coefficients provided by pylab.polyfit. Recall that pylab.polyfit takes as arguments an array of x values, an array of y values, a degree of polynomial fit, and an optional argument full, which, if True, will cause pylab.polyfit to return:
an array of coefficients
the residual of the fit
Three additional parameters that should not concern you.
You may assume that the modules pylab and numpy are already imported into the environment. You may use anything you wish from the numpy module, but only pylab.polyfit and pylab.array are available from the pylab module.
def findOrder(xVals, yVals, accuracy = 1.0e1):
# Your Code Here

Python Programming 
Anonymous,
Please obey honor code. This is MIT 6.00x final exam question.

Python Programming 
Anonymous,
i don't want the answer. just help me visualize the problem and how to go for the solution. i am in class 9th and i don't know anything about integrals, yet. coding is not the problem. i am able to do that

Python Programming 
Anonymous,
i don't know if u knew just tell me man ...
ty ... 
Python Programming 
badr,
you should not do it!!

Python Programming 
badr,
you should not do it!!
it the EDx final exam!!