Write a function to perform a least squares fit of N data points to a straight line using the following steps ### Steps1. Create sample data set and visualize test data. An example using the...

Write a function to perform a least squares fit of N data points to a straight line using the following steps
### Steps1. Create sample data set and visualize test data. An example using the make_regression function in the sklearn library is provided below. For full credit you must write your function to generate a noise sample data set. Your function should take as input * The desired number of data points (x,y) * The slope $a$ and intersept $b$ of your data $y =ax+b$ * A parameter to choose a noise level for the y's.1. Implement your own least squares fit function using the derivation in the provided notebook.. Your function must return at the minimum the predicted parameters for slope and intersept and the value of $\chi^2$.1. Create a figure of your data and your fitted line.1. Perform a linear regression off the same data set using a linear regression function from a scientific library of your choice (scipy, numpy, sklearn). One choice is **numpy.linalg.lstsq** but it is certainly not the easiest to use. Use the online documentation (https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html) to understand how it works.1. Verify that your result and the library routine agree.