in python

Linear regression simulation. Consider the linear regression model yi=0+1xi+i,i=1,,n, where 0=5,1=3 and iN(0,1) and xiUniform(0,1). (a) Generate n=100 data points (xi,yi) from the above model. Plot the data. Fit a linear regression line model using python and add the fitted line to the plot. (b) Repeat the experiment in part (a) for 1000 times (without plotting). Note that you will get different estimates of 1. Denote them as ^1(1),^1(2),,^1(1000). What is the mean of these values ? Plot a histogram of ^1(1),^1(2),,^1(1000). (c) Repeat (b) but now with i being a standard Cauchy distribution. How does the histogram change ? Specifically, comment about the tails of histogram. (Note: Here, you are still using least-squares linear regression. Only the data generating process is changed.)