$($b$)$ Using Python and the matrix method for linear least squares regression, solve and re$-$

port the model parameters for a and $b.$ Sanity check that your third unknown is close

to its expected value based on the fit values of a and $b.$

$($c$)$ Code a function in Python that computes an array of pressures given an array of molar

volumes and the model parameters a and $b.$ Use this function to plot the model predic$-$

tions against the experimental data for the pressure of methane.

$($d$)$ Finally, discuss if other direct solvers, e$.$g$.,$ Gaussian Elimination, and iterative solvers,

e$.$g$.,$ Gauss$-$Seidel, would be appropriate for this problem. Explain your reasoning with

evidence.