solve with python

a.Use your pseudorandom number generator from question 4 to generate a list of 1000 random floating point numbers between 0 and 1 (duplicates are okay). Consider these values to represent probability levels that can be found by integrating the Gaussian probability density function (PDF) between x=-5 to x=xisuch that pi=P(x

.b.Using your Simpson function (nPoints=50) to integrate the Gaussian PDF combined with your Secant method, find the set of x values compatible with your list of 1000 probabilities. c.Finally, calculate the estimates of the population parameters and 2using the unbiased sample estimators from you set of x values. Compare your values to those from N(175,15).

Output the values for your population estimators like:

Population meanestimate= y.yy

Population variance estimate = z.zz

Note: you may need to use a clamp function to confine your probabilities to fall within the lower limit associated with the lower limit for integration.(i.e., makesure piP(x-5|N(,))=Simpson(GPDF, (, ), -10*, -5*))