You may NOT use any Python packages other than random, math and copy Use your pseudorandom number generator to generate a list of 1000 uniformly-distributed, random, floating-point numbers between 0 and 1 (duplicates are okay). Consider these values to represent probability levels pi that can be found by integrating the Gaussian probability density function (PDF) between x=μ-5⋅σ to x=xi such that pi=P(x<xi|N(μ,σ)) where μ=175, σ=15. Using your Simpson function (nPoints=50) to integrate the Gaussian PDF combined with your Secant method for root finding, find the set of x values compatible with your list of 1000 probabilities. Note: you will need to use callback functions for this process. Finally, calculate the estimates of the population parameters μ and σ2 using the unbiased sample estimators from your set of x values. Compare your values to those from N(175, 15). Output the values for your population estimators like:Population mean estimate = y.yyPopulation 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., make sure pi≥P(x≤μ-5⋅σ|N(μ,σ))=Simpson(GPDF, (μ, σ), μ-10*σ, μ-5*σ))

Python which refers to the one it is a popular, general-purpose programming language. It is an…

Answered: You may NOT use any Python packages…

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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You may NOT use any Python packages other than random, math and copy Use your pseudorandom number generator to generate a list of 1000 uniformly-distributed, random, floating-point numbers between 0 and 1 (duplicates are okay). Consider these values to represent probability levels p_i that can be found by integrating the Gaussian probability density function (PDF) between

x=μ-5⋅σ to x=x_i such that p_i=P(x<x_i|N(μ,σ)) where μ=175, σ=15.

Using your Simpson function (nPoints=50) to integrate the Gaussian PDF combined with your Secant method for root finding, find the set of x values compatible with your list of 1000 probabilities. Note: you will need to use callback functions for this process.

Finally, calculate the estimates of the population parameters μ and σ² using the unbiased sample estimators from your set of x values. Compare your values to those from N(175, 15).

Output the values for your population estimators like:

Population mean estimate = 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., make sure p_i≥P(x≤μ-5⋅σ|N(μ,σ))=Simpson(GPDF, (μ, σ), μ-10*σ, μ-5*σ))

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