MC1-Homework-1

Overview

The purpose of this assignment is to get you started programming in Python. Note that we'll be using Python 2.7 for this course (not Python 3.0). This assignment is structured like many of the homeworks and projects for this course, namely that you are to create a Python function that solves a specific problem. We will call your function and verify that it provides the correct answer.

Task

Your task for this homework is to write two Python functions: One named stdev_p() that computes the population standard deviation of a list of numbers, and another named stdev_s() that computes the sample standard deviation. We will use the definitions of population and sample standard deviation provided at wikipedia [here]. Specifically the sample stdev should use the "Bessell Correction."

You have to write it yourself!

We want you to implement the functions described above using basic Python functions and control flow comments (e.g., while, for, etc.). You are not allowed to import any libraries.

Template

Here's a template for what your code should look like:

# calculate the population standard deviation
def stdev_p(data):
    result = 2.0 # your code goes here
    return result

# calculate the sample standard deviation
def stdev_s(data):
    result = 1.9 # your code goes here
    return result

if __name__ == "__main__":
    test = [2.0, 4.0, 4.0, 4.0, 5.0, 5.0, 7.0, 9.0]
    print "the population stdev is", stdev_p(test)
    print "the sample stdev is", stdev_s(test)

Cut and paste this template into a local file stdev_code.py. Note that the main part of the code tests the two functions. Note also that your main code will not be tested by the auto grading code. Only your functions will be called and tested.

You can run your program using the command: python stdev_code.py

Resources and ideas

• Python tutorials
• Python documentation

What to turn in

You can compose and test your code via the Udacity platform (continue here: MC1-Homework-1 - Instructions), however you must submit your final code via t-square.

• Submit your code as nth_prime.py via t-square. It is essential that you use exactly that name.

Udacity login instructions

If you are a GT OMSCS or on-campus student taking this course for credit, you must log in to Udacity using your Georgia Tech account: GT-Udacity Login

Here's a helpful video. Once you're in, visit the above link again, or navigate to the course from My Courses.

Note: DO NOT log in using your personal Udacity account, in case you have one.

Required, Allowed & Prohibited

Required:

• Your project must be coded in Python 2.7.x.
• Your code must run on one of the university-provided computers (e.g. buffet02.cc.gatech.edu), or on one of the provided virtual images.
• Your code must run in less than 5 seconds on one of the university-provided computers.

Allowed:

• You can develop your code on your personal machine, but it must also run successfully on one of the university provided machines or virtual images.
• Your code may use standard Python libraries.
• You may use the NumPy, SciPy and Pandas libraries.
• Small sections of code (up to 5 lines) that you collected from other students or the internet.
• Code provided by the instructor, or allowed by the instructor to be shared.

Prohibited:

• Any libraries not listed in the "allowed" section above.
• Any code you did not write yourself (except for the 5 line rule in the "allowed" section).
• Any Classes other than Random that create their own instance variables for later use (e.g., learners like kdtree).