The purpose of HW3 is to give you practice with the statistical methods we have reviewed over the last 3 lectures: calculating descriptive statistics, distributions, hypothesis testing and confidence intervals.
A secondary goal is to give you further practice with data visualization
- README.me: this file
- HW3-SKEL.ipynb
- energyuse.csv: student electricity use data, described below
- GerberdingElectricityChilledWater.csv
More details are in the Jupyter notebook for HW3. Follow through the notebook and complete all your work there. When you are done, check back in your work. I will check out your completed work and grade it with a GitHub issue as before.
Note that I must be able to execute all cells in the notebook starting from top to bottom. I will "clear all output" from your notebook and then execute it all in order to make sure the notebook works.
The file energyuse.csv contains energy usage data for 5 chemical engineering undergraduate students. The final line of the file contains the national average of the data. There are three types of data given: lighting, electricity and total (just the same).
Instructions are given in the skeleton notebook.
Comments in python (using # in a code cell - or just adding a markdown cell) is critical so I can follow your work. You can't really over-comment things.
My expectation is that everything you type in a Python notebook is your own work. Any instances of "copy-paste" from the web or another person's notebook should be clearly cited. Of course you may look at examples but it is my strong preference that you refrain from copy-paste and type everything in. There is a learning reason for this, which I am happy to discuss in office hours or on slack.
I will follow UW academic misconduct policy for any suspected instances of cheating on HW or projects. Any confirmed instance of cheating results in a zero on a HW assignment. Any 2nd confirmed instance of cheating results in a zero for the entire course grade.