To help everyone understand the value of these tools, we’ll spend two class days working on “mini-projects”. Students will form groups of two or three to:
- obtain a (clean) dataset
- apply a tool in our curriculum:
Line of Best Fit
Regression (Statistical Significance)
- write a brief discussion which provides
an examination of whether a method’s assumptions are satisfied in this application
- discusses whether analysis outputs are trustworthy
(its very possible to do correct math and produce nonsense results: maybe the dataset is biased?)
offers a quick summary of results which is easily understood by non-technical readers
You needn’t have any prior Python programming experience to succesfully complete these tasks.
The mini-projects will be graded on the mathematical understanding of the method used, not their implementation. Additionally, students will have access to:
TA support in class, and all their Python expertise
example projects of each type to work from
It is expected that each student (of each group) gets a working Python and Jupyter Notebook installation up and running:
We hope that this experience helps students to draw the connection between the abstract “whiteboard math” and how these tools are really used.