Analysis of STHQ's success in attracting different companies to come to Indianapolis.

A deeper dive into wine data for final project. Separates red from white wine.

Supplemental for a group project. Shows plots for how different predictors relate to wine quality.

I have alternative problems, including when there are multiple doors and multiple prizes. Although the ideas might not be terribly complex, it was fun to code the simulated problems.

Completed with Arsh Singh and Aiden Tirney

Additional findings of the NCAA data that exceed the scope of my final project

Select an interesting binary column of data, or one which can be reasonably converted into a binary variable This should be something worth modeling Build a logistic regression model for this variable, using between 1-4 explanatory variables Interpret the coefficients, and explain what they mean in your notebook Using the Standard Error for at least one coefficient, build a C.I. for that coefficient, and translate its meaning

Two null hypotheses using Neyman-Pearson framework and Fisher's Significance Testing framework.

For first meeting with TA

Describes the importance of looking at documentation when analyzing data, and looks for shortcomings in data documentation that can cause problems for my analysis.

Took 5 samples of ~50% of the population and scrutinized data.

Cleans my NCAA data for H510. Rows are combined for men and women, so I separate these. I am leaving some "combined" data untouched during this for convenience in my project, but these columns can be removed.

Uses NCAA dataset

Week 2 Data Dive - Summaries for INFO-H 501. Dataset used contains information from the NCAA during the years 2015-2019.