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2022-wi-stats21

adrenalinejunkies_islandsimulation

Using the simulation tool at https://islands.smp.uq.edu.au/login.php, we analyzed the effects of different treatments on the response, adrenaline levels. Using statistical techniques such as ANOVA and conditions of a well-designed experiment, we seek to find the relationship between different activities and adrenaline levels.

blueprintdatathon

calfresh

Analyzing the CalFresh system in California to predict enrollment rates across counties.

data-mining

My individual final project in UCLA's Statistics Intro to Data Mining 101C course. Analyzed a given dataset and used machine learning algorithms including logistic regression, random forests, boosting, and more, to predict the output from a test dataset we could not see publicly.

dsu-w2021-loss-weight-tracking

I was a participant on this Data Science Union project. We explored fitness data to predict weight loss.

imessagewordlestats

My friends send their Wordle stats in our iMessage GM mostly every day. I wanted to analyze that data, so I used pandas, sqlite3, and seaborn, among other Python tools to do this.

lebron-scoring-record

R Shiny Dashboard for LeBron's quest to surpass the scoring title. Project end date using Monte Carlo simulation.

lebron_scoring_record

Using Monte Carlo simulation to project when LeBron James will break the NBA scoring record. Visualized using R Shiny.

minimage

An image compression app built on Flask using Python. Containerized with Docker.

my_stats21_repo

rosita-sqlxr-databasemanagement

Is it possible to manage a supermarket – from employees to inventory to purchases – natively from R? With Rosita, it is. Rosita is an R program that utilizes MySQL for database management.

sleepscore

Analyzing sleep data from my Fitbit tracker using statistical methods such as multiple linear regression.

spikeballentertainment

What factors make sports entertaining to watch? More specifically, how can the sport of Roundnet (a 2v2 game comparable to volleyball where one hits on a net instead of over a net) grow its viewership? We created a survey to answer this question. Then, using statistical methods ranging from hypothesis testing to multiple linear regression, we used our findings to create a quantitative metric to predict how entertaining a new clip would be given certain factors.

tennis_matchchartingproject

Raw, user-submitted point-by-point data for pro tennis matches

ucla-datasquad.github.io

website for UCLA DataSquad ucla-datasquad.github.io

vitalstatistics

An R package for the analysis of CDC Vital Statistics birth records