Analyzed a 1,610-row lifestyle dataset (diet, exercise, and habits) to understand patterns linked to obesity. Built logistic regression models around physical activity, fast-food intake, and vegetable consumption, highlighting key lifestyle factors. Notably identified reverse causality effects and validated insights against the raw data.

IE5342 DOE Assignment 2

Analyzed 10 seasons (2014–2024) of NBA schedule data to measure the impact of travel and game density on team performance. Built interactive R visualizations (ggplotly, geosphere) to highlight back-to-backs, 4-in-6 stretches, and excessive travel periods. Designed a regression model to estimate wins gained or lost due to schedule factors, identifying the most advantaged and disadvantaged teams. Delivered a professional brief comparing Oklahoma City’s 2024–25 draft schedule with Denver’s, outlining key stretches and recovery strategies.

The accompanying R code generates a visualization of the Expected Value and Median as functions of p, using ggplot2 to plot both curves on the same graph. The results show that as p approaches 1, both statistics converge to 1 (success almost always occurs immediately), while for small p, the Expected Value grows rapidly like 1/p while the Median increases more moderately, reflecting the skewness of the distribution. Together, the blog and code provide both a mathematical derivation and a graphical interpretation that clarify how the mean and median behave under different probabilities.

um exemplo atualizado a partir de https://rpubs.com/Selcuk/1088675

The sample size is n=1599. There are no missing data points. There are outliers for residual sugar and chlorides. There were some bottles where sugar was high for red wine compared to other wine types, and therefore the data is right skewed. This is also true for chlorides, because there are a few high values, but most are in the same range. The chloride levels for wine vary by region, and high levels can make the wine taste too salty. The median is a better measure for these variables than the mean. Having taken into consideration, I do not have problems with the data quality. I’ve created histograms and boxplots to summarize the data for each variable. The histograms show the distributions. The alcohol variable is almost symmetrical. The quality scores are left skewed, but not much. More wines scored above the mean than below.