The goal was to determine if a policy intervention was effective at reducing CLABSI rates. The dataset contains information about two sets of hospitals, one that participated in a policy intervention designed to improve patient safety (Treatment = TRUE), and one that did not. The goal was to reduce central-line associated blood stream infections (CLABSIs), with lower rates being more desirable. The policy was implemented at the beginning of 2016. I performed a T-test comparing hospitals that received the policy to those that did not on their 2015 to 2016 differences. I also performed a regression. The results showed that the policy was not effective.

This is a kernel I developed on kaggle for the world development indicators relating to youth. I format the data in three ways and explore the missing data.

Effectively predicting students’ high school math scores can help predict students’ future success. The objective is find the statistical model that best predicts student’s final grades. We tried OLS, LASSO, Random Forest, Boosting, PCR, and GAM.

Previous works have cited the significance of race, gender and education in understanding youth voter turnout. In this paper, we continue to understand the impact of these demographic attributes by considering the relative breakdown of the youth population, and by examining predicted probabilities of voting through these variables. Predicted probabilities for voting are computed by first imputing missing values through the EMB algorithm, and then utilizing a logistic regression. The findings suggest that higher education is the most significant factor in youth who vote, and women on average have higher levels of education. Because youth identifying as white substantially outnumber youth identifying as black, we conclude that white women are the most influential youth voting block.

No single metric exists for evaluating development which encapsulates both human and industrial development. This paper proposes a more comprehensive approach of classifying development by using GDP, life expectancy, urban population percentage and carbon dioxide emissions as metrics for evaluation. Countries were classified into levels of development through a k-means clustering algorithm. We formed four distinct groups where in general, the higher the multivariate average of a country’s attributes, the more developed a country was considered. Using the same cluster centers, but for U.S. state data, we match states into their corresponding development clusters. All but four states were classified in the highest development group. The results of our country classification produced similar development groups to those formed using other commonly used development indexes. Our classification mechanism elevated countries with high emissions to higher development groups as compared to previously defined country development groups. We argue that environmental impact is an important component that should be considered in the creation of any future comprehensive development metric.