The COVID-19 pandemic has certainly affected all of us over the past year; however, not all regions of the country have been impacted equally. The purpose of our project is to determine how well the impact of COVID-19 across the United States has been predicted by “social vulnerability,” which is defined by the CDC as […]
B27-P: Building Dynamic Time Warping in R and Applying It to Time Series Data
Dynamic time warping is an algorithm for comparing two different time series by minimizing the distance between time series. This project explores how dynamic time warping can be applied to the number of Covid-19 cases in 2021 in Ohio and Illinois. The functions associated with dynamic time warping were created and tested in R using […]
A26: Predicting Model of the Commercial House Market in Beijing
China, as the world second largest economy, is one of the hotspots in both Economics field and Statistics field, so our research’s target country is China. In the Economics field we conclude that there are three gharries pulling one country’s economy, they are Investment, Consumption and Export. This theory can also be applied into Chinese […]
B52: An Analysis of Climate Change Impact on Society
Americans have very different opinions about whether climate change exists, whether it is man-made, and what actions should be taken to stop it. The purpose of my project is to determine if a relationship exists between a population’s opinion on climate change and the climate they experience. I have calculated several metrics that quantify different […]
B53: Expanded Research on Estimating the Total Economic Cost of Overweight and Obese Individuals—State-Level Analysis
This project builds upon the 2009 Society of Actuaries study on the estimated economic cost of overweight and obese individuals in the entire US and Canada. We found over a $235B increase in total US cost from 2009 to 2017 with the highest total cost coming from California, New York, Florida and the lowest coming […]
B54: Fitting Mixture Models to Right-Censored Data
Modeling Right-Censored Danish Fire Loss Data A dataset is right-censored at a censoring point if, whenever observations are greater than the censoring point, they are recorded as being equal to the censoring point; otherwise, they are recorded as their true values. Such data occur in insurance losses when a policy contains a maximum benefit. This […]
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