When presented with a dataset, it is beneficial to identify any relationships or trends. One way in which we can accomplish this is through the application of cluster analysis, a method for developing taxonomies within a set of observations. While this technique is beneficial in marketing, research, or any profession requiring data analysis, there are many algorithms for dfining clusters in a dataset. As a result, we raise the question, which clustering algorithm is the best in various scenarios?

Markov Chain Monte Carlo (MCMC) methods are powerful algorithms that enable statisticians to explore information about probability distributions through computer simulations when exact theoretical methods are not feasible. The Gibbs sampler, for example, allows us to gather information about marginal and joint distributions of multivariate densities assuming that we know information about the conditional distributions. Of particular interest is the use of MCMC methods in Bayesian statistics to help estimate posterior distributions.

Baseball is the great American pastime. In this study we examine different aspects of baseball games to determine what factors play a role in predicting the winning team for a specific game or an entire season. To predict who is likely to win individual games, we consider factors such as each team’s offensive or defensive ability, past game scores, and previous winning percentage. In particular, we examine the extent to which a team playing at home has an advantage over the visiting team.