In this project, I analyzed a time series of a collection of home sales prices from 2007 to 2019. I used classical and STL decomposition to observe the patterns and trends in this time series, and forecasting. I also looked at what would be the ideal sample size of observations for a training data set made from this time series.

In this project, I created standard Poisson regression models on frequency counts and rates of the cyclists on the Williamsburg Bridge, along with a Quassi-Poisson regression model to look at the dispersion.

A Poisson regression model project of the counts and rates of cyclists on the Williamsburg Bridge.

In this project, we will create several candidate models for the purpose of using a multiple logistic regression model to predict the odds of an individual being at risk for developing coronary heart disease (CHD) over a 10-year period. We will use cross-validation to determine which candidate model has the greatest predictive power. We will also use ROC analysis the determine which candidate model has the greatest global goodness.

This project utilizes multiple logistic regression to build a model which can be used to predict a patient's odds of being at risk for developing coronary heart disease (CHD) based upon various medical and personal risk factors.

In this project, I created and analyzed a simple logistic regression model to predict the odds of a patient being at risk for developing coronary heart disease (CHD) over a 10-year period of time based on their diastolic blood pressure level.

Analyzing the factors which affect the area of land affected by forest fires through the use of multiple regression models and bootstrap confidence intervals.

A statistical analysis of the various factors affecting the area burned by a forest fire. This project tests several multiple regression models for this data to see which one provides the best utility for the prediction and estimation of the area affected by a forest fire.