Melva R. Grant

The Bivariate Erlang and its Application in Modeling Recurrence Times of Kidney Dialysis Data

Recent advances in computer modeling allows us to find closer fits to data. Our emphasis is on the interdependence between occurrence at kidney dialysis. The interdependence between kidney dialysis occurrences is modelled by a bivariate exponential that we propose in this article. The application is shown on the McGilchrist and Aisbett kidney data set with the use of the exponential distribution. The proposed bivariate exponential model has exponential marginal densities, correlated via a latent random variables and with finite probability of simultaneous occurrence. Extension of the model to a bivariate Erlang type distribution with same shape parameter is presented.

Investigating Estimation: Influences of Time and Confidence of Urban Middle School Students

This study sought to determine the estimation processes used by 10 urban middle school students for solving computational estimation problems, and if there was a difference in the estimation processes utilized for straight computation and application problems. An adapted model of the Accessing Computational Estimation Test (ACE) was used to determine the estimation strategies employed by the subjects within timed and un-timed settings. Qualitative methods were used to analyze the data. The timed ACE tests were administered using an interview format and included computation and application problems. The findings indicated that there were no differences in estimation processes for straight computation and application problems; however, students performed better on timed tests for application problems.