This is the follow up study to analysis 1. It involves development of a regression model; and, also address development of binary decision rules and a Receiver Operating Characteristic (ROC) curve.

Data analysis 1 is an exploratory data analysis to determine plausible reasons why the original study was unsuccessful in predicting abalone age based on physical characteristics. Abalones are an economic and recreational resource that is threatened by a variety of factors which include: pollution, disease, loss of habitat, predation, commercial harvesting, sport fishing and illegal harvesting. Environmental variation and the availability of nutrients affect the growth and maturation rate of abalones. Over the last 20+ years it is estimated the commercial catch of abalone worldwide has declined in the neighborhood of 40%. Abalones are easily over harvested because of slow growth rates and variable reproductive success. Being able to quickly determine the age composition of a regional abalone population would be an important capability. The information so derived could be used to manage harvesting requirements. The assignment data are derived from an observational study of abalones. The intent of the investigators was to predict the age of abalone from physical measurements thus avoiding the necessity of counting growth rings for aging. Ideally, a growth ring is produced each year of age. Currently, age is determined by drilling the shell and counting the number of shell rings using a microscope. This is a difficult and time consuming process. Ring clarity can be an issue. At the completion of the breeding season sexing abalone can be difficult. Similar difficulties are experienced when trying to determine the sex of immature abalone. The study was not successful. The investigators concluded additional information would be required such as weather patterns and location which affect food availability.