The field of statistical research in weather allows for the application of old and new methods, some of which may describe relationships between certain variables better such as temperatures and pressure. The objective of this study was to apply a variety of traditional and novel statistical methods to analyze data from the National Data Buoy Center, which records among other variables barometric pressure, atmospheric temperature, water temperature and dew point temperature. The analysis included attempts to better describe and model the data as well as to make estimations for certain variables. The following statistical methods were utilized: linear regression, non-response analysis, residual analysis, descriptive statistics, parametric analysis, Kolmogorov-Smirnov test, autocorrelation, normal approximation for the binomial, and chi-squared test of independence. Of the more significant results, one was establishing the Johnson SB as the best fitting parametric distribution for a group of pressures and another was finding that there was high autocorrelation in atmospheric temperature and pressure for small lags. This topic remains conducive to future research, and such endeavors may strengthen the field of applied statistics and improve our understanding of various weather entities.
| Identifer | oai:union.ndltd.org:USF/oai:scholarcommons.usf.edu:etd-5822 |
| Date | 01 January 2013 |
| Creators | Allison, Malena Kathleen |
| Publisher | Scholar Commons |
| Source Sets | University of South Flordia |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Theses and Dissertations |
| Rights | default |
Page generated in 0.0018 seconds