id: 06643747
dt: j
an: 2016f.01304
au: Johnson, Roger W.; Kliche, Donna V.; Smith, Paul L.
ti: Modeling raindrop size.
so: J. Stat. Educ. 23, No. 1, 26 p., electronic only (2015).
py: 2015
pu: Taylor \& Francis, Abingdon, Oxfordshire; American Statistical Association
(ASA), Alexandria, VA
la: EN
cc: K90 K70 K40 M50
ut: stochastics; statistics; teaching; exploratory data analysis; data sets;
distribution; parameter estimation; maximum likelihood estimation;
disdrometer; beta density; exponential density; gamma density;
lognormal density; Weibull density; applied statistics; parametric
models; quality of fit; mathematical applications; meteorology
ci:
li: http://ww2.amstat.org/publications/jse/v23n1/johnson.pdf
ab: Summary: Being able to characterize the size of raindrops is useful in a
number of fields including meteorology, hydrology, agriculture and
telecommunications. Associated with this article are data sets
containing surface (i.e. ground-level) measurements of raindrop size
from two different instruments and two different geographical
locations. Students may begin to develop some sense of the character of
raindrop size distributions through some basic exploratory data
analysis of these data sets. Teachers of mathematical statistics
students will find an example useful for discussing the beta, gamma,
lognormal and Weibull probability density models, as well as fitting
these by maximum likelihood and assessing the quality of fit. R
software is provided by the authors to assist students in these
investigations.
rv: