This book gives a description of the group of statistical
distributions that have ample application to studies in statistics
and probability. Understanding statistical distributions is
fundamental for researchers in almost all disciplines. The informed
researcher will select the statistical distribution that best fits
the data in the study at hand. Some of the distributions are well
known to the general researcher and are in use in a wide variety of
ways. Other useful distributions are less understood and are not in
common use. The book describes when and how to apply each of the
distributions in research studies, with a goal to identify the
distribution that best applies to the study. The distributions are
for continuous, discrete, and bivariate random variables. In most
studies, the parameter values are not known a priori, and sample
data is needed to estimate parameter values. In other scenarios, no
sample data is available, and the researcher seeks some insight
that allows the estimate of the parameter values to be gained. This
handbook of statistical distributions provides a working knowledge
of applying common and uncommon statistical distributions in
research studies. These nineteen distributions are: continuous
uniform, exponential, Erlang, gamma, beta, Weibull, normal,
lognormal, left-truncated normal, right-truncated normal,
triangular, discrete uniform, binomial, geometric, Pascal, Poisson,
hyper-geometric, bivariate normal, and bivariate lognormal. Some
are from continuous data and others are from discrete and bivariate
data. This group of statistical distributions has ample application
to studies in statistics and probability and practical use in real
situations. Additionally, this book explains computing the
cumulative probability of each distribution and estimating the
parameter values either with sample data or without sample data.
Examples are provided throughout to guide the reader. Accuracy in
choosing and applying statistical distributions is particularly
imperative for anyone who does statistical and probability
analysis, including management scientists, market researchers,
engineers, mathematicians, physicists, chemists, economists, social
science researchers, and students in many disciplines.
General
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