This book provides the theoretical
framework needed to build, analyze and interpret various statistical
models. It helps readers choose the correct model, distinguish among
various choices that best captures the data, or solve the problem at
hand. This is an introductory textbook on probability and
statistics. The authors explain theoretical concepts in a step-by-step
manner and provide practical examples. The introductory chapter in this
book presents the basic concepts. Next, the authors discuss the measures
of location, popular measures of spread, and measures of skewness and
kurtosis. Probability theory, discrete distributions, and important
continuous distributions that are often encountered in practical
applications are analyzed. Mathematical Expectation is covered, along
with Generating Functions and Functions of Random Variables. It
discusses joint distributions, and novel methods to find the mean
deviation of discrete and continuous statistical distributions. * Provides insight on coding complex algorithms using the 'loop unrolling technique' * Covers
illuminating discussions on Poisson limit theorem, central limit
theorem, mean deviation generating functions, CDF generating function
and extensive summary tables * Contains extensive exercises at the end of each chapter and examples from interdisciplinary fields. Statistics for Scientists and Engineers is
a great resource for students in engineering, physical sciences, and
management, and also practicing engineers who require skill sets to
model practical problems in a statistical setting.