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Foundations and Applications of Statistics - An Introduction Using R (Hardcover, 2nd Revised edition)
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Foundations and Applications of Statistics - An Introduction Using R (Hardcover, 2nd Revised edition)
Series: Pure and Applied Undergraduate Texts
Expected to ship within 10 - 15 working days
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Foundations and Applications of Statistics simultaneously
emphasizes both the foundational and the computational aspects of
modern statistics. Engaging and accessible, this book is useful to
undergraduate students with a wide range of backgrounds and career
goals. The exposition immediately begins with statistics,
presenting concepts and results from probability along the way.
Hypothesis testing is introduced very early, and the motivation for
several probability distributions comes from p-value computations.
Pruim develops the students' practical statistical reasoning
through explicit examples and through numerical and graphical
summaries of data that allow intuitive inferences before
introducing the formal machinery. The topics have been selected to
reflect the current practice in statistics, where computation is an
indispensible tool. In this vein, the statistical computing
environment $\mathsf{R}$ is used throughout the text and is
integral to the exposition. Attention is paid to developing
students' mathematical and computational skills as well as their
statistical reasoning. Linear models, such as regression and ANOVA,
are treated with explicit reference to the underlying linear
algebra, which is motivated geometrically. Foundations and
Applications of Statistics discusses both the mathematical theory
underlying statistics and practical applications that make it a
powerful tool across disciplines. The book contains ample material
for a two-semester course in undergraduate probability and
statistics. A one-semester course based on the book will cover
hypothesis testing and confidence intervals for the most common
situations. In the second edition, the $\mathsf{R}$ code has been
updated throughout to take advantage of new $\mathsf{R}$ packages
and to illustrate better coding style. New sections have been added
covering bootstrap methods, multinomial and multivariate normal
distributions, the delta method, numerical methods for Bayesian
inference, and nonlinear least squares. Also, the use of matrix
algebra has been expanded, but remains optional, providing
instructors with more options regarding the amount of linear
algebra required.
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