## Ebooks

Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

The main focus of the book is on presenting and illustrating methods of inferential statistics that are useful in research. It begins with a chapter on descriptive statistics that immediately exposes the reader to real data. The next six chapters develop the probability material that bridges the gap between descriptive and inferential statistics. Point estimation, inferences based on statistical intervals, and hypothesis testing are then introduced in the next three chapters. The remainder of the book explores the use of this methodology in a variety of more complex settings.

This edition includes a plethora of new exercises, a number of which are similar to what would be encountered on the actuarial exams that cover probability and statistics. Representative applications include investigating whether the average tip percentage in a particular restaurant exceeds the standard 15%, considering whether the flavor and aroma of Champagne are affected by bottle temperature or type of pour, modeling the relationship between college graduation rate and average SAT score, and assessing the likelihood of O-ring failure in space shuttle launches as related to launch temperature.

The textbook contains enough material for a year-long course, though many instructors will use it for a single term (one semester or one quarter). As such, three course syllabi with expanded course outlines are now available for download on the book’s page on the Springer website.

A one-term course would cover material in the core chapters (1-4), supplemented by selections from one or more of the remaining chapters on statistical inference (Ch. 5), Markov chains (Ch. 6), stochastic processes (Ch. 7), and signal processing (Ch. 8—available exclusively online and specifically designed for electrical and computer engineers, making the book suitable for a one-term class on random signals and noise).For a year-long course, core chapters (1-4) are accessible to those who have taken a year of univariate differential and integral calculus; matrix algebra, multivariate calculus, and engineering mathematics are needed for the latter, more advanced chapters.

At the heart of the textbook’s pedagogy are 1,100 applied exercises, ranging from straightforward to reasonably challenging, roughly 700 exercises in the first four “core” chapters alone—a self-contained textbook of problems introducing basic theoretical knowledge necessary for solving problems and illustrating how to solve the problems at hand – in R and MATLAB, including code so that students can create simulations.New to this edition

• Extended and revised instructions and solutions to problem sets

• Overhaul of Section 7.7 on continuous-time Markov chains

• Supplementary materials include three sample syllabi and updated solutions manuals for both instructors and students

Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.