Modern Mathematical Statistics with Applications, Second Edition strikes a balance between mathematical foundations and statistical practice. In keeping with the recommendation that every math student should study statistics and probability with an emphasis on data analysis, accomplished authors Jay Devore and Kenneth Berk make statistical concepts and methods clear and relevant through careful explanations and a broad range of applications involving real data.
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.
From the book reviews:
"The text by Devore and Berk is a worthy entry into the collection of texts designed for the typical undergraduate mathematical statistics course. ? there is more than enough material in the book for a one-year course. ? the book would be a very solid choice for a year-long mathematical statistics course." (R. T. Smythe, Mathematical Reviews, December, 2014)
"The authors made a good revision in this edition. The format is standard, however it showcases many statistical ideas in a clear manner, for a smooth grasp of the material. The chapters are well structured, presented, and motivated. The main strength of the book is that it still offers a good number of worked examples that are based on real datasets emerging from a variety of fields. It is a good candidate for adoption as a textbook for a senior level undergraduate course." (Ahmed, Technometrics, November, 2014)
