Books about Faraway from Amazon.com

How one man's consuming passion for dogs saved a legendary breed from extinction and led him to a difficult, more soulful way of life in the wilds of Japan's remote snow country

As Dog Man opens, Martha Sherrill brings us to a world that Americans know very little about-the snow country of Japan during World War II. In a mountain village, we meet Morie Sawataishi, a fierce individualist who has chosen to break the law by keeping an Akita dog hidden in a shed on his property.

During the war, the magnificent and intensely loyal Japanese hunting dogs are donated to help the war effort, eaten, or used to make fur vests for the military. By the time of the Japanese surrender in 1945, there are only sixteen Akitas left in the country. The survival of the breed becomes Morie's passion and life, almost a spiritual calling.

Devoted to the dogs, Morie is forever changed. His life becomes radically unconventional-almost preposterous-in ultra-ambitious, conformist Japan. For the dogs, Morie passes up promotions, bigger houses, and prestigious engineering jobs in Tokyo. Instead, he raises a family with his young wife, Kitako-a sheltered urban sophisticate-in Japan's remote and forbidding snow country.

Their village is isolated, but interesting characters are always dropping by-dog buddies, in-laws from Tokyo, and a barefoot hunter who lives in the wild. Due in part to Morie's perseverance and passion, the Akita breed strengthens and becomes wildly popular, sometimes selling for millions of yen. Yet Morie won't sell his spectacular dogs. He only likes to give them away.

Morie and Kitako remain in the snow country today, living in the traditional Japanese cottage they designed together more than thirty years ago-with tatami mats, an overhanging roof, a deep bathtub, and no central heat. At ninety-four years old, Morie still raises and trains the Akita dogs that have come to symbolize his life.

In beautiful prose that is a joy to read, Martha Sherrill opens up the world of the Dog Man and his wife, providing a profound look at what it is to be an individualist in a culture that reveres conformity-and what it means to live life in one's own way, while expertly revealing Japan and Japanese culture as we've never seen it before..
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Authorised biography of influential musician/producer/cultural commentator Brian Eno, written with his co-operation.
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Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway's critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author's treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. A supporting Web site at www.stat.lsa.umich.edu/~faraway/ELM holds all of the data described in the book. Statisticians need to be familiar with a broad range of ideas and techniques. This book provides a well-stocked toolbox of methodologies, and with its unique presentation of these very modern statistical techniques, holds the potential to break new ground in the way graduate-level courses in this area are taught..
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Books on regression and the analysis of variance abound-many are introductory, many are theoretical While most of them do serve a purpose, the fact remains that data analysis cannot be properly learned without actually doing it, and this means using a statistical software package. There are many of these to choose from as well, all with their particular strengths and weaknesses. Lately, however, one such package has begun to rise above the others thanks to its free availability, its versatility as a programming language, and its interactivity. That software is R. In the first book that directly uses R to teach data analysis, Linear Models with R focuses on the practice of regression and analysis of variance. It clearly demonstrates the different methods available and more importantly, in which situations each one applies. It covers all of the standard topics, from the basics of estimation to missing data, factorial designs, and block designs, but it also includes discussion on topics, such as model uncertainty, rarely addressed in books of this type. The presentation incorporates an abundance of examples that clarify both the use of each technique and the conclusions one can draw from the results. All of the data sets used in the book are available for download from http://www.stat.lsa.umich.edu/~faraway/LMR/. The author assumes that readers know the essentials of statistical inference and have a basic knowledge of data analysis, linear algebra, and calculus. The treatment reflects his view of statistical theory and his belief that qualitative statistical concepts, while somewhat more difficult to learn, are just as important because they enable us to practice statistics rather than just talk about it..
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