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1. Introduction 13 are discussed in Chapter 9. Finally, in Chapter 10, we consider a setting in which we have input variables but no output variable. In particular, we present principal components analysis, K-means clustering, andhierarchical clustering. At the end of each chapter, we present one or more R lab sections in which we systematically work through applications of the various methods discussed in that chapter. These labs demonstrate the strengths and weaknesses of the various approaches, and also provide a useful reference for the syntax required to implement the various methods. The reader may choose to work through the labs at his or her own pace, or the labs may be the focus of group sessions as part of a classroom environment. Within each R lab, we present the results that we obtained when we performed the lab at the time of writing this book. However, new versions of R are continuously released, and over time, the packages called in the labs will be updated. Therefore, in the future, it is possible that the results shown in the lab sections may no longer correspond precisely to the results obtained by the reader who performs the labs. As necessary, we will post updates to the labs on the book website. We use the symbol to denote sections or exercises that contain more challenging concepts. These can be easily skipped by readers who do not wish to delve as deeply into the material, or who lack the mathematical background. Data Sets Used in Labs and Exercises In this textbook, we illustrate statistical learning methods using applications from marketing, finance, biology, and other areas. The ISLR package available on the book website contains a number of data sets that are required in order to perform the labs and exercises associated with this book. One other data set is contained in the MASS library, and yet another is part of the base R distribution. Table 1.1 contains a summary of the data sets required to perform the labs and exercises. A couple of these data sets are also available as text files on the book website, for use in Chapter 2. Book Website Thewebsiteforthisbookislocatedat www.StatLearning.com
14 1. Introduction Name Auto Boston Caravan Carseats College Default Hitters Khan NCI60 OJ Portfolio Smarket USArrests Wage Weekly Description Gas mileage, horsepower, and other information for cars. Housing values and other information about Boston suburbs. Information about individuals offered caravan insurance. Information about car seat sales in 400 stores. Demographic characteristics, tuition, and more for USA colleges. Customer default records for a credit card company. Records and salaries for baseball players. Gene expression measurements for four cancer types. Gene expression measurements for 64 cancer cell lines. Sales information for Citrus Hill and Minute Maid orange juice. Past values of financial assets, for use in portfolio allocation. Daily percentage returns for S&P 500 over a 5-year period. Crime statistics per 100,000 residents in 50 states of USA. Income survey data for males in central Atlantic region of USA. 1,089 weekly stock market returns for 21 years. TABLE 1.1. A list of data sets needed to perform the labs and exercises in this textbook. All data sets are available in the ISLR library, with the exception of Boston (part of MASS) andUSArrests (part of the base R distribution). It contains a number of resources, including the R package associated with this book, and some additional data sets. Acknowledgements A few of the plots in this book were taken from ESL: Figures 6.7, 8.3, and 10.12. All other plots are new to this book.
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Index 423 noise, 22, 228 non-linear
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Index 425 read.table(), 48, 49 regs
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