Using R for Introductory Statistics : John Verzani

Description of this bookThe pacing and content of this book are a bit different from those in most introductorytexts. More time is spent with exploratory data analysis (EDA) than is typical, a chapteron simulation is included, and a unified approach to linear models is given. If this book isbeing used in a semester-long sequence, keep in mind that the early material isconceptually easier but requires that the student learn more on the computer. The pacingis not as might be expected, as time must be spent learning the software and itsidiosyncrasies.Chapters 1 through 4 take a rather leisurely approach to the material, developing thetools of data manipulation and exploration. The material is broken up so that users whowish only to analyze univariate data can safely avoid the details of data frames, lists, andmodel formulas covered in Chapter 4. Those wishing to cover all the topics in the bookcan work straight through these first four chapters.Chapter 5 covers populations, random samples, sampling distributions, and the centrallimit theorem. There is no attempt to cover the background probability conceptsthoroughly. We go over only what is needed in the sequel to make statistical inference.Chapter 6 introduces simulation and the basics of defining functions. Since R is aprogramming language, simulations are a strong selling point for R’s use in theclassroom.Traditional topics in statistical inference are covered in chapters 7–11. Chapters 7, 8,and 9 cover confidence intervals, significance tests, and goodness of fit. Chapters 10 and11 cover linear models. Although this material is broken up into chapters on linearregression and analysis of variance, for the most part we use a common approach to both.Chapter 12 covers a few extensions to the linear model to illustrate how R is used in aconsistent manner with many different statistical models. The necessary background toappreciate the models is left for the reader to find.The appendices cover some background material and have information on writingfunctions and producing graphics that goes beyond the scope of the rest of the text.Typographic conventionsThe book uses a few quirky typographic conventions. Variables and commands aretypeset with a data typeface; functions as a. function() (with accompanying parentheses);and arguments to functions as col= (with a trailing equal sign). Help-page references havea leading question mark: ?par. Data sets are typeset like faithful. Those that require apackage to be loaded prior to usage also have the package name, such as Animals(MASS). Large blocks of commands are set off with a vertical bar:> hist(rnorm(100)) # draw histogramOften the commands include a comment, as does the one above. The output is formattedto have 4 digits and 65 characters per column, and the type size is smaller, in order to getmore information in a single line. This may cause minor differences if the examples aretried with different settings.