This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets. All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.
The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach.
This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful.
The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach.
After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book.
Fisher also gave us an expression for the standard error (standard deviation) for his measure of skewness: SE = IL ... Mn+DZ¥K—Y)l. Kn_n,. (35m. g2. _. (n—l)(n—2)(n—3)s4. _. (n—2)(n—3). As you can see, at the heart of it is the sum of ...
Aitkin , M. , Anderson , D. , Francis , B. and Hinde , J. ( 1989 ) Statistical Modelling in GLIM . ... Caroll , R. J. and Ruppert , D. ( 1988 ) Transformation and Weighting in Regression . New York , Chapman and Hall .
At the end of the book, there are several projects that require the use of multiple statistical techniques that could be used as a take-home final exam or final project for a class.
This book presents some of the most important modeling and prediction techniques, along with relevant applications.
The book includes more than 200 exercises with fully worked solutions. Some familiarity with basic statistical concepts, such as linear regression, is assumed. No previous programming experience is needed.
Continuity of Examples – A master data set containing nearly all of the data used in the book’s examples is introduced at the beginning of the text. This ensures continuity in the examples used across the text.