Basic Statistics with R: Reaching Decisions with Data provides an understanding of the processes at work in using data for results. Sections cover data collection and discuss exploratory analyses, including visual graphs, numerical summaries, and relationships between variables - basic probability, and statistical inference - including hypothesis testing and confidence intervals. All topics are taught using real-data drawn from various fields, including economics, biology, political science and sports. Using this wide variety of motivating examples allows students to directly connect and make statistics essential to their field of interest, rather than seeing it as a separate and ancillary knowledge area. In addition to introducing students to statistical topics using real data, the book provides a gentle introduction to coding, having the students use the statistical language and software R. Students learn to load data, calculate summary statistics, create graphs and do statistical inference using R with either Windows or Macintosh machines. Features real-data to give students an engaging practice to connect with their areas of interest Evolves from basic problems that can be worked by hand to the elementary use of opensource R software Offers a direct, clear approach highlighted by useful visuals and examples
Cox, D. R. and Oakes, D. (1984), Analysis of Survival Data, Chapman & Hall, London. Everitt, B. S. (1994), A Handbook of Statistical Analyses Using S-PLUS, Chapman & Hall, London. Hájek, J., Šidák, Z., and Sen, P. K. (1999), ...
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.
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.
Basic Statistics provides an accessible and comprehensive introduction to statistics using the free, state-of-the-art, powerful software program R. This book is designed to both introduce students to key concepts in statistics and to ...
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.
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 Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis.
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 .
This book presents some of the most important modeling and prediction techniques, along with relevant applications.
The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject.