The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. The aim of Modern Statistics with R is to introduce you to key parts of the modern statistical toolkit. It teaches you: - Data wrangling - importing, formatting, reshaping, merging, and filtering data in R. - Exploratory data analysis - using visualisation and multivariate techniques to explore datasets. - Statistical inference - modern methods for testing hypotheses and computing confidence intervals. - Predictive modelling - regression models and machine learning methods for prediction, classification, and forecasting. - Simulation - using simulation techniques for sample size computations and evaluations of statistical methods. - Ethics in statistics - ethical issues and good statistical practice. - R programming - writing code that is fast, readable, and free from bugs. Starting from the very basics, Modern Statistics with R helps you learn R by working with R. Topics covered range from plotting data and writing simple R code to using cross-validation for evaluating complex predictive models and using simulation for sample size determination. 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.
A basic understanding of probability theory can enhance comprehension of certain concepts discussed within this book.
The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand.
Some multivariate methods have been specifically designed to decompose the variability between codon usage within the differently abundantamino acids (Grantham et al., 1981; Perrière and Thioulouse, 2002), and this enables discovery of ...
Features: ● Assumes minimal prerequisites, notably, no prior calculus nor coding experience ● Motivates theory using real-world data, including all domestic flights leaving New York City in 2013, the Gapminder project, and the data ...
The second edition of this book includes a number of advances and insights that have occurred since the first edition appeared.
OpenIntro Statistics
(7.41) This is the basis for Pearson's )(2 test, and can be phrased as follows: “Given the null hypothesis H0 that the data are drawn from a population following the model M, the probability that the X 2 statistic in ...
(See Darroch & Ratcliff, 1972, .) This is usually very much faster than GLM fitting but is less flexible. To use loglin we need to form the frequencies into an array. A simple way to do this is to construct a matrix subscript from the ...
Even the most hesitant student is likely to embrace the material with this text." —David A.M. Peterson, Department of Political Science, Iowa State University Drawing on examples from across the social and behavioral sciences, Statistics ...
The book aims to put this methodology firmly within the grasp of undergraduates.