Description
Graphics for Statistics and Data Analysis with R (2nd Ed.)
Graphics for Statistics and Data Analysis with R
Chapman & Hall/CRC Texts in Statistical Science Series
Author: Keen Kevin J.
Language: EnglishSubjects for Graphics for Statistics and Data Analysis with R:
Keywords
Kernel Density Estimate; Normal Quantile Quantile Plot; Dot Chart; Quantile Quantile Plots; Ggplot2 Package; Cumulative Distribution Function; Nonparametric Kernel Density Estimate; Data Set; Stacked Bar Chart; Gaussian Kernel Density Estimate; Relative Frequency Histogram; Normal Quantile Plot; Data Ink Ratio; Sunflower Plot; Simple Linear Regression Model; Mid-parental Height; Horizontal Bar Chart; Kernel Density Estimation; EDF; Galton’s Data; Pie Chart; Bar Chart; Nonparametric Density Estimate; LOWESS Curve; Function Lowess
Publication date: 12-2020
· 15.6x23.4 cm · Paperback
Publication date: 05-2018
· 15.6x23.4 cm · Hardback
Description
/li>Contents
/li>Biography
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Praise for the First Edition
"The main strength of this book is that it provides a unified framework of graphical tools for data analysis, especially for univariate and low-dimensional multivariate data. In addition, it is clearly written in plain language and the inclusion of R code is particularly useful to assist readers? understanding of the graphical techniques discussed in the book. ? It not only summarises graphical techniques, but it also serves as a practical reference for researchers and graduate students with an interest in data display." -Han Lin Shang, Journal of Applied Statistics
Graphics for Statistics and Data Analysis with R, Second Edition, presents the basic principles of graphical design and applies these principles to engaging examples using the graphics and lattice packages in R. It offers a wide array of modern graphical displays for data visualization and representation. Added in the second edition are coverage of the ggplot2 graphics package, material on human visualization and color rendering in R, on screen, and in print.
Features
- Emphasizes the fundamentals of statistical graphics and best practice guidelines for producing and choosing among graphical displays in R
- Presents technical details on topics such as: the estimation of quantiles, nonparametric and parametric density estimation; diagnostic plots for the simple linear regression model; polynomial regression, splines, and locally weighted polynomial regression for producing a smooth curve; Trellis graphics for multivariate data
- Provides downloadable R code and data for figures at www.graphicsforstatistics.com
Kevin J. Keen is a Professor of Mathematics and Statistics at the University of Northern British Columbia (Prince George, Canada) and an Accredited Professional StatisticianTM by the Statistical Society of Canada and the American Statistical Association.
Introduction. The Graphical Display of Information. A Single Discrete Variable. Basic Charts for the Distribution of a Single Discrete Variable. Advanced Charts for the Distribution of a Single Discrete Variable. A Single Continuous Variable. Exploratory Plots for the Distribution of a Single Continuous Variable. Diagnostic Plots for the Distribution of a Continuous Variable. Nonparametric Density Estimation for a Single Continuous Variable. Parametric Density Estimation for a Single Continuous Variable. Two Variables. Depicting the Distribution of Two Discrete Variables. Depicting the Distribution of One Continuous Variable and One Discrete Variable. Depicting the Distribution of Two Continuous Variables. Statistical Models for Two or More Variables. Graphical Displays for Simple Linear Regression. Graphical Displays for Polynomial Regression. Visualizing Multivariate Data. References. Index.
Kevin J. Keen is a Professor of Mathematics and Statistics at the University of Northern British Columbia (Prince George, Canada) and an Accredited Professional StatisticianTM by the Statistical Society of Canada and the American Statistical Association.
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