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Univariate, bivariate, and multivari...

Denis, Daniel J., (1974-)

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Univariate, bivariate, and multivariate statistics using R = quantitative tools for data analysis and data science /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Univariate, bivariate, and multivariate statistics using R/ Daniel J. Denis.
Reminder of title:	quantitative tools for data analysis and data science /
Author:	Denis, Daniel J.,
Published:	Hoboken, NJ :John Wiley & Sons, : 2020.,
Description:	1 online resource (xvii, 366 p.) :ill.
[NT 15003449]:	Introduction to applied statistics -- Introduction to R and computational statistics -- Exploring data with R : essential graphics and visualization -- Means, correlations, counts : drawing inferences using easy-to-implement statistical tests -- Power analysis and sample size estimation using R -- Analysis of variance : fixed effects, random effects, mixed models and repeated measures -- Simple and multiple linear regression -- Logistic regression and the generalized linear model -- Multivariate analysis of variance (MANOVA) and discriminant analysis -- Principal components analysis -- Exploratory factor analysis -- Cluster analysis -- Nonparametric tests.
Subject:	Analysis of variance - Textbooks. -
Online resource:	https://onlinelibrary.wiley.com/doi/book/10.1002/9781119549963
ISBN:	9781119549963

Univariate, bivariate, and multivariate statistics using R = quantitative tools for data analysis and data science /
Denis, Daniel J.,1974-

Univariate, bivariate, and multivariate statistics using Rquantitative tools for data analysis and data science /[electronic resource] :Daniel J. Denis. - 1st ed. - Hoboken, NJ :John Wiley & Sons,2020. - 1 online resource (xvii, 366 p.) :ill.

Includes bibliographical references and index.

Introduction to applied statistics -- Introduction to R and computational statistics -- Exploring data with R : essential graphics and visualization -- Means, correlations, counts : drawing inferences using easy-to-implement statistical tests -- Power analysis and sample size estimation using R -- Analysis of variance : fixed effects, random effects, mixed models and repeated measures -- Simple and multiple linear regression -- Logistic regression and the generalized linear model -- Multivariate analysis of variance (MANOVA) and discriminant analysis -- Principal components analysis -- Exploratory factor analysis -- Cluster analysis -- Nonparametric tests.

"This book provides a user-friendly and practical guide on R, with emphasis on covering a broader range of statistical methods than previous books on R. This is a "how to" book and will be of use to undergraduates and graduate students along with researchers and professionals who require a quick go-to source to help them perform essential statistical analyses and data management tasks in R. The book only assumes minimal prior knowledge of statistics, providing readers with the tools they need right now to help them understand and interpret their data analyses. This book covers univariate, bivariate, and multivariate statistical methods, as well as some nonparametric tests. It provides students with a hands-on easy-to-read manual on the wealth of applied statistics and essential R computing that they will need for their theses, dissertations, and research publications. A strength of this book is its scope of coverage of univariate through to multivariate procedures, while simultaneously serving as a friendly introduction to R software"--

ISBN: 9781119549963Subjects--Topical Terms:

758314
Analysis of variance
--Textbooks.

LC Class. No.: QA279 / .D45776 2020

Dewey Class. No.: 519.5/302855133

Univariate, bivariate, and multivariate statistics using R = quantitative tools for data analysis and data science /
LDR:02856cmm a2200301 a 4500 001 2248157
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020 $a 9781119549963 $q (electronic bk.)
020 $a 9781119549918 $q (electronic bk.)
020 $a 9781119549956 $q (electronic bk.)
020 $z 9781119549932 $q (hbk.)
020 $z 1119549930 $q (hbk.)
035 $a 1145085252
040 $a DLC $b eng $c DLC $d OCLCO $d EBLCP $d SNM $d DG1 $d N $d UKAHL $d OCLCQ $d YDX $d OCLCF $d YDX
050 0 4 $a QA279 $b .D45776 2020
082 0 0 $a 519.5/302855133 $2 23
100 1 $a Denis, Daniel J., $d 1974- $3 3512914
245 1 0 $a Univariate, bivariate, and multivariate statistics using R $h [electronic resource] : $b quantitative tools for data analysis and data science / $c Daniel J. Denis.
250 $a 1st ed.
260 $a Hoboken, NJ : $b John Wiley & Sons, $c 2020.
300 $a 1 online resource (xvii, 366 p.) : $b ill.
504 $a Includes bibliographical references and index.
505 0 $a Introduction to applied statistics -- Introduction to R and computational statistics -- Exploring data with R : essential graphics and visualization -- Means, correlations, counts : drawing inferences using easy-to-implement statistical tests -- Power analysis and sample size estimation using R -- Analysis of variance : fixed effects, random effects, mixed models and repeated measures -- Simple and multiple linear regression -- Logistic regression and the generalized linear model -- Multivariate analysis of variance (MANOVA) and discriminant analysis -- Principal components analysis -- Exploratory factor analysis -- Cluster analysis -- Nonparametric tests.
520 $a "This book provides a user-friendly and practical guide on R, with emphasis on covering a broader range of statistical methods than previous books on R. This is a "how to" book and will be of use to undergraduates and graduate students along with researchers and professionals who require a quick go-to source to help them perform essential statistical analyses and data management tasks in R. The book only assumes minimal prior knowledge of statistics, providing readers with the tools they need right now to help them understand and interpret their data analyses. This book covers univariate, bivariate, and multivariate statistical methods, as well as some nonparametric tests. It provides students with a hands-on easy-to-read manual on the wealth of applied statistics and essential R computing that they will need for their theses, dissertations, and research publications. A strength of this book is its scope of coverage of univariate through to multivariate procedures, while simultaneously serving as a friendly introduction to R software"-- $c Provided by publisher.
588 $a Description based on print version record.
650 0 $a Analysis of variance $v Textbooks. $3 758314
650 0 $a Multivariate analysis $v Textbooks. $3 3512915
650 0 $a Mathematical statistics $x Data processing $v Textbooks. $3 3512916
650 0 $a R (Computer program language) $v Textbooks. $3 3197726
856 4 0 $u https://onlinelibrary.wiley.com/doi/book/10.1002/9781119549963