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Books on the topic 'R statistical software'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Pace, Larry. Beginning R: An Introduction to Statistical Programming. Apress, 2012.

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2

The art of R programming: Tour of statistical software design. No Starch Press, 2011.

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3

How to do linguistics with R: Data exploration and statistical analysis. John Benjamins Publishing Company, 2015.

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4

Ken, Kleinman, ed. Using R for data management, statistical analysis, and graphics. CRC Press, 2011.

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5

Clinical trial data analysis using R. CRC Press, 2011.

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6

Adaptive design theory and implementation using SAS and R. CRC Press, 2014.

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7

Chang, Mark. Adaptive design theory and implementation using SAS and R. CRC Press, 2007.

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8

Adaptive design theory and implementation using SAS and R. Taylor & Francis, 2008.

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9

Ecological models and data in R. Princeton University Press, 2008.

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10

Babeshko, Lyudmila, and Irina Orlova. Econometrics and econometric modeling in Excel and R. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1079837.

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The textbook includes topics of modern econometrics, often used in economic research. Some aspects of multiple regression models related to the problem of multicollinearity and models with a discrete dependent variable are considered, including methods for their estimation, analysis, and application. A significant place is given to the analysis of models of one-dimensional and multidimensional time series. Modern ideas about the deterministic and stochastic nature of the trend are considered. Methods of statistical identification of the trend type are studied. Attention is paid to the evaluati

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11

Computer simulation and data analysis in molecular biology and biophysics: An introduction using R. Springer, 2009.

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12

Using R for introductory statistics. Chapman & Hall/CRC, 2005.

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13

service), SpringerLink (Online, ed. A Tiny Handbook of R. Springer-Verlag Berlin Heidelberg, 2011.

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14

Stephen, Weston, ed. Parallel R. O'Reilly Media, 2012.

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15

Pace, Larry. Beginning R: An Introduction to Statistical Programming. Apress, 2012.

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16

Micheaux, Pierre Lafaye de, Rémy Drouilhet, and Benoit Liquet. The R Software: Fundamentals of Programming and Statistical Analysis. Springer, 2016.

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17

The R Software Fundamentals Of Programming And Statistical Analysis. Springer-Verlag New York Inc., 2014.

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18

Bayesian Computation with R (Use R). Springer, 2007.

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19

SAS Institute. Introduction to Statistical Analysis with SAS/ASSIST (R) Software Course Notes. SAS Publishing, 1996.

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20

SAS Institute. Introduction to Statistical Quality Improvement Using SAS/QC (R) Software Course Notes. SAS Publishing, 1996.

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21

How to Do Linguistics with R: Data Exploration and Statistical Analysis. Benjamins Publishing Company, John, 2015.

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22

Braun, John, and John Maindonald. Data Analysis and Graphics Using R. Cambridge University Press, 2003.

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23

Modern Regression Techniques Using R A Practical Guide For Students And Researchers. Sage Publications (CA), 2009.

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24

Láruson, Áki Jarl, and Floyd Allan Reed. Population Genetics with R. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198829539.001.0001.

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Population genetics is an inherently quantitative discipline. Because the focus of population genetics studies is usually on abstract concepts like the frequencies of genetic variants over time, it can at first glance be difficult to conceptualize and appropriately visualize. As more and more quantitative models and methods have become established in the discipline, it has become necessary for people just entering the field to quickly develop a good understanding of the many layers of complex approaches, so as to correctly interpret even basic results. An unfortunate side effect of the widespr

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25

Elsner, James B., and Thomas H. Jagger. Hurricane Climatology. Oxford University Press, 2013. http://dx.doi.org/10.1093/oso/9780199827633.001.0001.

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Hurricanes are nature's most destructive storms and they are becoming more powerful as the globe warms. Hurricane Climatology explains how to analyze and model hurricane data to better understand and predict present and future hurricane activity. It uses the open-source and now widely used R software for statistical computing to create a tutorial-style manual for independent study, review, and reference. The text is written around the code that when copied will reproduce the graphs, tables, and maps. The approach is different from other books that use R. It focuses on a single topic and explai

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26

Chapman, Stephen J. MATLAB(r) Programming for Engineers. Brooks/Cole Publishing Company, 1999.

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27

Multivariable Calculus with Maple V(r), Preliminary Edition. Wiley, 1993.

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28

Milliken, George A., Dayanand N. Naik, Walter W. Stroup, Russell D. Wolfinger, Ravindra Khattree, and Ramon C. Littell. Applied Multivariate Statistics with SAS (R) Software. Sas Inst, 1996.

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29

Peacock, Janet L., Sally M. Kerry, and Raymond R. Balise. Presenting Medical Statistics from Proposal to Publication. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198779100.001.0001.

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Presenting Medical Statistics from Proposal to Publication (second edition) aims to show readers how to conduct a wide range of statistical analyses from sample size calculations through to multifactorial regressions that are needed in the research process. The second edition of ‘Presenting’ has been revised and updated and now includes Stata, SAS, SPSS, and R. The book shows how to interpret each computer output and illustrates how to present the results and accompanying text in a format suitable for a peer-reviewed journal article or research report. All analyses are illustrated using real d

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30

SAS Institute. The Encore Series (R): SAS/GRAPH (R) Software. SAS Institute, Incorporated, 1994.

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31

SAS Institute. Basic Statistics Using SAS (R) Software Course Notes. SAS Publishing, 1998.

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32

Institute, SAS. Introduction to Statistics Using SAS/INSIGHT(R) Software Course Notes. SAS Publishing, 1998.

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33

SAS Institute. Highlighting SAS/SPECTRAVIEW (R) Software, Version 6. SAS Institute, Incorporated, 1994.

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34

Sall, John, Lee Creighton, and D. Ann Lehman Ph. JMP(R) Start Statistics: A Guide to Statistics and Data Analysis Using JMP(R) and JMP IN(R) Software, Third Edition. 3rd ed. SAS, 2005.

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35

Verzani, John. Using R for Introductory Statistics. Taylor & Francis Group, 2018.

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36

Verzani, John. Using R for Introductory Statistics. Chapman & Hall/CRC, 2004.

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37

Verzani, John. Using R for Introductory Statistics. Taylor & Francis Group, 2018.

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38

Allerhand, Mike. A Tiny Handbook of R. Springer, 2011.

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39

Fogarty, Michael J., and Jeremy S. Collie. Fishery Ecosystem Dynamics. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198768937.001.0001.

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This book provides an integrated framework for the quantitative analysis of exploited aquatic ecosystems, tracing the critical linkages between fundamental ecological processes and their implications for sustainable resource management. Examples are drawn from freshwater and marine ecosystems throughout the world. Fishery ecosystems have historically been subject to a broad array of human interventions, ranging from large-scale removal of biomass to deliberate attempts at ecosystem engineering involving species introductions, habitat alteration, and selective reorganization of ecosystem struct

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40

Basu, Sanjay. Modeling Public Health and Healthcare Systems. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190667924.001.0001.

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This book aims to empower readers to learn and apply engineering, operations research, and modeling techniques to improve public health programs and healthcare systems. Readers will engage in in-depth study of disease detection and control strategies from a “systems science” perspective, which involves the use of common engineering, operations research, and mathematical modeling techniques such as optimization, queuing theory, Markov and Kermack-McKendrick models, and microsimulation. Chapters focus on applying these techniques to classical public health dilemmas such as how to optimize screen

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41

Mesbah, Mounir, and Christophe Lalanne. Biostatistics and Computer-Based Analysis of Health Data Using the R Software. Elsevier Science & Technology Books, 2016.

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42

SAS Institute. Changes and Enhancements to SAS(R) Software for Personal Computers, Release 6.04 (SAS Technical Report). SAS Institute, Incorporated, 1990.

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43

Weston, Stephen, and Q. Ethan McCallum. Parallel R. O'Reilly Media, Incorporated, 2011.

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44

Seamless R And C Integration With Rcpp. Springer-Verlag New York Inc., 2013.

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45

R for Everyone: Advanced Analytics and Graphics (2nd Edition) (Addison-Wesley Data & Analytics Series). Addison-Wesley Professional, 2017.

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46

Verzani, John. Getting Started with RStudio. O'Reilly Media, Incorporated, 2011.

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47

Getting Started with Rstudio. O'Reilly Media, 2011.

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48

R for Everyone: Advanced Analytics and Graphics (Addison-Wesley Data and Analytics). Addison-Wesley Professional, 2013.

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