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Relevant bibliographies by topics / Statistical visualisation / Books

Books on the topic 'Statistical visualisation'

To see the other types of publications on this topic, follow the link: Statistical visualisation.

Author: Grafiati

Published: 9 March 2023

Last updated: 10 March 2023

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1

Visualization and verbalization of data. Boca Raton: CRC Press, Taylor & Francis Group, 2014.

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2

Farebrother, R. W. Visualizing statistical models and concepts. New York: Marcel Dekker, 2002.

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3

SAS Institute. Discovering JMP 11. [Place of publication not identified]: SAS Institute, 2013.

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4

R: Data analysis and visualization : a course in five modules. Birmingham [United Kingdom]: Packt, 2016.

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5

Impact of Visual Simulations in Statistics: The Role of Interactive Visualizations in Improving Statistical Knowledge. Springer, 2014.

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6

O'Donoghue, Peter, Ambikesh Jayal, Allistair McRobert, and Giles Oatley. Sports Analytics: Analysis, Visualisation and Decision Making in Sports Performance. Taylor & Francis Group, 2018.

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7

O'Donoghue, Peter, Ambikesh Jayal, Allistair McRobert, and Giles Oatley. Sports Analytics: Analysis, Visualisation and Decision Making in Sports Performance. Taylor & Francis Group, 2018.

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8

O'Donoghue, Peter, Ambikesh Jayal, Allistair McRobert, and Giles Oatley. Sports Analytics: Analysis, Visualisation and Decision Making in Sports Performance. Taylor & Francis Group, 2018.

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9

Data Analytics for business Edition. Data Analytics : for Statisticians Biologists Scientific Research Surveys : Collect Data with Statistical Tables to Fill for Data /analysis *Average Variance Standard Deviation*: Data Visualisation and Statistical Inference Time Series Data Analysis Tracker. Independently Published, 2020.

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10

Data Analytics for Business : Collect Data Tool with Statistical Tables to Fill for Data Analytics / Analysis *Average Variance Standard Deviation*: Data Visualisation and Statistical Inference Time Series Data Analysis Tracker a Manual for Polls Surveys. Independently Published, 2020.

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11

Data Analytics for Business : Collect Data Tool with Statistical Tables to Fill for Data Analytics / Analysis *Average Variance Standard Deviation*: Data Visualisation and Statistical Inference, Time Series Data Analysis Tracker, a Manual for Polls Surveys. Independently Published, 2020.

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12

Data Analytics for Business : Collect Data Tool with Statistical Tables to Fill for Data Analytics / Analysis *Average Variance Standard Deviation*: Data Visualisation and Statistical Inference, Time Series Data Analysis Tracker, a Manual for Polls Surveys. Independently Published, 2020.

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13

Statistics and Data Visualisation with Python. CRC Press LLC, 2022.

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14

Statistics and Data Visualisation with Python. CRC Press LLC, 2022.

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15

Rogel-Salazar, Jesus. Statistics and Data Visualisation with Python. Taylor & Francis Group, 2022.

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16

Rogel-Salazar, Jesus. Statistics and Data Visualisation with Python. Taylor & Francis Group, 2022.

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17

Rogel-Salazar, Jesus. Statistics and Data Visualisation with Python. Taylor & Francis Group, 2022.

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18

Union of International Associations Staff. Yearbook of International Organizations: Statistics, Visualisations and Patterns. De Gruyter, Inc., 2008.

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19

Visualizing Statistical Models and Concepts (Statistics: a Series of Textbooks and Monogrphs). CRC, 2002.

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20

Kirk, Andy. Data Visualisation: A Handbook for Data Driven Design. SAGE Publications, Limited, 2016.

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21

Data Visualisation: A Handbook for Data Driven Design. SAGE Publications, Limited, 2019.

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22

Data visualisation: A handbook for data driven design. Sage Publications, 2016.

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23

Kirk, Andy. Data Visualisation: A Handbook for Data Driven Design. SAGE Publications, Limited, 2019.

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24

Garcia, Guilherme D. Data Visualization and Analysis in Second Language Research. Taylor & Francis Group, 2021.

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25

Data Visualization and Analysis in Second Language Research. Taylor & Francis Group, 2021.

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26

Garcia, Guilherme D. Data Visualization and Analysis in Second Language Research. Taylor & Francis Group, 2021.

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27

Garcia, Guilherme D. Data Visualization and Analysis in Second Language Research. Taylor & Francis Group, 2021.

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28

Tracy, Ronald L., Kieran Mathieson, and David P. Doane. Visual Statistics Text with Student CD-ROM package. 2nd ed. McGraw-Hill/Irwin, 2000.

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29

Visual Statistics Text with Student CD-ROM package. McGraw-Hill/Irwin, 2000.

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30

Gibson, Catherine. Geographies of Nationhood. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192844323.001.0001.

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Geographies of Nationhood examines the meteoric rise of ethnographic mapmaking in the nineteenth and early twentieth centuries as a form of visual and material culture that gave expression to territorialised visions of nationhood. In the Russian Empire’s Baltic provinces, the development of ethnographic cartography, as part of the broader field of statistical data visualisation, progressively became a tool that lent legitimacy and an experiential dimension to nationalist arguments, as well as a wide range of alternative spatial configurations that rendered the inhabitants of the Baltic as part of local, imperial, and global geographies. The book argues that map production and the spread of cartographic literacy as a mass phenomenon in Baltic society transformed how people made sense of linguistic, ethnic, and religious similarities and differences by imbuing them with an alleged scientific objectivity that was later used to determine the political structuring of the Baltic region and beyond. The book treads new ground by expanding the focus beyond elites to include a diverse range of mapmakers, such as local bureaucrats, commercial enterprises, clergymen, family members, teachers, and landowners. It shifts the focus from imperial learned and military institutions to examine the proliferation of mapmaking across diverse sites in the Russian Empire, including the provincial administration, local learned societies, private homes, and schools. Understanding ethnographic maps in the social context of their production, circulation, consumption, and reception is crucial for assessing their impact as powerful shapers of popular geographical conceptions of nationhood, state-building, and border-drawing.

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31

Gamerman, Dani, Marcos O. Prates, Thais Paiva, and Vinicius D. Mayrink. Building a Platform for Data-Driven Pandemic Prediction: From Data Modelling to Visualisation - the CovidLP Project. Taylor & Francis Group, 2021.

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32

Gamerman, Dani, Marcos O. Prates, Thais Paiva, and Vinicius D. Mayrink. Building a Platform for Data-Driven Pandemic Prediction: From Data Modelling to Visualisation - the CovidLP Project. Taylor & Francis Group, 2021.

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33

Sievert, Carson. Interactive Web-Based Data Visualizations with R and Plotly. Taylor & Francis Group, 2020.

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34

Tripathy, B. K., Shrusti Ghela, and Anveshrithaa Sundareswaran. Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization. Taylor & Francis Group, 2021.

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35

Tripathy, B. K., Anveshrithaa S, and Shrusti Ghela. Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization. Taylor & Francis Group, 2021.

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36

Tripathy, B. K., Shrusti Ghela, and Anveshrithaa Sundareswaran. Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization. Taylor & Francis Group, 2021.

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37

Moraga, Paula. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny. Taylor & Francis Group, 2019.

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38

Moraga, Paula. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny. Taylor & Francis Group, 2019.

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39

Moraga, Paula. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny. Taylor & Francis Group, 2019.

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40

Moraga, Paula. Geospatial Health Data. Taylor & Francis Group, 2019.

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