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Books on the topic 'Statistical graphs'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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1

Random graphs for statistical pattern recognition. Hoboken, NJ: Wiley & Sons, 2002.

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2

Marchette, David J. Random Graphs for Statistical Pattern Recognition. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2004. http://dx.doi.org/10.1002/047172209x.

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3

Jaroslav, Nešetřil, and Winkler P. 1946-, eds. Graphs, morphisms, and statistical physics: DIMACS Workshop Graphs, Morphisms and Statistical Physics, March 19-21, 2001, DIMACS Center. Providence, RI: American Mathematical Society, 2004.

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4

Aldrich, James O. Building SPSS graphs to understand data. Thousand Oaks: SAGE Publications, 2013.

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5

Aldrich, James O. Building SPSS graphs to understand data. Thousand Oaks: SAGE Publications, 2013.

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6

Bayesian networks and decision graphs. New York: Springer, 2001.

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7

W, Dyal William, Eddins Donald L, and Centers for Disease Control (U.S.), eds. Descriptive statistics: Tables, graphs, & charts. Atlanta, Ga: U.S. Dept. of Health and Human Services, Public Health Service, Centers for Disease Control, 1988.

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8

Vladas, Sidoravicius, and Smirnov S. (Stanislav) 1970-, eds. Probability and statistical physics in St. Petersburg: St. Petersburg School in Probability and Statistical Physics : June 18-29, 2012 : St. Petersburg State University, St. Petersburg, Russia. Providence, Rhode Island: American Mathematical Society, 2015.

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9

Institute, SAS, ed. SAS system for statistical graphics. Cary, NC: SAS Institute, 1991.

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10

Creating more effective graphs. Hoboken, N.J: Wiley-Interscience, 2005.

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11

McMillan, Dawn. Understanding animals graphs. Mankato, Minn: Capstone Press, 2009.

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12

McMillan, Dawn. Understanding animals graphs. Mankato, Minn: Capstone Press, 2009.

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13

Picture graphs. Minneapolis: Lerner Publications, 2013.

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14

Institute, SAS. SAS/GRAPH 9.2 reference. Cary, NC: SAS Institute, 2009.

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15

McMillan, Dawn. Understanding animals graphs. Mankato, Minn: Capstone Press, 2009.

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16

Linda, Lesniak, ed. Graphs & digraphs. 4th ed. Boca Raton: Chapman & Hall/CRC, 2005.

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17

Linda, Lesniak, and Behzad Mehdi, eds. Graphs & digraphs. 2nd ed. Monterey, Calif: Wadsworth & Brooks/Cole Advanced Books & Software, 1986.

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18

Taylor-Butler, Christine. Understanding charts and graphs. New York: Children's Press, 2012.

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19

Brian, Everitt, ed. A handbook of statistical graphics using SAS ODS. Boca Raton: CRC Press, Taylor & Francis Group, 2015.

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20

Tierney, Cornelia C. Changes over time: Graphs. Menlo Park, Calif: Dale Seymour Publications, 1998.

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21

Tierney, Cornelia C. Changes over time: Graphs. Menlo Park, Calif: Dale Seymour Publications, 1998.

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22

Russell, Susan Jo. Sorting: Groups and graphs. Palo Alto, CA: Dale Seymour Publications, 1990.

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23

Understanding charts and graphs. New York: Children's Press, 2012.

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24

Kalyagin, V. A., A. P. Koldanov, P. A. Koldanov, and P. M. Pardalos. Statistical Analysis of Graph Structures in Random Variable Networks. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60293-2.

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25

Lions and tigers and graphs! Oh my! Mankato, Minn: Capstone Press, 2011.

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26

Kumar, Manoj. Post-harvest profile of grapes. Nagpur: Govt. of India, Ministry of Agriculture, Dept. of Agriculture & Co-operation, Directorate of Marketing & Inspection, 2009.

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27

A, Franklin Christine, ed. Statistical reasoning in sports. New York, NY: W. H. Freeman and Co. Publishers, 2011.

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28

E, Shipp Charles, and SAS Institute, eds. Quick results with SAS/GRAPH software. Cary, NC: SAS Institute, 1995.

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29

Christiansen, Stacy. Statistical Graphs. Oxford University Press, 2009. http://dx.doi.org/10.1093/jama/9780195176339.022.118.

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30

Gelman, Andrew, and Deborah Nolan. Statistical graphics. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198785699.003.0004.

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A statistical graph can offer an alternative compelling approach to teaching statistical thinking, but making good statistical graphs is hard to do. Each step in the process (e.g., change in scale, transform a variable, select colors, add a reference marker) engages students in better understanding data and models. However, this creative process is not easily encapsulated in a textbook. Since it is relatively easy to make a basic plot with statistical software, we can engage students in activities around making statistical graphs. This chapter provides guiding principles and lecture topics for teaching data visualization. The chapter contains exercises to deconstruct and reconstruct a plot, create a plot to reveal an important feature of the data, and turn a table of numbers into a plot.
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31

Marchette, David J. Random Graphs for Statistical Pattern Recognition. Wiley-Interscience, 2004.

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32

Marchette, David J. Random Graphs for Statistical Pattern Recognition. Wiley & Sons, Incorporated, John, 2008.

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33

Marchette, David J. Random Graphs for Statistical Pattern Recognition. Wiley & Sons, Incorporated, John, 2005.

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34

Manchester, Kathryn Elisebeth. Methods for the systematic evaluation of statistical graphs. 1985.

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35

Coolen, Ton, Alessia Annibale, and Ekaterina Roberts. Generating Random Networks and Graphs. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.001.0001.

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This book supports researchers who need to generate random networks, or who are interested in the theoretical study of random graphs. The coverage includes exponential random graphs (where the targeted probability of each network appearing in the ensemble is specified), growth algorithms (i.e. preferential attachment and the stub-joining configuration model), special constructions (e.g. geometric graphs and Watts Strogatz models) and graphs on structured spaces (e.g. multiplex networks). The presentation aims to be a complete starting point, including details of both theory and implementation, as well as discussions of the main strengths and weaknesses of each approach. It includes extensive references for readers wishing to go further. The material is carefully structured to be accessible to researchers from all disciplines while also containing rigorous mathematical analysis (largely based on the techniques of statistical mechanics) to support those wishing to further develop or implement the theory of random graph generation. This book is aimed at the graduate student or advanced undergraduate. It includes many worked examples, numerical simulations and exercises making it suitable for use in teaching. Explicit pseudocode algorithms are included to make the ideas easy to apply. Datasets are becoming increasingly large and network applications wider and more sophisticated. Testing hypotheses against properly specified control cases (null models) is at the heart of the ‘scientific method’. Knowledge on how to generate controlled and unbiased random graph ensembles is vital for anybody wishing to apply network science in their research.
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36

1951-, DURRETT RICK. Random Graph Dynamics (Cambridge Series in Statistical and Probabilistic Mathematics). Cambridge University Press, 2006.

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37

Peacock, Janet L., and Sally M. Kerry. Introduction to presenting statistical analyses. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780198599661.003.0005.

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Chapter 5 discusses presenting statistical analyses, and covers numerical data, results section, describing the results, assessing non-response bias, presenting results for different media, drawing up subject profiles, graphs and tables, and categorical and continuous data.
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38

Peacock, Janet L., Sally M. Kerry, and Raymond R. Balise. Introduction to presenting statistical analyses. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198779100.003.0005.

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Chapter 5 discusses presenting statistical analyses, and covers numerical data, results section, describing the results, assessing non-response bias, presenting results for different media, and drawing up subject profiles, graphs and tables, and categorical and continuous data. It shows how to present data that have been transformed for analysis. It contains helpful tips and information about reporting descriptive data clearly. The chapter includes analyses using Stata, SAS, SPSS, and R.
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39

Stata Statistics/Graphs/Data Management Release 6 (Volume 1 Reference A-G). Stata Corp., 1999.

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40

Zhang, Wenjun. Computational Ecology: Graphs, Networks and Agent-Based Modeling. World Scientific Publishing Co Pte Ltd, 2012.

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41

Bayesian Networks and Decision Graphs (Information Science and Statistics). Springer, 2007.

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42

Coolen, A. C. C., A. Annibale, and E. S. Roberts. Soft constraints: exponential random graph models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0004.

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Exponential random graph models (ERGMs) provide conceptually elegant recipes for generating soft-constrained random graphs. This chapter begins by explaining the theory and describing how to properly specify an ERGM, including demonstrating Lagrange’s method to derive the values of the model parameters that correspond to the desired constraints. Three ERGMs, all with constraints depending linearly on the adjacency matrix, are solved exactly: the targeted total number of links, targeted individual node degrees and targeted number of two-way links in a directed graph. However, when the controlled features become more complicated, ERGMs have a tendency to produce graphs in extreme phases (very dense or very sparse). The two-star model and the Strauss model are worked through in detail using advanced techniques from statistical mechanics in order to analyze the phase transitions. The chapter closes with a discussion of the strengths and weaknesses of ERGMs as null models.
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43

An Introduction to Exponential Random Graph Modeling. SAGE Publications, Inc, 2013.

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44

Buu, Anne, and Runze Li. New Statistical Methods Inspired by Data Collected from Alcohol and Substance Abuse Research. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190676001.003.0021.

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This chapter provides a nontechnical review of new statistical methodology for longitudinal data analysis that has been published in statistical journals in recent years. The methodology has applications in four important areas: (1) conducting variable selection among many highly correlated risk factors when the outcome measure is zero-inflated count; (2) characterizing developmental trajectories of symptomatology using regression splines; (3) modeling the longitudinal association between risk factors and substance use outcomes as time-varying effects; and (4) testing measurement reactivity and predictive validity using daily process data. The excellent statistical properties of the methods introduced have been supported by simulation studies. The applications in alcohol and substance abuse research have also been demonstrated by graphs on real longitudinal data.
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45

Selvin, Steve. The Joy of Statistics. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198833444.001.0001.

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The Joy of Statistics consists of a series of 42 “short stories,” each illustrating how elementary statistical methods are applied to data to produce insight and solutions to the questions data are collected to answer. The text contains brief histories of the evolution of statistical methods and a number of brief biographies of the most famous statisticians of the 20th century. Also throughout are a few statistical jokes, puzzles, and traditional stories. The level of the Joy of Statistics is elementary and explores a variety of statistical applications using graphs and plots, along with detailed and intuitive descriptions and occasionally using a bit of 10th grade mathematics. Examples of a few of the topics are gambling games such as roulette, blackjack, and lotteries as well as more serious subjects such as comparison of black/white infant mortality rates, coronary heart disease risk, and ethnic differences in Hodgkin’s disease. The statistical description of these methods and topics are accompanied by easy to understand explanations labeled “how it works.”
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46

Statistics on Weapons & Violence: A Selection of Statistical Charts, Graphs, and Tables About Weapons and Violence from a Variety of Published Sources ... Comments (Statistics for Students). Gale Group, 1995.

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47

Makela, Susanna, Yajuan Si, and Andrew Gelman. Graphical Visualization of Polling Results. Edited by Lonna Rae Atkeson and R. Michael Alvarez. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190213299.013.12.

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This chapter argues that it is wasteful to do a large, expensive poll and then just report a few percentages. Statistical modeling allows researchers to make the most effective use of available data, and graphs make it possible to convey more information more directly, both to general audiences and to specialists. Graphs are an invaluable tool at each step of the modeling process: exploring raw data, building and refining the model, and understanding and communicating the results are all made easier with graphs. In addition, graphical methods can be useful to survey researchers to understand weighting and other aspects of survey construction and analysis. The chapter includes several examples.
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48

Coolen, A. C. C., A. Annibale, and E. S. Roberts. Ensembles with hard constraints. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0005.

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This chapter introduces random graph ensembles involving hard constraints such as setting a fixed total number of links or fixed degree sequence, including properties of the partition function. It continues on from the previous chapter’s investigation of ensembles with soft-constrained numbers of two-stars (two-step paths) and soft-constrained total number of triangles, but now combined with a hard constraint on the total number of links. This illustrates phase transitions in a mixed-constrained ensemble – which in this case is shown to be a condensation transition, where the network becomes clumped. This is investigated in detail using techniques from statistical mechanics and also looking at the averaged eigenvalue spectrum of the ensemble. These phase transition phenomena have important implications for the design of graph generation algorithms. Although hard constraints can (by force) impose required values of observables, difficult-to-reconcile constraints can lead to graphs being generated with unexpected and unphysical overall topologies.
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49

Chance, Statistics & Graphs, Grades 3-5. World Teachers Press, 1996.

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50

L, Gall Timothy, Lucas Daniel M, Kratcoski Peter C. 1936-, and Kratcoski Lucille Dunn 1937-, eds. Statistics on weapons & violence: A selection of statistical charts, graphs, and tables about weapons and violence from a variety of published sources with explanatory comments. Detroit, MI: Gale Research, 1996.

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