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1

Patetta, Michael J. A descriptive analysis of housefire deaths in North Carolina. N.C. Dept. of Human Resources, Division of Health Services, State Center for Health Statistics, 1988.

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2

Patetta, Michael J. A descriptive analysis of housefire deaths in North Carolina. N.C. Dept. of Human Resources, Division of Health Services, State Center for Health Statistics, 1988.

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3

McManus, Rob. School violence in South Carolina, 1996-1998: An incident-based descriptive analysis. South Carolina Dept. of Public Safety, Office of Justice Programs, 2000.

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4

King, George D. Educating minority students in California: Descriptive analysis and policy implications. AOR, 1990.

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5

Kentucky Criminal Justice Statistical Analysis Center. The Kentucky SAC's crime estimation program: A description and preliminary analysis. The Center, 1987.

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6

Oort, Pepijn van. Spatial data quality: From description to application. NCG, Nederlandse Commissie voor Geodesie, 2005.

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7

Rosay, André. Descriptive analysis of sexual assaults in Anchorage, Alaska: Final report to the Bureau of Justice Statistics. Alaska Justice Statistical Analysis Center, Justice Center, University of Alaska Anchorage, 2003.

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8

Tsang, Andy M. Descriptive statistics tables from a detailed analysis of the National Human Activity Pattern Survey (NHAPS) data. National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, 1996.

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9

Randolph, KaDonna C. Descriptive statistics of tree crown condition in the southern United States and impacts on data analysis and interpretation. U.S. Dept. of Agriculture, Forest Service, Southern Research Station, 2006.

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10

Nesman, Edgar G. Baseline survey of the Honduran small farmer titling project: Descriptive analysis of the 1985 sample. Land Tenure Center, University of Wisconsin-Madison, 1987.

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11

Baker, Karen. Description and analysis of the payable 1990 property tax system: 1988 laws, chapter 719, article 5. Research Dept., Minnesota House of Representatives, 1989.

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12

Griffiths, James T. A descriptive summary of 1999-2000 bachelor's degree recipients 1 year later: With an analysis of time to degree. U.S. Dept. of Education, Institute of Education Sciences, 2003.

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13

Nevill, Stephanie. The path through graduate school: A longitudinal examination 10 years after bachelor's degree, postsecondary education descriptive analysis report. Dept. of Education, Institute of Education Sciences, National Center for Education Statistics, 2007.

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14

Foley, Kelly. Why stop after high school?: A descriptive analysis of the most important reasons that high school graduates do not continue to PSE. Canadian Millennium Scholarship Foundation, 2002.

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15

Horstman, Ronald G. Registration of the disabled in Zanzibar: A descriptive analysis of the 1988/1989 registration of the disabled in the Northern Region of Zanzibar. Faculty of Spatial Sciences, University of Groningen, 1991.

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16

Miksza, Peter, and Kenneth Elpus. Descriptive Analysis. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199391905.003.0004.

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Descriptive statistics allow researchers to use numbers to begin to tell the stories that exist in their data. This chapter presents an overview of the basic statistical tools researchers can use to summarize, display, and interpret data. The chapter presents guidelines for interpreting data and examples of typical sorts of data that music education researchers may gather. Statistical analyses suitable for identifying how data are distributed, determining typical values of a distribution, and describing how individuals differ on measured variables of interest are described. Approaches for grap
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17

Integrated Metaregression Framework For Descriptive Epidemiology. University of Washington Press, 2012.

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18

Tran, Thanh V., and Keith T. Chan. Applied Cross-Cultural Data Analysis for Social Work. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190888510.001.0001.

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Applied Cross-Cultural Data Analysis for Social Work is a research guide which provides a hands-on approach for learning and understanding data analysis techniques for examining and interpreting data for the purpose of cultural group comparisons. This book aims to provide practical applications in statistical approaches of data analyses that are commonly used in cross-cultural research and evaluation. Readers are presented with step-by-step illustrations in the use of descriptive, bivariate, and multivariate statistics to compare cross-cultural populations using large-scale, population-based s
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19

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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20

Alexander, Peter D. G., and Malachy O. Columb. Presentation and handling of data, descriptive and inferential statistics. Edited by Jonathan G. Hardman. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199642045.003.0028.

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The need for any doctor to comprehend, assimilate, analyse, and form an opinion on data cannot be overestimated. This chapter examines the presentation and handling of such data and its subsequent statistical analysis. It covers the organization and description of data, measures of central tendency such as mean, median, and mode, measures of dispersion (standard deviation), and the problems of missing data. Theoretical distributions, such as the Gaussian distribution, are examined and the possibility of data transformation discussed. Inferential statistics are used as a means of comparing grou
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21

Descriptive summary of 1989-90 beginning postsecondary students, two years later: Contractor report (Statistical analysis report / National Center for Education Statistics). For sale by the U.S. G.P.O., Supt. of Docs, 1994.

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22

Miksza, Peter, and Kenneth Elpus. Correlational Design and Analysis. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199391905.003.0006.

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Interests in how variables may relate to each other and how systems of relationships among variables may be at play often underlie the questions music education researchers pose. This chapter describes basic design and analysis considerations in research that involves the systematic investigation of whether and how variables are related; in other words, correlational research. The chapter poses correlational research as an extension of the book’s previous discussion of descriptive research. The chapter briefly describes the role of correlational studies in advancing theory, presents several is
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23

Life After College: A Descriptive Summary of 1992-93 Bachelor's Degree Recipients in 1997: With an Essay on Participation in Graduate and (Statistical Analysis Report / National Center for Education). U.S. Government Printing Office, 1999.

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24

Ferreira, Eliel Alves, and João Vicente Zamperion. Excel: Uma ferramenta estatística. Brazil Publishing, 2021. http://dx.doi.org/10.31012/978-65-5861-400-5.

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This study aims to present the concepts and methods of statistical analysis using the Excel software, in a simple way aiming at a greater ease of understanding of students, both undergraduate and graduate, from different areas of knowledge. In Excel, mainly Data Analysis Tools will be used. For a better understanding, there are, in this book, many practical examples applying these tools and their interpretations, which are of paramount importance. In the first chapter, it deals with introductory concepts, such as introduction to Excel, the importance of statistics, concepts and definitions. Be
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25

Halperin, Sandra, and Oliver Heath. 15. Quantitative Analysis. Oxford University Press, 2017. http://dx.doi.org/10.1093/hepl/9780198702740.003.0015.

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This chapter deals with quantitative analysis, and especially description and inference. It introduces the reader to the principles of quantitative research and offers a step-by-step guide on how to use and interpret a range of commonly used techniques. The first part of the chapter considers the building blocks of quantitative analysis, with particular emphasis on different ways of summarizing data, both graphically and with tables, and ways of describing the distribution of one variable using univariate statistics. Two important measures are discussed: the mean and the standard deviation. Af
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26

Auerbach, Charles, and Wendy Zeitlin. SSD for R. 2nd ed. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197582756.001.0001.

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Single-subject research designs have been used to build evidence to the effective treatment of problems across various disciplines, including social work, psychology, psychiatry, medicine, allied health fields, juvenile justice, and special education. This book serves as a guide for those desiring to conduct single-subject data analysis. The aim of this text is to introduce readers to the various functions available in SSD for R, a new, free, and innovative software package written in R, the robust open-source statistical programming language written by the book’s authors. SSD for R has the mo
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27

Multidimensional Nonlinear Descriptive Analysis. Chapman & Hall/CRC, 2006.

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28

Stefanowitsch, Anatol. Collostructional Analysis. Edited by Thomas Hoffmann and Graeme Trousdale. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780195396683.013.0016.

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This chapter discusses the concept of collostructional analysis, a family of quantitative corpus-linguistic methods that allow researchers to express the strength of the relationship between word constructions and the grammatical structures they occur in. It provides several case studies and shows how varying collostructional measures can enlighten constructionist analyses of lexical and grammatical constructions. The chapter explains that although adoption of collostructional analysis is a comparatively recent development in Construction Grammar, it has already been applied to a fairly wide r
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29

Goos, Peter, and David Meintrup. Statistics with JMP: Graphs, Descriptive Statistics and Probability. Wiley & Sons, Incorporated, John, 2015.

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30

Goos, Peter, and David Meintrup. Statistics with JMP: Graphs, Descriptive Statistics and Probability. Wiley & Sons, Incorporated, John, 2015.

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31

Goos, Peter, and David Meintrup. Statistics with JMP: Graphs, Descriptive Statistics and Probability. Wiley & Sons, Incorporated, John, 2016.

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32

STATISTICS WITH JMP: GRAPHS, DESCRIPTIVE STATISTICS AND PROBABILITY. JOHN WILEY, 2015.

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33

Goos, Peter, and David Meintrup. Statistics with JMP: Graphs, Descriptive Statistics and Probability. Wiley & Sons, Incorporated, John, 2015.

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34

Monge, Peter R., and Noshir Contractor. Theories of Communication Networks. Oxford University Press, 2003. http://dx.doi.org/10.1093/oso/9780195160369.001.0001.

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To date, most network research contains one or more of five major problems. First, it tends to be atheoretical, ignoring the various social theories that contain network implications. Second, it explores single levels of analysis rather than the multiple levels out of which most networks are comprised. Third, network analysis has employed very little the insights from contemporary complex systems analysis and computer simulations. Foruth, it typically uses descriptive rather than inferential statistics, thus robbing it of the ability to make claims about the larger universe of networks. Finall
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35

Society of Critical Care Medicine., ed. Critical care units: A descriptive analysis. Society of Critical Care Medicine, 2005.

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36

Cleary, Paul, Sam Ghebrehewet, and David Baxter. Essential statistics and epidemiology. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198745471.003.0022.

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This chapter provides a grounding in basic statistics, descriptive epidemiology, analytical epidemiology, and hypothesis testing appropriate for health protection practitioners. The analysis of categorical data using frequency distributions, and charts, and the interpretation of epidemic curves is described. The description of quantitative data including central tendency, standard deviation, and interquartile range is concisely explained. The role of geographical information systems and different disease map types is used to demonstrate how disease clusters may be detected. Determining possibl
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37

Gorman, Kyle, and Daniel Ezra Johnson. Quantitative Analysis. Edited by Robert Bayley, Richard Cameron, and Ceil Lucas. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199744084.013.0011.

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A sociolinguist who has gathered so much data that it has become difficult to make sense of the raw observations can turn to graphical presentation, and to descriptive statistics, techniques for distilling a collection of data into a few key numerical values, allowing the researcher to focus on specific, meaningful properties of the data set. A sociolinguist evaluates hypotheses about the connections between linguistic behavior, speakers, and society. The researcher begins this process by gathering data with the potential to falsify the hypotheses under consideration. Inferential statistics al
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38

Goodey, Thomas, Arjen Dijksman, Rene Verreault, and Maurice Allais. Experiments with the Paraconical Pendulum: Description and Results, with Statistical Analysis. Independently Published, 2018.

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39

Peacock, Janet L., and Sally M. Kerry. Introduction. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780198599661.003.0001.

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Chapter 1 gives and introduction and description of the resource in general, and describes the presentation of required statistical information through the entire research process, from the development of the research proposal, through to applying for ethical approval, to analysing the data, and then writing up the results. We use the term ‘statistical information’ to include describing the study design, the calculation of sample size, and data processing, as well as the data analysis and reporting of results. It is written for researchers in medicine and in the professions allied to medicine.
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40

Zbíral, Robert, ed. The Cradle of Laws. Nomos Verlagsgesellschaft mbH & Co. KG, 2020. http://dx.doi.org/10.5771/9783748905899.

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In almost all states, laws (statutes) serve as the most important instruments to prompt social, economic or institutional change. Parliaments traditionally used to be considered as the locus of law-making, yet observers of politics pointed out that it had rather been the government (executive) that affects the outputs of the legislative game more prominently. Statistical data reveal that in most cases the governmental bills submitted to parliaments are adopted unchanged. Despite that little attention has been aimed at the previous phase of the legislative process: drafting and negotiating of b
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41

Jolly, Elaine, Andrew Fry, and Afzal Chaudhry, eds. Epidemiology and evidence-based medicine. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199230457.003.0020.

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Chapter 20 focuses on epidemiology and evidence-based medicine. It covers study design, types of data and descriptive statistics, from samples to populations, relationships, relative risk, odds ratios, and 'number needed to treat', survival analysis, sample size, diagnostic tests, meta-analysis, before concluding with advice on how to read a paper.
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42

Petrelli, Maurizio. Introduction to Python in Earth Science Data Analysis: From Descriptive Statistics to Machine Learning. Springer International Publishing AG, 2022.

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43

Petrelli, Maurizio. Introduction to Python in Earth Science Data Analysis: From Descriptive Statistics to Machine Learning. Springer International Publishing AG, 2021.

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44

Field, Clive D. Counting Religion in Britain, 1970-2020. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192849328.001.0001.

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Counting Religion in Britain, 1970–2020, the fourth volume in the author’s chronological history of British secularization, sheds significant new light on the nature, scale, and timing of religious change in Britain during the past half-century, with particular reference to quantitative sources. Adopting a key performance indicators approach, twenty-one facets of personal religious belonging, behaving, and believing are examined, offering a much wider range of lenses through which the health of religion can be viewed and appraised than most contemporary scholarship (which is typically confined
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45

Bruce, Steve. Measuring Religion. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198786580.003.0004.

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Because the empirical study of contemporary religion is dominated by ethnographic case studies, this chapter argues for the importance of the statistical analysis of big data. It begins by arguing that description in words involves counting every bit as much as description in numbers. It explains different sorts of scales. It makes the case that counting the Godly is no more difficult than measuring any other sort of social characteristic and considers the relative merits of various ways of doing it. It details both the virtues and vices of quantitative analysis. Particular attention is given
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46

Rinaldi, Alberto, and Michelangelo Vasta. Financial Elites and the Italian Corporate Network, 1913–2001. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782797.003.0005.

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This chapter analyses the Italian corporate network along the twentieth century focusing on seven benchmark years (1913, 1927, 1936, 1960, 1972, 1983, and 2001). For each year, the top 250 companies (50 financial and 200 non-financial) by total assets have been selected. After showing some descriptive statistics, the chapter develops a network connectivity analysis of the system. This is integrated by a prosopographic study of the big linkers, defined as those directors who had the highest number of board positions in each year. The big linkers who held directorships in both banks and industri
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47

1984 population and housing census of Liberia: Summary results presented in graphics and descriptive analysis of some salient demographic characteristics. Ministry of Planning and Economic Affairs, 1987.

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48

Arkhipova, M. Yu, M. V. Kagirova, A. V. Ukolova, Yu N. Romantseva, A. E. Kharitonova, and V. V. Demichev. Analysis of the international practice of digitalization implementation in the agro-industrial complex of national and supranational economies, on the example of countries with traditionally developed agriculture. Publishing house of the Russian state agrarian University UN-TA im. K. A. Timiryazeva, 2021. http://dx.doi.org/10.26897/978-5-907477-35-3-2021-118.

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The analytical materials contain the results of studying the main trends in the development of agriculture, including the features of the use of digital technologies in the agricultural sector of countries with different levels of agricultural development and the degree of digitalization of the economy, a developed system of statistical indicators for a comprehensive description of the conditions, the process of results and the effectiveness of the use of digital technologies in agriculture, a system of indicators for monitoring these processes.
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49

A descriptive summary of 1999-2000 bachelor's degree recipient's 1 year later with an analysis of time to degree. National Center for Education Statistics, U.S. Dept. of Education, Institute of Education Sciences, 2003.

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50

Zabrodin, Anton. Financial applications of random matrix theory: a short review. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.40.

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This article reviews some applications of random matrix theory (RMT) in the context of financial markets and econometric models, with emphasis on various theoretical results (for example, the Marčenko-Pastur spectrum and its various generalizations, random singular value decomposition, free matrices, largest eigenvalue statistics) as well as some concrete applications to portfolio optimization and out-of-sample risk estimation. The discussion begins with an overview of principal component analysis (PCA) of the correlation matrix, followed by an analysis of return statistics and portfolio theor
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