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Titlebook: Recent Studies on Risk Analysis and Statistical Modeling; Teresa A. Oliveira,Christos P. Kitsos,Luís Grilo Book 2018 Springer Internationa

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書(shū)目名稱Recent Studies on Risk Analysis and Statistical Modeling
編輯Teresa A. Oliveira,Christos P. Kitsos,Luís Grilo
視頻videohttp://file.papertrans.cn/824/823358/823358.mp4
概述Presents the latest advances in the field of risk analysis.Illustrates the fascinating interactions that have developed between the statistical and biomedical sciences.Describes a diverse range of app
叢書(shū)名稱Contributions to Statistics
圖書(shū)封面Titlebook: Recent Studies on Risk Analysis and Statistical Modeling;  Teresa A. Oliveira,Christos P. Kitsos,Luís Grilo Book 2018 Springer Internationa
描述.This book provides an overview of the latest developments in the field of risk analysis (RA). Statistical methodologies have long-since been employed as crucial decision support tools in RA. Thus, in the context of this new century, characterized by a variety of daily risks - from security to health risks - the importance of exploring theoretical and applied issues connecting RA and statistical modeling (SM) is self-evident. In addition to discussing the latest methodological advances in these areas, the book explores applications in a broad range of settings, such as medicine, biology, insurance, pharmacology and agriculture, while also fostering applications in newly emerging areas. This book is intended for graduate students as well as quantitative researchers in the area of RA..
出版日期Book 2018
關(guān)鍵詞Risk analysis; Statistical distributions; Health risk assessment; Risk quantification; Extreme value the
版次1
doihttps://doi.org/10.1007/978-3-319-76605-8
isbn_softcover978-3-030-09532-1
isbn_ebook978-3-319-76605-8Series ISSN 1431-1968 Series E-ISSN 2628-8966
issn_series 1431-1968
copyrightSpringer International Publishing AG, part of Springer Nature 2018
The information of publication is updating

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Daniel Deidda,Robert G. Aykroyd,Charalampos Tsoumpasation mean for the data using either the 95% confidence interval about the mean (Chapter 3 of this book) or the one-group t-test of the mean (Chapter 4 of this book). You have also learned how to test for the difference between the means for two groups of to determine if this difference was a “signi
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Milan Stehlík,Philipp Hermann,Stefan Giebel,Jens-Peter Schenkation mean for the data using either the 95% confidence interval about the mean (Chap. 3 of this book) or the one-group t-test of the mean (Chap. 4 of this book). You have also learned how to test for the difference between the means for two groups of people to determine if this difference was a “si
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Izabela Pruchnicka-Grabiashis test compares the mean of your data set against the hypothesized population mean for your data to determine if the difference between these two values is “l(fā)arge enough” to be considered a “significant difference.” The formula is presented, explained, and a practical example is given using your c
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Marta Ferreirang . to predict Y instead of a single predictor as we discussed in Chap. 6 of this book. The resulting statistical procedure is called “multiple correlation” because it uses two or more predictors, each weighed differently in an equation, to predict Y. The job of multiple correlation is to determine
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Víctor Leiva,Camilo Lillo,Rodrigo Morrásresearch study and only one measurement (i.e., variable) “number” on each of these people. This chapter asks you to change gears again and to deal with the situation in which you are measuring two variables instead of only one variable and you are trying to discover the “relationship” between these
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