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Titlebook: Statistical Analysis of Clinical Data on a Pocket Calculator, Part 2; Statistics on a Pock Ton J. Cleophas,Aeilko H. Zwinderman Book 2012 T

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发表于 2025-3-21 19:46:48 | 显示全部楼层 |阅读模式
书目名称Statistical Analysis of Clinical Data on a Pocket Calculator, Part 2
副标题Statistics on a Pock
编辑Ton J. Cleophas,Aeilko H. Zwinderman
视频videohttp://file.papertrans.cn/877/876327/876327.mp4
概述Methods that are easy on a pocket calculator and difficult on a software program are reviewed.You better understand what you‘re doing.Methodologies for which there is little software are reviewed.Pock
丛书名称SpringerBriefs in Statistics
图书封面Titlebook: Statistical Analysis of Clinical Data on a Pocket Calculator, Part 2; Statistics on a Pock Ton J. Cleophas,Aeilko H. Zwinderman Book 2012 T
描述.The first part of this title contained all statistical tests relevant to starting clinical investigations, and included tests for continuous and binary data, power, sample size, multiple testing, variability, confounding, interaction, and reliability. The current part 2 of this title reviews methods for handling missing data, manipulated data, multiple confounders, predictions beyond observation, uncertainty of diagnostic tests, and the problems of outliers. Also robust tests, non-linear modeling , goodness of fit testing, Bhatacharya models, item response modeling, superiority testing, variability testing, binary partitioning for CART (classification and regression tree) methods, meta-analysis, and simple tests for incident analysis and unexpected observations at the workplace and reviewed. .Each test method is reported together with (1) a data example from practice, (2) all steps to be taken using a scientific pocket calculator, and (3) the main results and their interpretation. Although several of the described methods can also be carried out with the help of statistical software, the latter procedure will be considerably slower. .Both part 1 and 2 of this title consist of a mi
出版日期Book 2012
关键词Clinical data analysis; Computer science; Data management; Data mining; Machine learning
版次1
doihttps://doi.org/10.1007/978-94-007-4704-3
isbn_softcover978-94-007-4703-6
isbn_ebook978-94-007-4704-3Series ISSN 2191-544X Series E-ISSN 2191-5458
issn_series 2191-544X
copyrightThe Author(s) 2012
The information of publication is updating

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Bhattacharya Modeling for Unmasking Hidden Gaussian Curves,Bhattacharya modeling can be used for unmasking Gaussian curves in the data. It is applied for determining normal values of diagnostic tests and their confidence intervals, and for searching subsets in the data.
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Binary Partitioning for CART (Classification and Regression Tree) Methods,Binary partitioning is used to determine the best fit decision cut-off levels for a dataset with false positive and false negative patients. It serves a purpose similar to that of the receiver operating characteristic (ROC) curve method, but, unlike ROC curves, it is adjusted for the magnitude of the samples, and therefore more precise.
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,Physicians’ Daily Life and the Scientific Method,We assumed the numbers of unanswered questions in the physicians’ daily life would be large. But just to get impression, one of the authors of this chapter (TC) recorded all of the unanswered answers he asked himself during a single busy day. Excluding the questions with uncertain but generally accepted answers, he included 9 questions.
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Statistical Analysis of Clinical Data on a Pocket Calculator, Part 2978-94-007-4704-3Series ISSN 2191-544X Series E-ISSN 2191-5458
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