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Titlebook: Topics in Biostatistics; Walter T. Ambrosius Book 2007 Humana Press 2007 Radiologieinformationssystem.bioinformatics.biostatistics.cancer.

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发表于 2025-3-21 19:26:53 | 显示全部楼层 |阅读模式
书目名称Topics in Biostatistics
编辑Walter T. Ambrosius
视频video
概述Provides a framework for choosing appropriate biostatistical methods.Introduces advanced methods in statistics, including how to choose and work with statistical packages.Includes supplementary materi
丛书名称Methods in Molecular Biology
图书封面Titlebook: Topics in Biostatistics;  Walter T. Ambrosius Book 2007 Humana Press 2007 Radiologieinformationssystem.bioinformatics.biostatistics.cancer.
描述.Basic Biostatistics presents a multidisciplinary survey of biostatics methods, each illustrated with hands-on examples. Methods range from the elementary, including descriptive statistics, study design, statistical interference, categorical variables, evaluation of diagnostic tests, comparison of means, linear regression, and logistic regression. These introductory methods create a portfolio of biostatistical techniques for both novice and expert researchers. More complicated statistical methods are introduced as well, including those requiring either collaboration with a biostatistician or the use of a statistical package. Specific topics of interest include microarray analysis, missing data techniques, power and sample size, statistical methods in genetics. Expert advice is given on when to seek statistical help, and how to conduct a meeting with the statistical collaborator or consultant. Basic Biostatistics is an essential resource for researchers at every level of their career..
出版日期Book 2007
关键词Radiologieinformationssystem; bioinformatics; biostatistics; cancer; classification; descriptive statisti
版次1
doihttps://doi.org/10.1007/978-1-59745-530-5
isbn_softcover978-1-61737-623-8
isbn_ebook978-1-59745-530-5Series ISSN 1064-3745 Series E-ISSN 1940-6029
issn_series 1064-3745
copyrightHumana Press 2007
The information of publication is updating

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Observational Study Design,troduces the major types of observational study designs: the longitudinal or cohort study, the comparative or case-control study, and some of their variants. It also includes examples of the key measures of relationship between factor and outcome in observational studies, the relative risk and the o
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Statistical Inference on Categorical Variables,pter, we first describe types of categorical data (nominal and ordinal) and how these types of data are distributed (binomial, multinomial, and independent multinomial). Next, methods for estimation and making statistical inferences for categorical data in commonly seen situations are presented. Thi
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Correlation and Simple Linear Regression, continuous variables with each other. These steps include estimation and inference, assessing model fit, the connection between regression and ANOVA, and study design. Examples in microbiology are used throughout. This chapter provides a framework that is helpful in understanding more complex stati
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Multiple Linear Regression, continuous outcome. Important steps in using this approach include estimation and inference, variable selection in model building, and assessing model fit. The special cases of regression with interactions among the variables, polynomial regression, regressions with categorical (grouping) variables
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Linear Mixed Effects Models, requires balancing adequate representation of the process with simplicity. Experiments involving multiple (correlated) observations per subject do not satisfy the assumption of independence required for most methods described in previous chapters. In some experiments, the amount of random variation
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