Results 161 to 170 of about 14,515,084 (225)
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Nursing Management, 2016
Anyone interested in health policy development from before the birth of the NHS until now might find the Health Foundation’s new website Policy Navigator interesting. The aim is to help policymakers, and other interested parties, better understand the development of the NHS, and the complexity involved in the provision of health care, through charting ...
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Anyone interested in health policy development from before the birth of the NHS until now might find the Health Foundation’s new website Policy Navigator interesting. The aim is to help policymakers, and other interested parties, better understand the development of the NHS, and the complexity involved in the provision of health care, through charting ...
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Statistical Analysis of Experimental Data
2012While an increasing number of observational studies in modern political science use quite sophisticated statistical methods, experimental studies often continue to apply rather simple statistical instruments like t-tests or analysis of variance (ANOVA).
Shikano, Susumu +2 more
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Statistical Analysis of Microarray Data
Addiction Biology, 2005Microarrays promise dynamic snapshots of cell activity, but microarray results are unfortunately not straightforward to interpret. This article aims to distill the most useful practical results from the vast body of literature available on microarray data analysis.
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Statistical Analysis of Metabolomics Data
2013Statistical matters form an integral part of a metabolomics experiment. In this chapter we describe several important aspects in the analysis of metabolomics data such as the removal of unwanted variation and the identification of differentially abundant metabolites, along with a number of other essential statistical considerations.
Alysha M, De Livera +2 more
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2021
Statistical thinking is a large part of how we approach the analysis of data. A range of simple descriptive statistics is often sufficient for us to acquire an improved understanding of our data. Looking at features such as missing values, the mean, the range of the data, the presence of outliers and the shape of a distribution can tell us a lot about ...
Richard Hill, Stuart Berry
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Statistical thinking is a large part of how we approach the analysis of data. A range of simple descriptive statistics is often sufficient for us to acquire an improved understanding of our data. Looking at features such as missing values, the mean, the range of the data, the presence of outliers and the shape of a distribution can tell us a lot about ...
Richard Hill, Stuart Berry
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Statistical Analysis of Discrete Relational Data
SSRN Electronic Journal, 1986Social interaction data record the intensity of the relationship, or frequency of interaction, between two individual actors. Recent methods for analysing such data have treated these relational variables as continuous. A more appropriate method, described here, views these dyadic interactions as variables in multidimensional discrete cross‐classified ...
Wasserman, Stanley, Iacobucci, Dawn
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Statistical Analysis of Genomic Tag Data
Statistical Applications in Genetics and Molecular Biology, 2004We present a series of statistical solutions to challenges that commonly arise in the production and analysis of genomic tag libraries. Tag libraries are collections of fragments of DNA or RNA, with each unique fragment often present in millions or billions of copies. Inferences can be made from data obtained by sequencing a subset of the library.
LaFramboise, Thomas L. +2 more
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1998
Abstract This book is a guide to the practical application of statistics in data analysis as typically encountered in the physical sciences. It is primarily addressed at students and professionals who need to draw quantitative conclusions from experimental data.
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Abstract This book is a guide to the practical application of statistics in data analysis as typically encountered in the physical sciences. It is primarily addressed at students and professionals who need to draw quantitative conclusions from experimental data.
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Probability, Statistics, and Reliability for Engineers and Scientists
Introduction Introduction Knowledge, Information, and Opinions Ignorance and Uncertainty Aleatory and Epistemic Uncertainties in System Abstraction Characterizing and Modeling Uncertainty Simulation for Uncertainty Analysis and Propagation Simulation ...
B. Ayyub, R. McCuen
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1983
Graphical methods by P. A. Tukey Robust methods by C. L. Mallows Multilinear methods by J. B. Kruskal A case study in data analysis by J. R. Kettenring.
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Graphical methods by P. A. Tukey Robust methods by C. L. Mallows Multilinear methods by J. B. Kruskal A case study in data analysis by J. R. Kettenring.
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