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Multivariate Data Analysis of Proteome Data
2006We present the background for multivariate data analysis on proteomics data with a hands-on section on how to transfer data between different software packages. The techniques can also be used for other biological and biochemical problems in which structures have to be found in a large amount of data. Digitalization of the 2D gels, analysis using image
Kåre, Engkilde +2 more
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Multivariate Analysis of Incomplete Mapped Data
Transactions in GIS, 2003AbstractClassical multivariate analyses are based on matrix algebra and enable the analysis of a table containing measurements of a set of variables for a set of sites. Incomplete mapped data consist of measurements of a set of variables recorded for the same geographical region but for different zonal systems and with only a partial sampling of this ...
Stéphane Dray +2 more
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Multivariate data analysis of multivariate populations
Chemometrics and Intelligent Laboratory Systems, 2007For data that can be arranged in populations, multivariate data analysis of digitalized distributions is suggested as an alternative method to detect variations. This approach includes a representation of the data that avoids information destroying pre-processing such as averaging.
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Multivariate Data and Multivariate Analysis
2005Multivariate data arise when researchers record the values of several random variables on a number of subjects or objects or perhaps one of a variety of other things (we will use the general term “units”) in which they are interested, leading to a vector-valued or multidimensional observation for each.
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Multivariate Data Analysis [PDF]
, 2012 The study of biology, computer science, and information technology all combine to form the science of bioinformatics [1]. This field was created to make discoveries on new biological insights [1]. Bioinformatics has several important task one of them being able to analyze and interpret different types of data and this includes multivariate data ...
Copeland, Diana, Raymer, Michael
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Multivariate temporal data analysis ‐ a review
WIREs Data Mining and Knowledge Discovery, 2021ABSTRACTThe information technology revolution, especially with the adoption of the Internet of Things, longitudinal data in many domains become more available and accessible for secondary analysis. Such data provide meaningful opportunities to understand process in many domains along time, but also challenges.
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2016
This short chapter shows how the statistical properties of higher-dimensional data can be visualized: with 3D-surfaces, Scatterplot Matrices, and Correlation Matrices.
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This short chapter shows how the statistical properties of higher-dimensional data can be visualized: with 3D-surfaces, Scatterplot Matrices, and Correlation Matrices.
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2000
In our hypotheses, we stated that the various dimensions of internationalisation are expected to be a function of firm size, international experience of the founders, external fmance, technology intensity, innovativeness, the extent to which products are customised and the costs of commercialisation.
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In our hypotheses, we stated that the various dimensions of internationalisation are expected to be a function of firm size, international experience of the founders, external fmance, technology intensity, innovativeness, the extent to which products are customised and the costs of commercialisation.
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Different chapters within this book and the literature prove the ToF-SIMS as a powerful surface analysis tool. However, one large drawback is that data analysis can be challenging and is very time-consuming compared to the measurement time and to other analysis techniques. Often only a small subset of data is analysed, e.g.
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Multivariate data analysis for outcome studies
American Journal of Community Psychology, 1981The use of multivariate statistical techniques for analyzing the complex data often gathered in outcome studies is discussed. The multivariate analysis of variance (MANOVA) is suggested for multiple group studies common to outcome studies. This technique can be utilized for a large number of specific research designs whenever multiple outcome measures ...
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