scholarly journals Application of multivariate statistical methods in the assessment of mountain organic soil transformation in the central Sudetes

2017 ◽  
Vol 54 (1) ◽  
pp. 43-59
Author(s):  
Bogna Zawieja ◽  
Bartłomiej Glina
Keyword(s):  
Cluster Analysis ◽  
Statistical Methods ◽  
Science Research ◽  
Organic Soil ◽  
Soil Science ◽  
Organic Soils ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Discriminant Variable ◽  
And Cluster Analysis

Summary In studies of organic soil degradation and transformation, alongside the conventional methods used in soil science, an increase in the importance of advanced statistical methods can be observed. In this study some multivariate statistical methods were applied in an investigation of organic soil transformation in the central Sudetes. Andrews curves, linear and kernel discriminant variable analysis and cluster analysis were used. The similarities among peatland soils and their layers were determined. It can be stated that the application of statistical methods in soil science research related to organic soil transformation is a valuable tool. The use of various statistical methods (such as Andrews curves, linear and kernel discriminant variables and cluster analysis) can with high probability confirm earlier laboratory or field observations. This is particularly justified in the case of organic soils derived from varied geobotanical peat materials, different types of peatlands and water supply types, which impact the primary properties of the soil.

scholarly journals Evaluation of spatial differentiation in the Pilsen region from a socioeconomic perspective

2016 ◽  
Vol 34 (34) ◽  
pp. 73-90 ◽  
Author(s):  
Marie Novotná ◽  
Marta Šlehoferová ◽  
Alena Matušková
Keyword(s):  
Cluster Analysis ◽  
Czech Republic ◽  
Statistical Methods ◽  
Spatial Differentiation ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
The Czech Republic ◽  
Voronoi Map ◽  
The Given ◽  
Statistical Indicators

AbstractThe main objective of this article is to evaluate spatial differentiation in the Pilsen region in the Czech Republic, to create a typology of territorial units, and to evaluate the potential for development and possible threats to development in relation to individual territorial types. To this end, municipal statistical indicators pertaining to population, employment, and economy, were gathered from each of the given territories. The Voronoi map technique was applied to interpolate the values of selected indicators. The typology was created using one of the multivariate statistical methods, namely, the cluster analysis. Furthermore, typological regions and strategies for their development were created.

Karyotype and seed protein analyses of Lycium(Solanaceae) in Iran

1999 ◽  
Vol 56 (2) ◽  
pp. 253-264 ◽  
Author(s):  
M. Sheidai ◽  
Z. Narengi ◽  
M. Khatamsaz
Keyword(s):  
Cluster Analysis ◽  
Statistical Methods ◽  
Chromosome Numbers ◽  
Close Relationships ◽  
Seed Protein ◽  
Storage Proteins ◽  
Seed Storage ◽  
Seed Storage Proteins ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical

Twelve populations of six Lycium species were analysed for karyotypie characters and seed storage proteins using multivariate statistical methods. All reported chromosome numbers (mostly new) are 2n = 24 (x = 12). Karyotypes were symmetrical and placed in 1A and 2A classes of Stebbins karyotype classification. Cluster analysis of karyological and protein data revealed variations among the populations of L. depressum and L. ruthenicum, and supports close relationships of L. kopetdaghi and L. depressum with L. makranicum, and L. shawii with L. edgeworthii.

scholarly journals European Jurassic floras: statistics and palaeoenvironmental proxies

2014 ◽  
Vol 54 (2) ◽  
pp. 173-195 ◽  
Author(s):  
Maria Barbacka ◽  
Emese Bodor ◽  
Agata Jarzynka ◽  
Evelyn Kustatscher ◽  
Grzegorz Pacyna ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
United Kingdom ◽  
Environmental Factors ◽  
Statistical Methods ◽  
Species Level ◽  
Statistical Analyses ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
The United Kingdom ◽  
Plant Groups

Abstract The Jurassic floras of Europe show considerable diversity. To examine the extent of this diversity and its possible causes we used multivariate statistical methods (cluster analysis, PCA, NMDS) to compare all significant Jurassic floras in Europe. Data were based on 770 taxa from 46 fossiliferous occurrences (25 units) from France, Germany, Greenland, Hungary, Italy, Norway, Poland, Romania, Scotland, Serbia, Sweden, Switzerland, and the United Kingdom. Statistical analyses were applied at species level and genus level, and also performed for the major plant groups. The genus cladograms show affinities between different localities based on environmental factors, while the cladograms based on species affinities indicate only taxonomical correlations. The study shows that locality age does not seem to be of paramount importance for floral composition.

Ecological distribution of moss-dwelling tardigrades on Vancouver Island, British Columbia, Canada

1991 ◽  
Vol 69 (1) ◽  
pp. 122-129 ◽  
Author(s):  
R. Deedee Kathman ◽  
Stephen F. Cross
Keyword(s):  
British Columbia ◽  
Cluster Analysis ◽  
Vancouver Island ◽  
Ecological Distribution ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Moss Species ◽  
Components Analysis ◽  
And Cluster Analysis ◽  
The Relationship

Replicate samples of tardigrades were collected at six altitudes from five mountains on Vancouver Island, British Columbia, Canada, to determine the relationship between species of tardigrades and altitude, and between species of tardigrades and species of mosses in which they were collected. A total of 13 696 tardigrades representing 39 species were collected and identified. Thirty-seven species of mosses were identified. Data were analyzed using principal components analysis and cluster analysis. The results from both multivariate statistical methods indicated that the distribution and abundance of tardigrades were not dependent upon the altitude or moss species.

scholarly journals The usefulness of robust multivariate methods: A case study with the menu items of a fast food restaurant chain

2020 ◽  
Vol 42 ◽  
pp. e17
Author(s):  
Paulo Jorge Canas Rodrigues ◽  
Rafael Almeida ◽  
Kézia Mustafa
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Statistical Methods ◽  
Fast Food ◽  
Principal Component ◽  
Component Analysis ◽  
Fast Food Restaurant ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Robust Statistical Methods

Multivariate statistical methods have been playing an important role in statistics and data analysis for a very long time. Nowadays, with the increase in the amounts of data collected every day in many disciplines, and with the raise of data science, machine learning and applied statistics, that role is even more important. Two of the most widely used multivariate statistical methods are cluster analysis and principal component analysis. These, similarly to many other models and algorithms, are adequate when the data satisfies certain assumptions. However, when the distribution of the data is not normal and/or it shows heavy tails and outlying observations, the classic models and algorithms might produce erroneous conclusions. Robust statistical methods such as algorithms for robust cluster analysis and for robust principal component analysis are of great usefulness when analyzing contaminated data with outlying observations. In this paper we consider a data set containing the products available in a fast food restaurant chain together with their respective nutritional information, and discuss the usefulness of robust statistical methods for classification, clustering and data visualization.

Null se constructions in Brazilian and European Portuguese: morphosyntactic deletion or emergence of new constructions?

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Augusto Soares da Silva ◽  
Susana Afonso ◽  
Dafne Palú ◽  
Karlien Franco
Keyword(s):  
Statistical Methods ◽  
External Factors ◽  
Important Predictor ◽  
Cognitive Grammar ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Passive Construction ◽  
European Portuguese ◽  
Change Of State ◽  
The Moment

Abstract Se constructions designate a set of polysemous constructions along a transitivity continuum marked by the clitic se that perform various functions: reflexive/reciprocal, middle, anticausative, passive, and impersonal. A counterpart of these constructions without the clitic – the null se construction – is also attested. Based on an extensive usage-feature and profile-based analysis, and using multivariate statistical methods, we analyze, considering Cognitive Grammar, the conceptual, structural, and lectal factors that determine the choice between overt and null se constructions. The results of the study show that the null constructions are far more frequent in Brazilian (BP) than in European Portuguese (EP). In BP, the focus on the moment of change is a crucial factor for the overt/null variation in reflexive/reciprocal, middle, anticausative, and impersonal constructions. If the moment of the change of state is profiled, the overt se construction is usually produced. If the moment of change is not profiled, the null se construction is preferred. External factors also play a role in the variation. Register is an important predictor for the observed variation of the anticausative construction, and the only predictor for the overt/null variation in the case of the passive construction. In EP, the null se variant is mainly limited to anticausative constructions. In all cases of null constructions, there is a shift to an absolute construal, which has an impact on the way that the transitivity continuum is conceptualized.

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