scholarly journals Statistical Study of Variability in Rainfall and Analysis of Etreme Rainfall Events for Hill Stations of Uttarakhand

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Bimal Pande ◽  
Sneh Joshi ◽  
Seema Pande
Keyword(s):  
Principal Component Analysis ◽  
Statistical Analysis ◽  
Statistical Study ◽  
Parametric Method ◽  
Principal Component ◽  
Extreme Rainfall ◽  
Component Analysis ◽  
Rainfall Pattern ◽  
Composite Matrix ◽  
Rainfall Events

Statistical analysis of rainfall pattern and its variability for 20 years (1990-2010) data is performed for two mountainous urban centres of Uttarakhand i.e. Almora (29.60 N, 79.670 E and altitude 1,204m asl) and Nainital (29.40 N, 79.470 E and altitude 2,020m asl). Non Parametric method of Principal Component Analysis (PCA) gives the correlation between different extreme rainfall indices. It is concluded that PCA suggest 90% of the variance in composite matrix of extreme rainfall indices.

scholarly journals Statistical Study of Variability in Rainfall and Analysis of Etreme Rainfall Events for Hill Stations of Uttarakhand

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Bimal Pande ◽  
Sneh Joshi ◽  
Seema Pande
Keyword(s):  
Principal Component Analysis ◽  
Statistical Analysis ◽  
Statistical Study ◽  
Parametric Method ◽  
Principal Component ◽  
Extreme Rainfall ◽  
Component Analysis ◽  
Rainfall Pattern ◽  
Composite Matrix ◽  
Rainfall Events

Statistical analysis of rainfall pattern and its variability for 20 years (1990-2010) data is performed for two mountainous urban centres of Uttarakhand i.e. Almora (29.60 N, 79.670 E and altitude 1,204m asl) and Nainital (29.40 N, 79.470 E and altitude 2,020m asl). Non Parametric method of Principal Component Analysis (PCA) gives the correlation between different extreme rainfall indices. It is concluded that PCA suggest 90% of the variance in composite matrix of extreme rainfall indices.

Component Analysis: An Aid to the Interpretation of Data

1969 ◽  
Vol 5 (2) ◽  
pp. 151-164 ◽  
Author(s):  
D. A. Holland
Keyword(s):  
Principal Component Analysis ◽  
Statistical Study ◽  
Principal Component ◽  
Component Analysis ◽  
Linear Functions ◽  
Analysis Approach ◽  
Specific Technique ◽  
Mathematical Technique ◽  
Biological Concepts ◽  
General Component

SummaryPrincipal component analysis is a mathematical technique for summarizing a set of related measurements as a set of derived variates, frequently fewer in number, which are definable as independent linear functions of the original measurements. Consideration of their mathematical nature shows that they do not, themselves, necessarily correspond to sensible biological concepts, though they are more amenable to statistical study than the original measurements. Further, by assessing the extent to which they are in accordance with biological hypotheses, or with the results of other, similar, analyses, they can be transformed into other linear functions which are meaningful in the biological sense, or consistent with other results. Thus the specific technique of principal component analysis is developed into a more general component analysis approach. With proper regard for the purpose the analysis is intended to serve and for the mathematical restrictions involved, this approach can lead to a useful condensation of a mass of data, a better under-standing of the observed individuals as entities rather than collections of isolated measurements, and to the formulation of new hypotheses for subsequent examination.

scholarly journals Statistical Analysis of Mitochondrial Pathologies in Childhood: Identification of Deficiencies Using Principal Component Analysis

2000 ◽  
Vol 80 (7) ◽  
pp. 1019-1030 ◽  
Author(s):  
Thierry Letellier ◽  
Gilles Durrieu ◽  
Monique Malgat ◽  
Rodrigue Rossignol ◽  
Jaromir Antoch ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Statistical Analysis ◽  
Principal Component ◽  
Component Analysis

scholarly journals Principal Component Analysis of Multiwavelength Properties of Seyfert Galaxies

1994 ◽  
Vol 159 ◽  
pp. 502-502
Author(s):  
Deborah Dultzin–Hacyan ◽  
Carlos Ruano
Keyword(s):  
Principal Component Analysis ◽  
Statistical Analysis ◽  
Principal Component ◽  
Seyfert Galaxies ◽  
Component Analysis ◽  
X Ray ◽  
Radio Data

A multidimensional statistical analysis of observed properties of Seyfert galaxies has been carried out using Principal Component Analysis (PCA) applied to X-ray, optical, near and far IR and radio data for all the Seyfert galaxies types 1 and 2 for the catalog by Lipovtsky et al. (1987).

scholarly journals Chemical Composition and Variability of Leaf and Berry oils from Corsican Juniperus macrocarpa

2018 ◽  
Vol 13 (2) ◽  
pp. 1934578X1801300
Author(s):  
Joséphine Ottavioli ◽  
Ange Bighelli ◽  
Joseph Casanova ◽  
Félix Tomi
Keyword(s):  
Principal Component Analysis ◽  
Statistical Analysis ◽  
Chemical Composition ◽  
Principal Component ◽  
Component Analysis ◽  
Germacrene D ◽  
Leaf Oil ◽  
Main Components ◽  
Main Component ◽  
C Nmr

The chemical composition of five leaf oil samples and eighteen berry oil samples from Corsican Juniperus macrocarpa have been investigated by GC(RI), GC-MS and 13C NMR. The composition of berry oils was dominated by monoterpene hydrocarbons with α-pinene (56.4-78.9%) as main component followed by myrcene (2.2-11.9%). Germacrene D (4.5-103%) was the major sesquiterpene. The contents of the main components of leaf oils varied drastically from sample to sample: α-pinene (28.7-76.4%), δ3-carene (up to 17.3%), β-phellandrene (up to 12.3%), manoyl oxide (up to 8.1%). The occurrence of the unusual ( Z)-pentadec-6-en-2-one (0.1-1.2%) should be pointed out. Statistical analysis (Principal Component Analysis and k- means partition) suggested a unique group with atypical samples.

scholarly journals The Principal Component Analysis of Tribological Tests of Surface Layers Modified with IF-WS2 Nanoparticles

2015 ◽  
Vol 235 ◽  
pp. 9-15
Author(s):  
Jacek Pietraszek ◽  
Joanna Korzekwa ◽  
Andrii Goroshko
Keyword(s):  
Principal Component Analysis ◽  
Inverse Problem ◽  
Surface Layer ◽  
Statistical Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Technological Parameters ◽  
Surface Layers ◽  
Hyper Plane

The investigation described in this paper resulted in some complicated statistical analysis. The first level was an experimental design with technological parameters as factorials input and geometrical surface layer properties as quantitative outputs. The second level was an analysis generally leading to an optimization inverse problem: what parameters result in desired surface layer properties. The principal component analysis was made to identify possibility of a dimensionality reduction and simplify the optimization. Obtained results showed that the experimental dataset is practically two-dimensional but PCA projection involves all factors into the skewed hyper-plane. This paper contains a description of the problem, obtained results, analysis and conclusions.

Kansei Quantitative Analysis and its Application to Drum Washing Machine Apperence Design

2013 ◽  
Vol 401-403 ◽  
pp. 193-196 ◽  
Author(s):  
Sai He ◽  
Jian Ming Che
Keyword(s):  
Principal Component Analysis ◽  
Statistical Analysis ◽  
Quantitative Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Method ◽  
Kansei Engineering ◽  
Washing Machine ◽  
Design Elements ◽  
Statistical Analysis Method

Kansei Engineering (KE) refers to the translation of consumers' emotional requirements about a product into perceptual design elements. The Kansei Engineering is applied to the drum washing machine aided by a variety of engineering mean.Semantic differential (SD) is applied to extract the kansei tags.Multivariate statistical analysis method is also used for data mining.The data of design elements is processed by principal component analysis (PCA) and SPSS.

The Principal Component Analysis of Coastal Areas

2014 ◽  
Vol 513-517 ◽  
pp. 3703-3706
Author(s):  
Qiu Ju Wang ◽  
Da Shen Xue
Keyword(s):  
Principal Component Analysis ◽  
Statistical Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Coastal Areas ◽  
Principal Component Analysis Analysis ◽  
Consumer Prices ◽  
The Government ◽  
Main Component ◽  
The Impact

In order to development the economic of China's coastal areas better, the paper mainly discusses the coastal areas of China's consumer price factors, the main use of software SPSS, using statistical analysis, principal component analysis, analysis that the impact is the main component of consumer prices and reached the level of consumer prices in coastal areas, which is conducive to the Government to take appropriate measures to faster and better development of the region's economy.

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