scholarly journals Evaluation of disparities in living standards of regions of the Czech Republic

2012 ◽  
Vol 60 (4) ◽  
pp. 407-414
Author(s):  
Hana Vostrá Vydrová ◽  
Zuzana Novotná
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Czech Republic ◽  
Analysis Of Variance ◽  
Risk Groups ◽  
Environmental Variable ◽  
Living Standards ◽  
Standard Of Living ◽  
Multivariate Statistical ◽  
The Czech Republic

This paper focuses on regional differences between the regions of the Czech Republic. We will focus on observation of inequalities between indicators of living in different regions of the Czech Republic. The indicators are evaluated at NUTS 3 (regions), using multivariate statistical techniques - factor analysis and cluster analysis. We have identified the twelve indicators of living standards. Base data was reduced using factor analysis on the three emerging factors: 1) basic characteristics, 2) risk groups, 3) environmental variable. Cluster analysis was compiled groups of regions with similar characteristics. Cluster analysis of the breakdown of the county into three clusters based on selected indicators of living standards. They can be described as a group with higher average and lower standard of living. In the first cluster are only two regions (Liberec Region and Karlovy Vary), the third cluster is composed of Prague and the second cluster includes all other regions of the Czech Republic. To verify the evidence of differences between clusters were calculated by multivariate analysis of variance for the various indicators of living standards. An analysis of variance indicates that significant differences between clusters are caused by the standard of living indicators: GDP (regional), the average wage of women, medical equipment, culture entertainment and recreation, higher education, the disabled handicapped and older people. The data were processed in the program STATISTICA 10th.

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.

scholarly journals Living and social standard of population in the regions of the Czech Republic

2011 ◽  
Vol 59 (4) ◽  
pp. 63-70
Author(s):  
Jaroslav Dufek ◽  
Bohumil Minařík ◽  
Vlasta Nepivodová
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Czech Republic ◽  
The Czech Republic ◽  
Social Standard

Living and social standard of the population of the regions of the Czech Republic has been assessed by means of six expert selected indicators. Characteristics of the levels and variability, correlation and regressive relations between the indicators were determined and the regions were divided into groups using cluster analysis. Based on the 4 main indicators selected by means of factor analysis, global scores of weighted standardised values of indicators of living and social standard of the population were calculated for the respective regions and the order of regions was established.

scholarly journals A survey of feline trichomonosis suggests a low incidence of Tritrichomonas blagburni among cats in the Czech Republic

2017 ◽  
Vol 62 (No. 5) ◽  
pp. 269-273 ◽  
Author(s):  
V. Ceplecha ◽  
V. Svobodova ◽  
C. Lendon ◽  
R. Husnik ◽  
K. Horackova ◽  
...  
Keyword(s):  
Czech Republic ◽  
Single Case ◽  
Risk Groups ◽  
Species Differentiation ◽  
Culture Methods ◽  
The Czech Republic ◽  
Pcr Screening ◽  
Low Incidence ◽  
Medium Method

Tritrichomonas blagburni (previously called T. foetus) has been implicated as an aetiological agent of long-term large-bowel diarrhoea in cats in many countries worldwide. The aim of this study was to determine the presence of, and risk factors for T. blagburni among a cohort of cats living in different conditions in the Czech Republic. Samples were collected from 170 cats living in different environments. The InPouch™ TF-Feline medium method was used for diagnosis of feline trichomonosis. A single case (0.6%) with motile trichomonads identified as Pentatrichomonas hominis was found in a cat from a multi-cat household. Our study suggests that trichomonads and in particular, T. blagburni, infection may be much less common in the Czech Republic than in neighbouring countries, despite the inclusion of cats that were likely to be from higher-risk groups. A review of studies of the association of trichomonads and feline diarrhoea carried out in different countries revealed variation in the frequency of trichomonads detected. Different combinations of PCR or culture methods for screening or confirmation have been utilised, with or without species differentiation; however, this could not solely account for the variation in the occurrence between countries. From those studies where differentiation was performed, we calculated from the combined studies that T. blagburni occurred in six cats without diarrhoea (1.1%) and 47 cases with diarrhoea (5%). This finding supports an association with diarrhoea as well as the occurrence of asymptomatic cases. We note that in many studies, including our own, the occurrence of T. blagburni may well be underestimated and suggest that future studies use a combination of PCR screening of both faeces and faecal cultures, with differentiation of trichomonad species.

EVALUATION AND MODELLING OF GROUND WATER QUALITY DATA OF ALLAHABAD CITY BY ENVIRONMETRIC METHODS

2016 ◽  
Vol 2 (4) ◽  
pp. 211
Author(s):  
Girdhari Lal Chaurasia ◽  
Mahesh Kumar Gupta ◽  
Praveen Kumar Tandon
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Sampling Location ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Positive Loading

Water is an essential resource for all the organisms, plants and animals including the human beings. It is the backbone for agricultural and industrial sectors and all the small business units. Increase in human population and economic activities have tremendously increased the demand for large-scale suppliers of fresh water for various competing end users.The quality evaluation of water is represented in terms of physical, chemical and Biological parameters. A particular problem in the case of water quality monitoring is the complexity associated with analyzing the large number of measured variables. The data sets contain rich information about the behavior of the water resources. Multivariate statistical approaches allow deriving hidden information from the data sets about the possible influences of the environment on water quality. Classification, modeling and interpretation of monitored data are the most important steps in the assessment of water quality. The application of different multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) help to identify important components or factors accounting for most of the variances of a system. In the present study water samples were analyzed for various physicochemical analyses by different methods following the standards of APHA, BIS and WHO and were subjected to further statistical analysis viz. the cluster analysis to understand the similarity and differences among the various sampling stations.  Three clusters were found. Cluster 1 was marked with 3 sampling locations 1, 3 & 5; Cluster-2 was marked with sampling location-2 and cluster-3 was marked with sampling location-4. Principal component analysis/factor analysis is a pattern reorganization technique which is used to assess the correlation between the observations in terms of different factors which are not observable. Observations correlated either positively or negatively, are likely to be affected by the same factors while the observations which are not correlated are influenced by different factors. In our study three factors explained 99.827% of variances. F1 marked  51.619% of total variances, high positive strong loading with TSS, TS, Temp, TDS, phosphate and moderate with electrical conductivity with loading values of 0.986, 0.970, 0.792, 0.744, 0.695,  0.701, respectively. Factor 2 marked 27.236% of the total variance with moderate positive loading with total alkalinity & temp. with loading values 0.723 & 0.606 respectively. It also explained the moderate negative loading with conductivity, TDS, and chloride with loading values -0.698, -0.690, -0.582. Factor F 3 marked 20.972 % of the variances with positive loading with PH, chloride, and phosphate with strong loading of pH 0.872 and moderate positive loading with chloride and phosphate with loading values 0.721, and 0.569 respectively. 

scholarly journals Hierarchical Organization of Reading Subskills: Statistical Approaches

1975 ◽  
Vol 7 (3) ◽  
pp. 223-228 ◽  
Author(s):  
R. G. Stennett ◽  
P. C. Smythe ◽  
Madeline Hardy
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Multiple Regression ◽  
Analysis Of Variance ◽  
Hierarchical Organization ◽  
Experimental Designs ◽  
Stepwise Multiple Regression ◽  
Scaling Methods ◽  
Statistical Approaches ◽  
Reading Subskills

This article describes a number of alternate methodological solutions to the problem of isolating reading subskills and establishing the nature of their hierarchical organization. The authors briefly review and evaluate Stepwise Multiple Regression, Factor Analysis, Cluster Analysis, Scaling Methods, Analysis of Variance and Transfer Designs, as potential tools in research on the hierarchical organization of reading subskills. With the exception of transfer-type experimental designs, none of the approaches seems satisfactory, the authors conclude.

scholarly journals Species diversity of Carabidae (Coleoptera) in different succession stages of a limestone quarry Hády (Brno, Czech republic)

2010 ◽  
Vol 58 (4) ◽  
pp. 57-64 ◽  
Author(s):  
Vladimír Hula ◽  
Pavla Šťastná
Keyword(s):  
Cluster Analysis ◽  
Czech Republic ◽  
Red List ◽  
Pitfall Traps ◽  
Reclaimed Land ◽  
Significant Similarity ◽  
The Czech Republic ◽  
Invertebrate Animals ◽  
The Difference ◽  
And Control

Carabid beetle samples were obtained from pitfall traps at an abandoned quarry of a limestone massif Hády near Brno. The samples were collected each month between April and September in years 2007–2008. A total of 61 species were registered and represented by 2619 individuals. Similarity of individual areas was evaluated by using the Cluster analysis – the most significant similarity was found in reclaimed sites (99.87%) and control sites (90.52%). The difference in similarity between two observed years came out distinctively (F = 2.906; P = 0.0010). Three species (Cylindera germanica, Licinus cassideus and Aptinus bombarda) from the Red List of Invertebrate Animals of the Czech Republic and one species (Cylindera germanica) affected by the Order 395/1992 Sb. as amended by Decree 175/2006 Sb. were detected among the observed species. The reclaimed land and surrounding field areas left to natural succession were dominated by species of open habitats.

scholarly journals Development of the rate of employment and unemployment of males and females in ten associated countries of EU

2004 ◽  
Vol 52 (6) ◽  
pp. 87-104
Author(s):  
Jan Klíma ◽  
Milan Palát
Keyword(s):  
Cluster Analysis ◽  
Czech Republic ◽  
Statistical Analysis ◽  
Correlation Analysis ◽  
Development Trends ◽  
Total Employment ◽  
The Czech Republic ◽  
Males And Females ◽  
And Cluster Analysis

The paper is focused on the evaluation the rates of employment and unemployment of women, men and as a whole in ten associated countries of EU. Rates of employment were evaluated in the period 1996-2002. Rates of unemployment were evaluated in the period 1996-2003. Employment of males in all ten associated countries of EU is higher then employment of females. Unemployment of females in the Czech Republic, Cyprus, Malta, Poland, Slovakia and Slovenia is higher than unemployment of males and in opposite unemployment of females in Estonia, Hungary, Lithuania and Latvia is lower than unemployment of males. Trends of rates of male, female and total employment and unemployment are evaluated. Methods of regression and correlation analysis, development trends and cluster analysis were applied for the mathematical-statistical analysis.

Summer PM1 measured at a rural background site in Central Europe

2021 ◽  
Author(s):  
Petra Pokorná ◽  
Naděžda Zíková ◽  
Radek Lhotka ◽  
Petr Vodička ◽  
Jean-Eudes Petit ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Czech Republic ◽  
Size Distribution ◽  
Particle Number ◽  
Size Range ◽  
Organic Aerosol ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
The Czech Republic ◽  
Rural Background

<p>This work aims to assess the summer PM<sub>1</sub> based on particle size distribution, density and origin. An intensive sampling campaign was conducted in July 2019 at the National Atmospheric Observatory Košetice (NAOK) in the Czech Republic.</p><p>5-min integrals of particle number concentration (PNC) and particle number size distribution (PNSD) data were recorded by a Scanning Mobility Particle Sizer (size range 10 – 800 nm, SMPS, IFT TROPOS, Leipzig, with CPC 3772, TSI USA) and size-resolved PM chemical composition was measured by a Compact Time of Flight Aerosol Mass Spectrometer (C-ToF-AMS, Aerodyne, USA). 1-min PM<sub>1 </sub>black carbon (BC) concentrations by aethalometer (AE33, Magee Scientific, USA) and 4-h PM<sub>2.5 </sub>organic and elemental carbon (OC/EC) concentrations (Sunset Laboratory Inc., USA) were measured.  Also 12-h PM<sub>1</sub> samples by a sequential Leckel LVS-3 (Sven Leckel Ingenieurbüro, Germany) for a subsequent chemical analysis (water-soluble ions, monosaccharides, anhydrides, and saccharides) were collected. Additionally, 10-min average SO<sub>2</sub>, NO<sub>2</sub>, NO<sub>x</sub> and CO concentrations along with the values of meteorological parameters were recorded. To determine the origin of non-refractory PM<sub>1</sub> (NR-PM<sub>1</sub>) species (Org, NO<sub>3</sub><sup>-</sup>, SO<sub>4</sub><sup>2-</sup>, NH<sub>4</sub><sup>+</sup>) the back trajectories describing the air mass origin were clustered using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model [1]. Last, but not least, the multi-time factor analysis model [2] with modifications was applied on combined dataset (on-line and off-line measurements) to refine the analysis results with respect to the organic aerosol factors as well as organic aerosol sources and their origin.</p><p>The campaigns was characterized by prevailing westerly winds with average wind speed of 3.0±1.5 m s<sup>-1</sup>, average temperature of 18.5±4.7 °C and negligible precipitation. The average PM<sub>1</sub> (NR-PM<sub>1</sub> and eBC) measured concentration was 8.5±3.5 µg m<sup>-3</sup> (12h PM<sub>1</sub> 10.1±6.4 µg m<sup>-3</sup>). Based on the PNC predominated particles in the size range 25 – 80 nm (N25 – 50 and N50 – 80), also called the Aitken mode, typical for rural background stations and originates from the aging of the particles generated during new particle formation events [3]. NR-PM<sub>1</sub> was composed primarily by organics (58%) and sulphate (22%) in the accumulation mode (Org mode diameter 300 nm and SO<sub>4</sub><sup>2-</sup> mode diameter 385 nm) with average particle density ~ 1.4 g m<sup>-3</sup>. This result in combination with the cluster analysis points to the regional origin of the particles from southeast (Austria-Hungary-Slovakia). Six Org factors (primary organic aerosol (POA) – fungal origin, biomass burning organic aerosol (BBOA) – related secondary aerosol (SA), semivolatile aerosol – nitrate-rich, secondary organic aerosol (SOA) – oxalate-rich, semivolatile aerosol – microbial origin, primary traffic and biomass organic aerosol (OA)) based on combined data were resolved by multi-time factor analysis model. Modelling of combined dataset provided insides into processes involved in SOA formation and sources.</p><p> </p><p>[1] Rolph, G., et al., (2017) Environ. Modell. Software 95, 210–228.</p><p>[2] Zhou, L., et al., (2004) Atmos. Environ. 38, 4909–4920.</p><p>[3] Costabile, F., et al., (2009) Atmos. Chem. Phys. 9, 3163–3195.</p><p> </p><p>This work was supported by the GACR under grant P209/19/06110Y and by the MEYS of the Czech Republic under grant ACTRIS-CZ LM2018122 and ACTRIS-CZ RI (CZ.02.1.01/0.0/0.0/16_013/0001315).</p>

scholarly journals How to Gain Success in SME? A Case Study of a region in the Czech Republic.

2007 ◽  
Vol 2 (1) ◽  
pp. 41-47 ◽  
Author(s):  
Jarmila Sebestova ◽  
Zaneta Rylkova ◽  
Marek Smysl
Keyword(s):  
Economic Development ◽  
Factor Analysis ◽  
Czech Republic ◽  
Its Region ◽  
The Czech Republic ◽  
Endogenous Factors ◽  
Northeast Region ◽  
Research Findings

How to Gain Success in SME? A Case Study of a region in the Czech Republic.Small and medium sized entrepreneurship (SME) is often considered a contemporary phenomenon. Why have so many authors dedicated their work to this field? The main reason is that SME influences society and contributes to the economic development of its region. This analysis emphasizes endogenous factors for success. The situation in the Moravia-Silesian Region (the northeast region of the Czech Republic) in which we applied factor analysis on a research sample supported our hypothesis. Moreover, we used VRIO analysis to clearly interpret our research findings.

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