scholarly journals A MULTIVARIATE DATA ANALYSIS OF LOGISTICS STRUCTURE AS A FACTOR OF COMPETITIVENESS

2017 ◽  
Vol 14 (1) ◽  
pp. 90 ◽  
Author(s):  
Henrique Duarte Carvalho ◽  
Henrique Terra Fonseca
Keyword(s):  
Economic Growth ◽  
Data Analysis ◽  
World Trade ◽  
Multivariate Data Analysis ◽  
Literature Search ◽  
Multivariate Data ◽  
Secondary Data ◽  
Relationship Of ◽  
Data Analysis Methods

The importance of logistics structure to economies is becoming increasingly significant and in order to support the economic growth based on exports, governments have sought to constantly improve the quality of logistics infrastructure of their countries, ensuring and promoting competitiveness of its production internationally. The consensus is that the logistics structure forms a vital link in the entire chain of trade, contributing to the international competitiveness of a country. This study aims to characterize the country as its logistics structure and relationship of this result to the promotion of competitiveness for them by relevance participation in world trade. To reach that goal the methodological procedure was performed a literature search and analysis of secondary data. Initially, through the identification and validation of data for countries and hence the application of multivariate data analysis methods to measure dimensions that allow such classification, planning, and especially the identification of dimensions of logistics infrastructure components in terms of promotion competitiveness.

scholarly journals Evaluation and Comparison of Statistical and Conventional Methods of Forecasting Avalanche Hazard

1977 ◽  
Vol 19 (81) ◽  
pp. 375-387 ◽  
Author(s):  
P. Föhn ◽  
W. Good ◽  
P. Bois ◽  
C. Obled
Keyword(s):  
Data Analysis ◽  
Statistical Models ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Raw Data ◽  
Avalanche Forecasting ◽  
Avalanche Hazard ◽  
Forecasting Methods ◽  
Conventional Methods

Abstract Principal problems concerning the raw data and methodological limitations of statistical and conventional avalanche forecasting methods are summarized. The concepts of four statistical models based on multivariate data analysis, are outlined in a few words. In order to give an idea of the potential and quality of the different methods, test runs over two winters are discussed and a tentative store is established. Statistical models I and IV, together with the conventional forecast, attain a score of 70-80%, whereas statistical models II and III show a slightly poorer performance.

scholarly journals COMBINED MULTIVARIATE DATA ANALYSIS OF GC/MS AND SENSORY EVALUATION FOR AROMA QUALITY PREDICTION MODEL OF ORTHODOX BLACK TEA GRADES

2016 ◽  
Vol 54 (6) ◽  
pp. 708
Author(s):  
Hoang Quoc Tuan ◽  
Nguyen Duy Thinh ◽  
Nguyen Thi Minh Tu
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Consumer Preference ◽  
Preference Test ◽  
Black Tea ◽  
Quality Prediction ◽  
Least Squares Regression ◽  
Volatile Composition

Relationships between sensory aroma and the volatile composition of 04 black tea grades produced from Northern Vietnam were studied. Consumer preference test on the aroma was carried out by 80 consumers to evaluate the aroma quality of these samples. Aroma concentrate was prepared by Brewed Extraction Method (BEM) method and analyzed using GC/MS. Partial Least Squares Regression (PLSR) was used to determine the relationship between preference scores and peak area percentage data of 39 detected volatile compounds. Among these compounds, 20 identified compounds were determined to contribute significantly to the perceived aroma quality of OTD black teas. On the basis of these 20 compounds, the PLSR model was constructed to predict the aroma quality of OTD black teas. The result showed that the volatile composition by GC/MS in the profiling with sensory and multivariate data analysis should be a useful reference for aroma quality prediction of OTD black tea grades.

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