Classification and regression tree, principal components analysis and multiple linear regression to summarize data and understand travel behavior

2009 ◽  
Vol 1 (4) ◽  
pp. 295-308 ◽  
Author(s):  
Cira Pitombo ◽  
A. Sousa ◽  
L. Filipe
2020 ◽  
pp. 291-317
Author(s):  
Ana Nieto Masot ◽  
Nerea Ríos Rodríguez ◽  
Gema Cárdenas Alonso

Desde finales del siglo XX, la aparición de nuevas tendencias y modelos de consumo del turismo ha permitido la proliferación de equipamientos turísticos en Extremadura, optándose por la explotación de los recursos patrimoniales, tanto naturales como culturales. Así, en este trabajo se analizan la oferta y demanda del sector turístico en la región extremeña, haciendo hincapié en la clasificación por Territorios Turísticos del gobierno regional. Para ello, se realiza un análisis de variables económicas, patrimoniales y sociales mediante las técnicas de Regresión Lineal Múltiple (OLS), Análisis de Componentes Principales y Sistemas de Información Geográfica (SIG). Se podrá ver qué Territorios Turísticos han implantado mejores líneas de actuación encaminadas a la recepción de visitantes, al incremento de las rentas económicas y a una preservación de la población en los espacios rurales, así como cuáles presentan deficiencias en su desarrollo turístico. Since the end of the last century, the development of new trends and tourism consumption models has allowed the proliferation of tourist equipment in Extremadura, exploiting the heritage resources, both natural and cultural. In this paper, supply and demand of tourist sector in Extremadura are analyzed, emphasizing the classification by Tourist Territories of the regional government. For this, economic, social and patrimonial variables are studied through the Multiple Linear Regression statistic (OLS), Principal Components Analysis (PCA)and Geographic Information Systems (GIS). It will be seen what Tourist Territories have implemented better strategies intend to reception of visitors, the increase of economic rents and the preservation of the population in rural areas, as well as which ones present deficiencies in their tourist development.


2020 ◽  
Vol 14 (2) ◽  
pp. 61-76
Author(s):  
Saurabh Kumar

Webometrics can be used for understanding the quantitative aspects of web resources. The present study investigates the role of webometrics in determining the academic ranking of the institute. The extensive research was conducted on a sample of 59 reputed academic institutes based out of India. The data was analysed using two techniques viz. linear regression and classification and regression tree. From the results of the study, it was found that among all the webometrics parameters, Alexa rank and Semrush rank of the website was found to be the most crucial factor for determining the academic ranking of the institute. The study has insights for policymakers of the institute as the results of the study can be used for devising various ways to improve the webometrics parameters in order to enhance the academic ranking of the institute.


1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.


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