scholarly journals Comparison of theoretical derivations, simple linear regressions, multiple linear regression and principal components for analysis of fish mortality, growth and environmental temperature data

2001 ◽  
Vol 12 (6) ◽  
pp. 591-598 ◽  
Author(s):  
A. L. Jensen
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Principal Components ◽  
Environmental Temperature ◽  
Temperature Data ◽  
Fish Mortality ◽  
Linear Regressions

scholarly journals Relationship between the Physical Properties and Hand of Jean Fabric

2016 ◽  
Vol 16 (3) ◽  
pp. 138-145 ◽  
Author(s):  
Atsushi Kawamura ◽  
Chunhong Zhu ◽  
Julie Peiffer ◽  
KyoungOk Kim ◽  
Yi Li ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Physical Properties ◽  
Multiple Linear Regression ◽  
Principal Components ◽  
Evaluation System ◽  
Principal Component ◽  
Semantic Differential ◽  
Differential Method ◽  
Fabric Hand

Abstract We investigated the distinctive characteristics of jean fabrics (denim fabrics obtained from jeans) and compared the physical properties and the hand. We used 13 kinds of jean fabric from commercial jeans and 26 other fabric types. The physical properties were measured using the Kawabata evaluation system, and the fabric hand was evaluated by 20 subjects using a semantic differential method. To characterise the hand of jean fabrics compared with other fabrics, we used principal component analysis and obtained three principal components. We found that jean fabrics were characterised by the second principal component, which was affected by feelings of thickness and weight. We further characterised the jean fabrics according to ‘softness & smoothness’ and ‘non-fullness’, depending on country of origin and type of manufacturer. The three principal components were analysed using multiple linear regression to characterise the components according to the physical properties. We explained the hand of fabrics including jean fabrics using its association with physical properties.

Dissociation of substituted benzenesulphonamides in water, methanol and ethanol

1984 ◽  
Vol 49 (5) ◽  
pp. 1182-1192 ◽  
Author(s):  
Miroslav Ludwig ◽  
Oldřich Pytela ◽  
Karel Kalfus ◽  
Miroslav Večeřa
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Potentiometric Titration ◽  
Principal Components ◽  
Dissociation Constants ◽  
Benzoic Acids ◽  
Reaction Constant ◽  
The Media ◽  
Reaction Constants

Thirteen monosubstituted arylsulphonamides (XC6H4SO2NH2) and two 3,4-disubstituted arylsulphonamides (X2C6H3SO2NH2) have been synthetized and their dissociation constants have been measured by potentiometric titration in water, methanol, and ethanol. The Hammett substitution dependences have been calculated for all the media, and changes in the reaction constants due to transition from water to alcohols are discussed in confrontation with analogous dependences of benzoic acids. The reaction constant ρ found in methanol is lower than that in water. The dissociation constants have been treated mathematically by the method of the principal components and by multiple linear regression.

scholarly journals Factors Controlling Coral Skeletal U/Ca Ratios with Implications for their Use as a Proxy for Past Ocean Conditions

2021 ◽  
Vol 17 (4) ◽  
pp. 3-17
Author(s):  
Emily Patterson ◽  
Spencer Eanes ◽  
Penelope Lancrete ◽  
Anne Gothman ◽  
Paul Roback
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Environmental Variables ◽  
Scleractinian Coral ◽  
Environmental Parameters ◽  
Environmental Data ◽  
Global Ocean ◽  
Regularized Regression ◽  
Concentration Data ◽  
Linear Regressions

Seawater temperature, salinity and carbonate chemistry have been shown to influence the uranium/calcium (U/Ca) ratios of scleractinian coral skeletons. This apparent sensitivity of U/Ca to multiple environmental parameters calls into question whether there is one environmental variable that most strongly controls coral U/Ca, and whether U/Ca can be straightforwardly applied as a paleoenvironmental proxy due to the tendency of environmental variables to covary in space and time. In this study, uranium concentration data from an existing compilation of tropical scleractinian coral U-series measurements is paired with environmental data from the World Ocean Atlas (WOA) and the Global Ocean Data Analysis Project (GLODAP) to examine the sensitivity of coral skeletal U/Ca to multiple seawater properties including temperature, salinity, pH, and saturation state. First, univariate linear regressions and multiple linear regressions were used to compare relationships between uranium and environmental parameters in the dataset with relationships observed in previous studies. Next, principal component analysis and regularized regression were used to identify the most likely predictors of coral U/Ca in order to create a multiple linear regression model. Results indicate that pH, Ω, alkalinity, and temperature are all significant predictors of uranium concentrations in coral. The magnitude and strength of relationships between U/Ca and environmental variables also differ across different genera. Seawater properties with strong correlations and small ranges make interpretation of these results difficult. However, results of these analyses indicate that U/Ca is dependent on multiple environmental parameters and that previously developed univariate regressions may be insufficient to characterize the full range of variables that influence coral [238U]. KEYWORDS: Coral; Paleoceanography; Proxy Calibration; Uranium; Multiple Linear Regression; Regularized Regression; Environmental Change; Oceanographic Databases

scholarly journals Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

2015 ◽  
Vol 2 (4) ◽  
pp. 1317-1337 ◽  
Author(s):  
T. S. dos Santos ◽  
D. Mendes ◽  
R. R. Torres
Keyword(s):  
Climate Change ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
South America ◽  
Multiple Linear Regression ◽  
Principal Components ◽  
Northeastern Brazil ◽  
Monthly Precipitation ◽  
Artificial Neural

Abstract. Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANN) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon, Northeastern Brazil and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model out- put and observed monthly precipitation. We used GCMs experiments for the 20th century (RCP Historical; 1970–1999) and two scenarios (RCP 2.6 and 8.5; 2070–2100). The model test results indicate that the ANN significantly outperforms the MLR downscaling of monthly precipitation variability.

scholarly journals Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

2016 ◽  
Vol 23 (1) ◽  
pp. 13-20 ◽  
Author(s):  
T. Soares dos Santos ◽  
D. Mendes ◽  
R. Rodrigues Torres
Keyword(s):  
Climate Change ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
South America ◽  
Multiple Linear Regression ◽  
Principal Components ◽  
Northeastern Brazil ◽  
Monthly Precipitation ◽  
Artificial Neural

Abstract. Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANNs) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon; northeastern Brazil; and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model output and observed monthly precipitation. We used general circulation model (GCM) experiments for the 20th century (RCP historical; 1970–1999) and two scenarios (RCP 2.6 and 8.5; 2070–2100). The model test results indicate that the ANNs significantly outperform the MLR downscaling of monthly precipitation variability.

scholarly journals PLANIFICACIÓN TURÍSTICA EN EXTREMADURA. ANÁLISIS DE SUS TERRITORIOS TURÍSTICOS

2020 ◽  
pp. 291-317
Author(s):  
Ana Nieto Masot ◽  
Nerea Ríos Rodríguez ◽  
Gema Cárdenas Alonso
Keyword(s):  
Geographic Information Systems ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Principal Components ◽  
Rural Areas ◽  
Supply And Demand ◽  
Regional Government ◽  
Components Analysis ◽  
Siglo Xx ◽  
Sistemas De Informacion

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.

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