Partial Least Squares Regression

This study used multiple regression analysis to estimate the relationships between egg albumen index and height and external quality traits of eggs in Japanese quails. Egg albumen index and height were selected as dependent variables, while egg weight, width, length, shape index and Haugh unit were determined as independent variables. In the multiple regression analysis, although the overall fit of the model was high, it was determined that there were multicollinearity problem among the independent variables. To solve this problem, the Principal Components Regression (PCR) method, which is widely used in the literature, was applied. With this method, the following regression equations were obtained, respectively, by using egg weight (X1, Z1), width (X2, Z2), length (X3, Z3), shape index (X4, Z4) and Haugh unit (X5, Z5) variables in estimating albumen index and height: Y ̂=(-19.95) ̂-(0.02) ̂X_1-(0.22) ̂X_2-(0.03) ̂X_3+(0.01) ̂X_4+(0.43) ̂X_5+e ̂ T ̂=(-13.21) ̂+(0.09) ̂Z_1+(0.03) ̂Z_2-(0.004) ̂Z_3-(0.005) ̂Z_4+(0.18) ̂Z_5+e ̂ The regression equations obtained were found to be statistically significant (P<0.05) and the goodness of fit of the models were determined as Detaermination coefficient (R²) = 0.86 and 0.99, respectively. Principal Components Regression (PCR) method reduced the errors caused by multicollinearity problem by decreasing the standard errors of the parameters and increased the accuracy of the model. The results show that Principal Components Regression (PCR) method effectively solved the multicollinearity problem and ensured the reliability of the model by increasing the prediction accuracy. These findings reveal that Principal Components Regression (PCR) method can be used effectively in poultry breeding and selection studies.

<p>Trumpeter Swans were extirpated from Ontario in the late 1800s and by the early 1900s, only three breeding populations remained, with most of the surviving birds in Alaska (Pacific Coast Population), fewer in Canada (Western Canada Population), and a population in the Greater Yellowstone area of the contiguous United States. The Ontario reintroduction program ended in 2006, and limited empirical analyses have been conducted since, hence, this paper examines the Ontario breeding distribution of TRUS (1991-2021) to see if there is evidence of density dependent range expansion in areas around the captive breeding release sites. To do this, I first quantified the seasonal distribution by grid cells count (2.96 km x 2.96 km) followed by kernel density mapping. Secondly, I assessed the occurrence and extent of short-stopping by calculating the average geographic location o sightings and the minimum distance between each wintering location and release sites.</p> <p>These drivers acted as independent variables for the regression model, and it was found that the R2 of 0.15 (Fstat = 1117.328, p<0.0001). The most important predictor in the model was distance to release sites (-0.225), followed closely by distance to winter sites (-0.205), in which they both had a negative relationship with the dependent variable. On the other hand, human population density had a positive relationship of 0.078 with the dependent variable. The results from this study confirmed that the Ontario breeding distribution of TRUS (1991-2020) shows evidence of density dependent range expansion in areas around captive breeding release sites, confirming short-stopping in winter as well.</p>

<p>Los métodos de regresión se utilizan ampliamente en el ámbito forestal para la predicción y el cartografiado de las variables de estructura y combustibilidad. En este artículo se evalúan diferentes modelos de regresión (lineal, no lineal, árboles de regresión y ensemble). Como variables independientes se utilizaron métricas extraídas de datos LiDAR full-waveform, mientras que los valores de las variables dependientes se generaron a partir de modelos basados en datos de campo obtenidos para 78 parcelas de 16 m de radio. Se llevaron a cabo transformaciones de las variables dependientes e independientes con selección de atributos para evaluar su influencia en la predicción de la variable respuesta. Con el fin de verificar diferencias significativas y ordenar los modelos de regresión se emplearon los tests no paramétricos de Wilcoxon y Friedman, y el análisis post-hoc o los tests de comparación post-hoc por pares, como el de Nemenyi, para el test de Friedman. Las regresiones basadas en la transformación de la variable dependiente, como raíz cuadrada o logaritmo, o en la transformación de las variables independientes, obtuvieron un incremento de la R<sup>2</sup> de hasta un 6% con respecto a la regresión lineal. Mediante el método CART (Classification and Regression Tree) se obtuvieron resultados discretos, si bien su uso puede estar indicado para la categorización o estratificación. Con el método basado en la transformación de la variable dependiente mediante raíz cuadrada se consiguieron los mejores resultados comparativos en la predicción de variables forestales, excepto para el volumen. Sin embargo, su uso no siempre implica una mejora significativa con respecto a los otros métodos de regresión usados en este trabajo.</p>

During the COVID-19 lockdown, India saw a major restriction in the movement of people. Patients with acute myocardial infarction (MI) required early interventions and follow-up of independent predictors like symptom-to-balloon (STB) time and door-to-balloon (DTB) time. This study aimed to determine changes in STB and DTB time before and after the COVID-19 lockdown and its associated risk factors. A hospital-based cross-sectional study of 105 patients admitted to the cardiac care units (CCU) of two tertiary care centers in a district of Southern India for six months was conducted to compare the changes in STB and DTB time before and after the COVID-19 lockdown (three months before March 2020 and three months after March 2020), and data was collected from medical records. The data collected was then entered into Microsoft Excel (Microsoft Corporation, Washington, USA), numerically coded, and analyzed using SPSS Statistics version 21 (IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp.). The Chi-square and Mann-Whitney U tests assessed the association between the dependent and independent variables. The STB/DTB time (before and after the COVID-19 lockdown) was the dependent variable, while the age, gender, co-morbidities, smoking status, and date of admission of patients (before and after the COVID-19 lockdown) were taken as the independent variables. A p-value of <0.05 was considered statistically significant. The predictor variables were identified using the regression method, where all variables with a significance of <0.2 were taken. The overall mean (±SD) STB time was 408.7 (±307.1) minutes, and the mean (±SD) DTB time was 161.7 (±261.6) minutes. The pre-lockdown mean STB time was 404.6 minutes, and the mean DTB time was 153 minutes, whereas the post-lockdown mean STB and DTB time were higher at 413.3 minutes and 171.6 minutes, respectively. Out of the total 105 patients, 95 (90.5%) had an STB time of ≥120 minutes, and 77 (73.3%) had an ideal DTB time of <90 minutes. There was no statistically significant variation in the STB and DTB time before and after the lockdown. Only the age group >60 years (38 (97.4%)) was found to be statistically significant with an STB time of ≥120 minutes after the lockdown (p-value=0.040), and patients referred from primary and secondary care centers (AOR (95% CI)=4.669 (1.129-19.298)) were found to be an independent factor in reducing DTB time before and after the COVID-19 lockdown. The efficiency of the health system, irrespective of the COVID-19 lockdown, was observed; nevertheless, a delay in the overall recognition of symptoms of MI was perceived. The importance of time factors in identifying the symptoms of non-communicable diseases (NCDs), especially MI and stroke, has to be ascertained among the general population.

1 Introduction The question raised in this article is about how to theoretically and methodologically apprehend public policy controversies (1). More precisely, the role of ideas during public policy controversies between different policy coalitions is put into perspective. The starting point is the will to analytically construct ideas as dependent variables. Public policy scholars have underlined the confusion that lies at the heart of public policy analysis, where the status of ideas as causes or as variables is often blurred (Hassenteufel/Smith 2002: 60). When ideas are considered as independent variables, the analysis is likely to be more descriptive than explicative. In order to construct ideas as dependent variables, the focus has to be on the progressive formation of belief through the coalitions' adversarial activities. Hence, what will be questioned here is the process by which individuals do engage--or not-in public policy debate regarding harm reduction. We will not consider actors' engagement in a cause as unilaterally deriving from their beliefs. This line of questioning is closely linked to the ontological and epistemological choices. We assume that a processualist ontological stance is well-fitted to avoid the tautology induced by considering ideas as deja-la. By viewing militant commitment as a social and dynamic activity, a processualist focus highlights how ideas occur in the course of action (Fillieule 2001: 199-200). From this point of view, mobilized groups are by no means seen as preexisting entities driven by fixed ideas (Offerle 1994). This is particularly salient in the case of highly emotional controversies, where actors' ideas are often considered as if they had always existed. Furthermore, actors engaged in the same struggle do not necessarily share homogenous beliefs. Analyzing the heterogeneity of investments enables to understand the dynamics of the collective action (Mathieu 2004: 19). Similarly, the focus on the process of idea formation during policy controversies provides an opportunity to attain a deep understanding of the dynamics of the controversy. Analyzing ideas as dependent variables has important methodological repercussions. It shifts the location to where the explicative factors -and hence the data- are searched. The present reflection on public policy controversies is based on a case study research on harm reduction policies in matters of drug addiction in two Swiss cantons, Vaud and Geneva. The theoretical starting point of this study is the Advocacy Coalition Framework (ACF). Daniel Kubler (2000) has convincingly illustrated that this particular subject is well captured with the help of ACF's theoretical framework. The formulation of harm reduction policies are regularly the scene of sharp confrontations between policy coalitions, and ACF's concepts are particularly well-suited to the analysis of the Swiss multilevel politico-administrative system. Meanwhile, the benefits of using concepts from the social movement theory (Kubler 2001: 623) or the political sociology (Hassenteufel/Smith 2002: 63) in the public policy analysis has been asserted. The paper is structured as follows: In the first section, we review the contributions of the ACF to public policy analysis, and point out two theoretical points that appeared to be worth specification in the light of our case studies. These two points, already identified in the literature, concern the study of the long-term coalitions' birth and structuring process (Kubler 2001: 623; Schlager 1995), and the analysis of the specific arenas where the controversy takes place (Hassenteufel/Smith 2002:70-71; Muller 2006: 52). The second section is a theoretical discussion where we detail the concepts drawn from political sociology we used in our study. In the third section, we discuss the methodological operationalization of these concepts. We finally turn to the case studies on harm reduction policies. 2 Advocacy Coalition Framework By founding the Advocacy Coalition Framework at the end of the 1980s, Paul A. …

As a key tool for real-time monitoring of air pollutant concentrations, the chemical sensor, the core component of the low-cost Air Quality Monitor (AQM), is susceptible to a variety of factors during the measurement process, leading to errors in the measurement data. To enhance the measurement accuracy of chemical sensors, this paper presents a calibration method based on the PCR-GPR model. This method not only effectively enhances the measurement accuracy of chemical sensors, but also combines the interpretability of traditional statistical models with the high-precision characteristics of Gaussian Process Regression (GPR) models. First, we perform Principal Component Analysis (PCA) on the measurement data of the AQM to solve the multicollinearity problem. Through PCA, we successfully extracted 8 principal components, which not only contained 95% of the information in the original data, but also effectively eliminated the correlation between the variables, providing a more robust data base for subsequent modeling. Subsequently, we established a Principal Component Regression (PCR) model using the concentration of pollutants measured by the national monitoring station as the dependent variable and the 8 principal components extracted above as the independent variables. The PCR model can effectively extract the linear relationship between the independent and dependent variables, providing a linear part of the explanation for the calibration process. However, there are often complex nonlinear relationships between pollutant concentrations and AQM measurements. To capture these nonlinear relationships, we further established a GPR model with the residuals of the PCR model as the dependent variable and the measurement data of the AQM as the independent variable. By combining the PCR model and the GPR model, we obtained the final PCR-GPR calibration model. It is worth mentioning that this study adopted the time series cross-validation method for data grouping, an innovative approach that is more aligned with real-world scenarios and adequately captures the seasonal variations in pollutant concentrations. The experimental results show that the model exhibits excellent performance on several evaluation metrics and can calibrate the chemical sensor well, improving its measurement accuracy by 16.94% ~ 82.01%.

This book highlights the estimation of crop yield in Central Gujarat, especially with regard to the development of Multiple Regression Models and Principal Component Regression (PCR) models using climatological parameters as independent variables and crop yield as a dependent variable. It subsequently compares the multiple linear regression (MLR) and PCR results, and discusses the significance of PCR for crop yield estimation. In this context, the book also covers Principal Component Analysis (PCA), a statistical procedure used to reduce a number of correlated variables into a smaller number of uncorrelated variables called principal components (PC). This book will be helpful to the students and researchers, starting their works on climate and agriculture, mainly focussing on estimation models. The flow of chapters takes the readers in a smooth path, in understanding climate and weather and impact of climate change, and gradually proceeds towards downscaling techniques and then finally towards development of principal component regression models and applying the same for the crop yield estimation

A Jump-U model of echo pattern for a sonar ranging module

Microbial and epidemiological factors in early detection of esophageal squamous cell carcinoma and precancerous lesions.

This paper studies the dynamics of a mathematical model of a continuously reproducing diploid population with two alleles at one locus. The dependent variables are allele frequency and population density. If the genotype fitnesses are frequency and density dependent, the stability of equilibria is related to the geometry of the zero allele fitness curves. The asymptotic behavior of solutions where fitness is only density dependent is contrasted to the asymptotic behavior where fitness is frequency and density dependent. A parameterized family of fitness functions giving a Hopf bifurcation and limit cycles is investigated analytically and numerically.

This study aims to determine the factors that influence Mudharobah financing decisions at the Muamalat Bank Jambi Branch. This relates to the arrears in repaying Customer's loans to banks. In this study using primary data, and the method used in this study is the method of multiple linear regression. Econometrics analysis using multiple linear regression was conducted to identify the variables that influenced the Mudharobah financing decision at the Muamalat Bank Jambi branch. The variables used in this study are financing decision variables as dependent and character variables, capacity, capital, conditions of economic, collateral as independent variables. 25 statement items used to assess financing decisions, there are 99.96 percent of statement items used to assess financing decisions are declared valid or can be accepted as statement items to assess financing decisions, only 0.04 percent of statement items in assessing financing decisions are declared invalid. Financing decision as a dependent variable in this study there is a Cronbach's Alpha value of 0.988 which is greater than the value of r table that is 0.3202 meaning all items of this statement are declared reliable or trusted as a data collection tool in this study.

This article concerns aspects of internationalization and market servicing modes in the Greek food industry. During the late 1980s, foreign direct investment into the Greek food industry increased to around US $80 million per year from below US $10 million in the early 1980s and sales arising from accumulated FDI inflows (called here subsidiary sales, i.e., sales by subsidiaries of multinational enterprises) became an important mode for servicing sales in Greece by foreign companies. Sales arising from licensing grew but were relatively less important except in the oils and fats sector of the food industry, whereas the contribution of imports declined. The present study examines the determinants of the shares of subsidiary sales and licensed sales in total production separately for eight three-digit food subsectors of the Greek food industry for 6 years, using the Tobit model with fixed effects to account for the limited nature of the dependent variable. Ownership advantages, proxied by variables to represent marketing and technological intensity, and internalization conditions, proxied by a variable to represent transactions costs, are shown to be important determinants of the subsidiary sales share. The licensing sales share is high in subsectors where technological competition is advanced but transactions costs are low. © 1998 John Wiley & Sons, Inc.

This study is focused on the influences of corporate social responsibility on the performance of the commercial banks of Bangladesh. Most of the commercial banks are covered in this study. The independent variable (corporate social responsibility) have been analyzed by several indicators. Primary data has been collected from 200 respondents through a self-administered questionnaire and secondary data has been collected from recently published articles, research papers, annual report, conference proceedings, survey report, internship report, and websites etc. which are relevant to the subject matter of the study. To measure reliability and validity of the study, reliability statistics and KMO and Bartlett's Test have been adopted. In both test the sample size and data are proved to be reliable and valid for the study. To measure the relationship between corporate social responsibility and corporate performance, regression and correlation method have been done. The regression coefficients of most of the indicators of independent variable have significant values to prove the sufficient influence on the dependent variable (corporate performance). The correlation coefficient also proves a strong relationship between dependent and independent variable. The study concludes that as there is huge impact of social responsibility on corporate performance, the corporations should be ethically responsible and go forward for providing more contribution to the society to make sure sustainable growth of the industry and betterment of the people of the country. Keywords: Corporate Social Responsibility, Corporate Performance, CSR practices. DOI : 10.7176/EJBM/11-5-02

Slums are densely populated settlements that are not habitable, the building density is very high in a very limited area, causing various problems such as being prone to social diseases and diseases. The distribution of slums in an area can be analyzed using geographic information systems as an innovative strategy to visualize geospatial information accurately, allowing for more in-depth and data-based analysis to support decision making. This study uses the Geographically Weighted Regression (GWR) method. Geographically Weighted Regression (GWR) is a statistical analysis that allows researchers to evaluate spatial variations in the relationship between dependent and independent variables, using the Fixed Gaussian kernel function to provide constant weighting across research locations, so that they can understand complex and different spatial relationships in each location. Based on data processing on seven indicators and 16 slum parameters stipulated in PUPR Regulation No. 14 of 2018, Panjang District, Bandar Lampung City, there are 5 sub-districts that are categorized as light slums with slum distribution points spread across various locations without clear concentration. The results of the Geographically Weighted Regression (GWR) model show that the independent variables (X) and dependent variables (Y) reflect a complex relationship with variable X 11 (non-standard wastewater management system) having the highest coefficient of 0.213830. The results of the GWR model in the ANOVA table show that the GWR model does not provide significant improvement.

Putting the Measurement of Physical Capacity of Older Adults in Its Place.