scholarly journals Analysis of seasonal growth through the application of a multiple linear regression model on data from tag-and-recapture experiments

2010 ◽  
Vol 11 (1) ◽  
pp. 143
Author(s):  
C. STAMATOPOULOS ◽  
J.F. CADDY
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Temporal Variation ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Annual Growth ◽  
Seasonal Growth ◽  
Variation Data ◽  
The Impact

When the Brody coefficient K is subject to temporal variation, data from tag-and-recapture experiments permit analysis of seasonal growth. Temporal values for K can be estimated without using a pre-determined oscillating function and the impact of seasonality on annual growth can be analyzed more realistically. The method is applicable to intra-annual intervals of single or multiple cohorts.

scholarly journals Influence of the key account manager in the provisioning management: Evidence from staple companies during the events of COVID-19

Accounting ◽  
2022 ◽  
Vol 8 (2) ◽  
pp. 161-170 ◽  
Author(s):  
Luis-Ricardo Flores-Vilcapoma ◽  
Cynthia-Paola A lbengrin-Mendoza ◽  
Gabriela-Briggite Gomez-Rojas ◽  
Yuri Sánchez-Solis ◽  
Wagner Vicente-Ramos
Keyword(s):  
Experimental Design ◽  
Linear Regression ◽  
Correlation Analysis ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Cross Sectional ◽  
Key Account ◽  
The Impact

The purpose of this research was to evaluate the degree of influence exercised by the Key Account Manager in the provisioning management in the main companies called Staple in Peru, during the events of COVID-19. The research was of type quantitative, cross-sectional and temporal, with a non-experimental design, using a multiple linear regression model and correlation analysis to determine the impact that exists between the variables. The data belongs to the Industrias San Miguel company, distributed in a weekly period from June 2019 to March 2021, which gives 88 observations. The results allow us to conclude that the Key Account Manager is an important manager of the supply of goods during the crisis caused by COVID-19 in staple companies.

scholarly journals Distribusi Belanja Desa dan Capaian Status Pembangunan Desa di Provinsi Riau

2021 ◽  
Vol 5 (2) ◽  
pp. 407
Author(s):  
Bakti Kharisma ◽  
Werry Darta Taifur ◽  
Fajri Muharja
Keyword(s):  
Linear Regression ◽  
Rural Development ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Development Process ◽  
Multiple Linear Regression Model ◽  
Urban Bias ◽  
The Impact ◽  
The Village

The Village Law has become one of the berakhthroughs in overcoming the impact of development that tends to be urban bias. Village is no longer only an object of development but the main actor in rural development process. The source of the budget for the implamentation of rural development has increased significantly with the village fund policy. This study aims to analyze the impact of village budgets and village typology on the achievement of village status in Riau Province. Multiple linear regression model was used to analyze the impact of village budget and village typology has a significant impact on the increase in the developing village index in Riau Province.

scholarly journals Em Busca do Orientador de Formação Ideal

2015 ◽  
Vol 28 (4) ◽  
pp. 486
Author(s):  
Ana Pinheiro Sá ◽  
Cristina Teixeira-Pinto ◽  
Rafaela Veríssimo ◽  
Andreia Vilas-Boas ◽  
João Firmino-Machado
Keyword(s):  
Internal Medicine ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Learning Process ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Clinical Teacher ◽  
Clinical Teachers ◽  
The Impact

<strong>Introduction:</strong> The authors established the profile of the Internal Medicine clinical teachers in Portugal aiming to define a future interventional strategy plan as adequate as possible to the target group and to the problems identified by the residents.<br /><strong>Material and Methods:</strong> Observational, transversal, analytic study. An online anonymous questionnaire was defined, evaluating the demographic characteristics of the clinical teachers, their path in Internal Medicine and their involvement in the residents learning process.<br /><strong>Results:</strong> We collected 213 valid questionnaires, making for an estimated response rate of 28.4%. Median global satisfaction with the clinical teacher was 4.52 (± 1.33 points) and the classification of the relationship between resident and clinical teacher was 4.86 ± 1.04 points. The perfect clinical teacher is defined by high standards of dedication and responsibility (4.9 ± 1.37 points), practical (4.8 ± 1.12 points) and theoretical skills (4.8 ± 1.07 points). The multiple linear regression model allowed to determine predictors of the resident’s satisfaction with their clinical teacher, justifying 82,5% of the variation of satisfaction with the clinical teacher (R2 = 0.83; R2 a = 0.82).<br /><strong>Discussion:</strong> Postgraduate medical education consists of an interaction between several areas of knowledge and intervening variables in the learning process having the clinical teacher in the central role. Overall, the pedagogical abilities were the most valued by the Internal Medicine residents regarding their clinical teacher, as determinants of a quality residentship.<br /><strong>Conclusion:</strong> This study demonstrates the critical relevance of the clinical teacher in the satisfaction of residents with their residentship. The established multiple linear regression model highlights the impact of the clinical and pedagogical relantionship with the clinical teacher in a relevant increase in the satisfaction with the latter.

The Impact of Climate Change on Agriculture in Morocco: An Econometric Study

2020 ◽  
Vol 1 (2) ◽  
pp. 19-28
Author(s):  
Faycel Tazigh
Keyword(s):  
Climate Change ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Independent Variables ◽  
Temperature And Precipitation ◽  
The Impact ◽  
The Relationship

This paper aims to analyze the relationship that may exist between climate change and cereal yield in Morocco. In order to study this correlation between variables, we used the most common form of regression model which is the multiple linear regression model. There are two main uses of multiple linear regression model. The first one is to quantify the weight of impact that the independent variables had on the dependent variable. The second use is to predict not only the relationship that may found between variables but also their impacts. In our case, we have chosen temperature and precipitation as an independent variables and cereal yield as dependent variable.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

scholarly journals Magnetization Transfer Ratio of Peripheral Nerve and Skeletal Muscle

2021 ◽  
Author(s):  
Olivia Fösleitner ◽  
Véronique Schwehr ◽  
Tim Godel ◽  
Fabian Preisner ◽  
Philipp Bäumer ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Body Weight ◽  
Sciatic Nerve ◽  
Linear Regression ◽  
Peripheral Nerve ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Magnetization Transfer ◽  
Multiple Linear Regression Model

Abstract Purpose To assess the correlation of peripheral nerve and skeletal muscle magnetization transfer ratio (MTR) with demographic variables. Methods In this study 59 healthy adults evenly distributed across 6 decades (mean age 50.5 years ±17.1, 29 women) underwent magnetization transfer imaging and high-resolution T2-weighted imaging of the sciatic nerve at 3 T. Mean sciatic nerve MTR as well as MTR of biceps femoris and vastus lateralis muscles were calculated based on manual segmentation on six representative slices. Correlations of MTR with age, body height, body weight, and body mass index (BMI) were expressed by Pearson coefficients. Best predictors for nerve and muscle MTR were determined using a multiple linear regression model with forward variable selection and fivefold cross-validation. Results Sciatic nerve MTR showed significant negative correlations with age (r = −0.47, p < 0.001), BMI (r = −0.44, p < 0.001), and body weight (r = −0.36, p = 0.006) but not with body height (p = 0.55). The multiple linear regression model determined age and BMI as best predictors for nerve MTR (R2 = 0.40). The MTR values were different between nerve and muscle tissue (p < 0.0001), but similar between muscles. Muscle MTR was associated with BMI (r = −0.46, p < 0.001 and r = −0.40, p = 0.002) and body weight (r = −0.36, p = 0.005 and r = −0.28, p = 0.035). The BMI was selected as best predictor for mean muscle MTR in the multiple linear regression model (R2 = 0.26). Conclusion Peripheral nerve MTR decreases with higher age and BMI. Studies that assess peripheral nerve MTR should consider age and BMI effects. Skeletal muscle MTR is primarily associated with BMI but overall less dependent on demographic variables.

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