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.

A Mathematical Comparison of the Lachance - Traill Matrix Correction Procedure with Statistical Multiple Linear Regression Analysis in XRF Applications

1992 ◽  
Vol 36 ◽  
pp. 1-10
Author(s):  
Anthony J. Klimasara
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Mathematical Meaning ◽  
Matrix Correction ◽  
Independent Variables ◽  
Xrf Analysis

AbstractIt will be shown that the Lachance-Traill XRF matrix correction equations can be derived from the statistical multiple linear regression model. By selecting and properly transforming the independent variables and then applying the statistical multiple linear regression model, the following form of the matrix correction equation is obtained:Furthermore, it will be shown that the Lachance-Traill influence coefficients have a deeper mathematical meaning. They can be related to the multiple regression coefficients of the transformed system:Finally, it will be proposed that the Lachance-Traill model is equivalent to the statistical multiple linear regression model with the transformed independent variables. Knowing these facts will simplify correction subroutines in Quantitative/Empirical XRF Analysis programs. These mathematical facts have already been implemented and presented in a paper: “Automated Quantitative XRF Analysis Software in Quality Control Applications” (Pacific-International Congress on X-ray Analytical Methods, Hawaii, 1991).This demonstrates that the Lachance-Traill model has a strong mathematical foundation and is naturally justified mathematically.

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 The Effect of Phase on Paper Thickness Optimization in the Paper Recycling Process Using Recycling Machine

2020 ◽  
Vol 2 (1) ◽  
pp. 19
Author(s):  
Fauzhia Rahmasari
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Multiple Linear Regression Model ◽  
Thickness Optimization ◽  
Recycling Process ◽  
Paper Recycling ◽  
Independent Variables ◽  
The Relationship

AbstractEfforts to manage the recycling of paper waste into new paper have been carried out in recent times. It takes a tool or machine that is able to effectively and efficiently recycle used paper into new paper. There are several factors that affect the effectiveness of paper recycling machines, one of which is the paper thickness. One method that can be used to analyze the factors that influence paper thickness in the paper production process using a paper recycling machine is regression analysis. Regression analysis is data analysis techniques in statistics that is used to examine the relationship between several independent variables and dependent variable. However, if we want to examine the relationship or effect of two or more independent variables on a dependent variable, the regression model used is a multiple linear regression model. This study purposes are to analyze the factors that influence paper thickness using a paper recycling machine using multiple linear regression and to inform the modeling about that. The results showed that the factors that affect the paper thickness optimization are destruction and press phase. AbstractUpaya pengelolaan daur ulang sampah kertas menjadi kertas baru telah banyak dilakukan pada jaman sekarang. Dibutuhkan suatu alat atau mesin yang mampu secara efektif dan efisien dalam mendaur ulang kertas bekas menjadi kertas baru. Terdapat beberapa faktor yang mempengaruhi tingkat efektifitas mesin daur ulang kertas diantaranya adalah ketebalan kertas. Salah satu metode yang dapat digunakan untuk menganalisis faktor-faktor yang mempengaruhi ketebalan kertas pada proses produksi kertas menggunakan mesin daur ulang kertas adalah analisis regresi. Analisis regresi merupakan teknik analisis data dalam statistika yang digunakan untuk mengkaji hubungan antara beberapa variabel bebas dengan variabel tidak bebas. Namun, jika ingin mengkaji hubungan atau pengaruh dua atau lebih variabel bebas terhadap satu variabel tidak bebas, maka model regresi yang digunakan adalah model regresi linier berganda. Tujuan dalam penelitian ini yaitu menganalisis faktor-faktor yang mempengaruhi ketebalan kertas menggunakan mesin daur ulang kertas menggunakan regresi linier berganda serta memberikan informasi pemodelan mengenai hal tersebut. Hasil penelitian menunjukkan bahwa faktor yang mempengaruhi keoptimalan ketebalan kertas adalah fase penghancuran dan pemadatan kertas

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.

Canine Parvoviral Enteritis and Its Determinants-An Epidemiological Analysis

2021 ◽  
Author(s):  
M. Geetha ◽  
G. Selvaraju
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Tamil Nadu ◽  
Multiple Linear Regression Model ◽  
Age Group ◽  
Environmental Determinants ◽  
Disease Occurrence ◽  
The Relationship

Background: Canine parvoviral enteritis (CPVE) is a highly contagious disease of dogs of less than two years age group characterized by vomiting, haemorrhagic foul smelling diarrhoea, high grade pyrexia, dehydration and followed by death. The disease is caused by Canine parvovirus type-2 (CPV-2) and its variants, CPV-2a, 2b and 2c. Environmental and host determinants are playing an important role in the occurrence of CPVE in dogs. Limited numbers of research studies have been were conducted on the role of the determinants associated with the disease occurrence. Hence, the present study was aimed to assess the influence of host and environmental determinants associated with the incidence of CPVE in dogs. Methods: Retrospective data on the incidence of CPVE in Namakkal region, Tamil Nadu was collected (2017-2019) from Veterinary Clinical Complex (VCC), Veterinary College and Research Institute (VC and RI), Namakkal, Tamil Nadu and had been subjected to temporal and spatial clustering and regression analysis. One hundred and twenty three faecal samples were collected from dogs with clinical signs of CPVE and subjected to PCR using H primer of CPV. Cross-sectional study was used to investigate the relationship between the disease and hypothesized causal factors. Relative risk, odds ratio were used to determine the causal association. Weather data was collected for the period from 2017-2019 from Animal Feed Analytical and Quality Control Laboratory (AFAQAL), VC and RI, Namakkal to assess the relationship of disease occurrence with the environmental determinants. Multiple linear regression model was developed for prediction of CPVE by correlation of environmental determinants with the occurrence of CPVE. Result: Temporal analysis revealed endemic pattern of CPVE started last week of April, peaks in June and ends in August and second peak was noticed at November month. Higher incidences ( greater than 70%) were noticed in males and less than 6 months age group dogs. Polymerase chain reaction for confirmation of CPV infection in dogs revealed the positivity of 70.73%. Analysis of risk factors associated with CPVE revealed that vaccination, roaming of dogs, maternal vaccination and early weaning having positive statistical association with the incidence of CPVE. Multiple linear regression model revealed that relative humidity is positively associated with the occurrence of CPVE in dogs. Vaccination of dogs against CPV and administration of boosters at regular intervals, weaning of dogs after 45 days of age are used as primary strategies for prevention of CPVE.

FACTORS ASSOCIATED WITH CHILDREN’S HEIGHT AND BODY MASS INDEX IN LITHUANIA, 1990–2008

2015 ◽  
Vol 48 (4) ◽  
pp. 502-529 ◽  
Author(s):  
Andrej Suchomlinov ◽  
Janina Tutkuviene
Keyword(s):  
Body Mass Index ◽  
Linear Regression ◽  
Regression Model ◽  
Socioeconomic Factors ◽  
Multiple Linear Regression ◽  
Birth Order ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Factors Associated ◽  
Independent Variables

SummaryThe aim of the study was to reveal the ethnic and socioeconomic factors associated with height and body mass index (BMI) of children during the period of political and social transition in Lithuania in 1990–2008. Data were derived from the personal health records of 1491 children (762 boys and 729 girls) born in 1990 in Vilnius city and region. Height and BMI from birth up to the age of 18 years were investigated. Children were divided into groups according to their ethnicity, place of residence, father’s and mother’s occupation and birth order. Height and BMI were compared between the groups; a Bonferroni correction was applied. A multiple linear regression model was used to measure the effects of the independent variables on height and BMI. Girls living in Vilnius city were significantly taller in later life at the ages of 8 and 11 years. Sons of mothers employed as office workers appeared to be significantly taller at the ages of 7, 12, 14 and 15 years compared with the sons of labourers. First-born girls were taller at the age of 7 years than later-born girls of the same age (124.48±5.11 cm and 122.92±5.14 cm, respectively,p<0.001). Later-born children of both sexes had higher BMIs at birth compared with first-borns; however, first-born girls had higher BMIs at the age of 11 years compared with their later-born peers (17.78±2.87 kg/m² and 16.79±2.14 kg/m² respectively,p<0.001). In the multiple linear regression model, the five tested independent variables explained only up to 18% of total variability. Boys were more sensitive to ethnic and socioeconomic factors: ethnicity appeared to be a significant predictor of boys’ height at the age of 5 years (p<0.001), while birth order (p<0.001) predicted boys’ BMI at birth. In general, ethnicity, place of residence, father’s and mother’s occupation and birth order were not associated with children’s height and BMI in most age groups.

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