scholarly journals Corporate governance and innovation: Evidence from Bahrain bourse

2016 ◽  
Vol 12 (1) ◽  
pp. 15-25 ◽  
Author(s):  
Hasan Mohamed Hasan Al-Mannaei ◽  
Allam Mohammed Mousa Hamdan
Keyword(s):  
Corporate Governance ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Listed Companies ◽  
The Relationship ◽  
Year 2013

The study aims to assess corporate governance and innovation in selected listed companies at Bahrain Bourse. The study sample included 39 companies in the year 2013. The study built one Linear Regression Model to study the relationship between corporate governance and innovation. After testing the first hypothesis, there is an accepted level of corporate governance in selected listed companies at Bahrain Bourse. And after testing the second hypothesis, there is no relationship between corporate governance and innovation in selected listed companies at Bahrain Bourse, whether the corporate governance is strong in selected listed companies at Bahrain Bourse or not, it has no relationship to Innovation. In Kingdom of Bahrain the innovation is weak due to the fact that Bahrain imports innovation from other countries. The study recommends that all companies listed in Bahrain Bourse to send their employees for special courses on corporate governance, which shows its benefits and to increase their awareness and advises to conduct a workshop of innovation in companies listed in Bahrain Bourse by professional institutes

The Settlement Prediction of Pile Foundation Based on Grey Linear Regression Model

2012 ◽  
Vol 204-208 ◽  
pp. 320-325
Author(s):  
Jia Kun Liu ◽  
Jian Ping Wang ◽  
Min Zhu ◽  
Xiao Jie Hou
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Pile Foundation ◽  
Load Test ◽  
Static Load Test ◽  
Combined Model ◽  
Actual Meaning ◽  
Relationship Of ◽  
The Relationship

Grey linear regression model is a covert grey combined model that is built based on GM(1,1) model and linear regression model. It improves undervaluation of linear regression model which can not in press the exponential growth and come to deficiency of grey GM (1, 1) model which has not linear factor. This paper briefly introduces the establishment and precision examination method of the grey linearity regression model and establishes the grey linear regression model to predict the relationship of load and settlement. Based on the data of static load test, the load-settlement curve is simulated and analyzed. The result of study shows that Grey Linear regression Model can effectively predict the settlement of pile foundation, and be of the theoretical and actual meaning for further analyzing the bearing capability of pile foundation.

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.

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.

scholarly journals Multi-linear regression model for chlorine consumption by waters

2020 ◽  
Vol 26 (4) ◽  
pp. 200402-0
Author(s):  
Guocheng Zhu ◽  
Shanshan Zhang ◽  
Yongning Bian ◽  
Andrew S Hursthouse
Keyword(s):  
Water Quality ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Drinking Water Treatment ◽  
Model Combination ◽  
Water Matrix ◽  
Matrix Properties ◽  
Multi Linear Regression ◽  
The Relationship

In drinking water treatment, disinfection is a key step to ensure the safety of water quality and people's health but little is known of the relationship between chlorine consumption and water matrix properties from varied sources (BWM). In this study, we measured the fluorescence from fractions of NOM (FFN) for the relevant BWM. This included the evaluation of three components: the chlorine-dependence factor (CDF) (DOC and NH3-N), the BWM (such as NO3<sup>-</sup>, NO2<sup>-</sup> and turbidity), and FFN (I-V fluorescence fractions). Multi-linear regression model was used to fit the data. Results showed that when using the CDF, BWM and FNN, in the prediction of chlorine consumption showed the (R<sup>2</sup>) values were 0.72, 0.71 and 0.41, respectively. While the FNN did not fit the model well it did enhance the model using CDF by 11.26%. The FNN is not effective in enhancement of the BWM response to the model. Combination of the CDF, BWM and FNN or that of the CDF and BWM were both effective in prediction of chlorine consumption.

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