scholarly journals Prospects for the creation and use of paired and multiple correlation and regression models in beekeeping

2021 ◽  
pp. 58-63
Author(s):  
O. Galatiuk ◽  
A. Lakhman ◽  
T. Romanishina ◽  
V. Behas
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Air Temperature ◽  
Economic Performance ◽  
Quality Data ◽  
Anthropogenic Factors ◽  
Multiple Correlation ◽  
Correlation And Regression Analysis ◽  
The Relationship ◽  
Natural And Anthropogenic Factors

In animal husbandry, including beekeeping, there are a growing number of independent consultancy services to analyse the performance of the industry in relation to disease monitoring status and preventive measures to maintain proper bee family health. In order to provide expert advice, these services must always be backed up by quality data and accurate statistical analysis. It would give clear instructions on how to interpret the results obtained when processing them, and show directions for improving disease prevention. Currently, there are problems related to improving the control of infectious diseases in bees, as various natural and anthropogenic factors have a multidirectional effect on the economic performance of beekeeping. There are also concerns about the control of infectious animal and insect diseases, which is a multifaceted series of causes due to natural and anthropogenic factors that have a polyvector effect on the economic performance of beekeeping. Therefore, the experimental application of different types of correlation and regression analysis in this industry by constructing pairwise and multivariate dependencies and their statistical interpretation was the aim of the paper. The correlation and regression model under study contains four sets of characteristics: result variable (y) - the amount of honey from 20 different apiaries in one season and factor variables: x1 - air temperature in the apiaries; x2 - amount of probiotic "Enteronormin Iodis + Se" to stimulate the immune system as one of the preventive methods; x3 - number of beehives in each apiary. Linear proportional relationships between apiary productivity and the factors included in the regression model are obtained. According to the results of the correlation-regression analysis, paired correlation coefficients showed that the relationship between air temperature in the apiary and produced honey is medium connection (r1 = 0,666), the relationship between the amount of probiotic applied per frame and produced honey is tight (close) connection (r2 = 0,813), the relationship between the number of beehives and produced honey is medium connection (r3 = 0,633). The regression coefficients show how the amount of honey produced in an apiary changes when each factor changes by one, with the other factors in the equation fixed. So, raising the temperature by 1 °C increases the honey production by 216 kg in each apiary, while increasing the concentration of "Enteronormin Iodis + Se" by 1 cm3 per beehive frame increases the nectar production by 1,12 kg for one hive. The coefficient of multiple determination (R2 = 0,954163) identifies a close relationship in the model created (95% of the factors investigated determine apiary performance). Therefore, modelling in the form of linear and multiple correlation and regression analysis is feasible in beekeeping. Key words: beekeeping, modeling, system analysis, factor and result characteristics.

A Study about Factors Influencing the University Performance

2013 ◽  
Vol 64 (2) ◽  
Author(s):  
Rosman Md. Yusoff ◽  
Faisal Khan ◽  
Asad Mubeen ◽  
Kamran Azam
Keyword(s):  
Regression Analysis ◽  
Empirical Relationship ◽  
Decision Makers ◽  
High Tech ◽  
Data Correlation ◽  
Correlation And Regression Analysis ◽  
Factors Influencing ◽  
University Performance ◽  
The University ◽  
The Relationship

The purpose of this study is to find out the empirical relationship and influence of Research Environment, Integration of the university with Industry, High-tech Employment and Professional & Managerial Development on the University Performance.A Questionnaire has been used to collect the data. Correlation and Regression analysis were used to determine the relationship and influence of identified dimensions over the University Performance.The findings of the study show that the identified dimensions significantly relate and influence the University Performance. This study would be helpful for the university administration while making policies to upgrade its performance. Less work has been done in Pakistan for the development of universities. This study distinctively identifies and represents the variables and their influence over the university performance. The findings increase the value of the study as it would help the decision makers at the universities to think ‘out of the box’.

scholarly journals Policy Assessment Investment Appeal of Innovation Projects Enterprises

2019 ◽  
Vol 9 (2) ◽  
pp. 5111-5122
Keyword(s):  
Regression Analysis ◽  
Practical Experience ◽  
Cash Flows ◽  
Financial Indicators ◽  
Correlation And Regression Analysis ◽  
Proposed Model ◽  
Modern Economic ◽  
The Subject ◽  
The Relationship ◽  
Future Cash Flows

The aim of the article is to summarize theoretical principles and practical experience regarding the relationship between the investment attractiveness of innovative projects and economic growth. The methods of correlation and regression analysis, extrapolation and modeling are used. The subject of the study was the features and patterns of the formation, use and regulation of the policy for assessing the investment attractiveness of company’s innovative projects in modern economic conditions. In the course of the study, an algorithm was developed to assess the size and level of investment attractiveness of the algorithm is based on the allocation of components of the enterprise’s potential. It is indicated that models for evaluating efficiency and cost should take into account not only future cash flows, but also non-financial indicators. Are proposed the construction of a multivariate model based on regression analysis. The essence of this model is to combine the traditional method of correlation analysis with least squares. This approach has the main advantage - relatively high accuracy and low costs in the construction and forecasting. The proposed model of a system for ensuring the investment attractiveness of innovative projects of companies consists of two main subsystems - information-analytical and implementation-control. These blocks provide results that allow you to increase investment attractiveness, as well as timely determine the external and internal risks of the enterpriseThe aim of the article is to summarize theoretical principles and practical experience regarding the relationship between the investment attractiveness of innovative projects and economic growth. The methods of correlation and regression analysis, extrapolation and modeling are used. The subject of the study was the features and patterns of the formation, use and regulation of the policy for assessing the investment attractiveness of company’s innovative projects in modern economic conditions. In the course of the study, an algorithm was developed to assess the size and level of investment attractiveness of the algorithm is based on the allocation of components of the enterprise’s potential. It is indicated that models for evaluating efficiency and cost should take into account not only future cash flows, but also non-financial indicators. Are proposed the construction of a multivariate model based on regression analysis. The essence of this model is to combine the traditional method of correlation analysis with least squares. This approach has the main advantage - relatively high accuracy and low costs in the construction and forecasting. The proposed model of a system for ensuring the investment attractiveness of innovative projects of companies consists of two main subsystems - information-analytical and implementation-control. These blocks provide results that allow you to increase investment attractiveness, as well as timely determine the external and internal risks of the enterprise

CLUSTER REGRESSION ANALYSIS TO DETERMINE THE IMPACT OF GDP ON VOLUNTARY HEALTH INSURRANCE PREMIUS

2021 ◽  
Vol 1 (7) ◽  
pp. 16-22
Author(s):  
N.L. Bushueva ◽  
Keyword(s):  
Regression Analysis ◽  
Health Insurance ◽  
Financial Stability ◽  
Socioeconomic Development ◽  
Medical Insurance ◽  
Voluntary Health Insurance ◽  
Correlation And Regression Analysis ◽  
Key Indicators ◽  
The Impact ◽  
The Relationship

Increasingly, the policy of the Russian Federation is aimed at the development of healthcare, and spending on the healthcare industry is becoming a serious problem for the financial stability of national health systems, even in highincome countries. Voluntary medical insurance, as one of the forms of medical insurance, contributes to the formation of all insurance premiums, thereby this form can be one of the indirect factors of socioeconomic development of Russia. The paper hypothesizes the existence of a relationship between the level of development of the welfare of the country and the volume of the premium fund of voluntary health insurance. A study was conducted on the relationship between voluntary health insurance and GDP, as one of the key indicators of the country's development, through correlation and regression analysis.

Revisiting the Relationship Between Marketing Education and Marketing Career Success

2017 ◽  
Vol 39 (2) ◽  
pp. 109-123 ◽  
Author(s):  
Donald R. Bacon
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Career Success ◽  
Academic Major ◽  
Marketing Education ◽  
Classic Article ◽  
Using Data ◽  
The Relationship

In a replication of a classic article by Hunt, Chonko, and Wood, regression analysis was conducted using data from a sample of 864 marketing professionals. In contrast to Hunt, Chonko, and Wood, an undergraduate degree in marketing was positively related to income in marketing jobs, but surprisingly, respondents with some nonmarketing majors earned about the same as marketing majors in marketing jobs. Satisfaction with a marketing career was not significantly related to academic major. The income regression model explained 30% of the variance in marketing income, which is an improvement over the earlier study, but also indicates that most of the variance in marketing success is not explained by education. Implications are discussed.

scholarly journals Determinants of Capital Structure in Bahrain Stock Market

2016 ◽  
Vol 3 (2) ◽  
pp. 177
Author(s):  
Abdelrhman Ahmad Meero
Keyword(s):  
Regression Analysis ◽  
Correlation Analysis ◽  
Stock Market ◽  
Capital Structure ◽  
Linear Model ◽  
Significant Relationship ◽  
Correlation And Regression Analysis ◽  
Dependent Variables ◽  
The Relationship ◽  
Industrial Investment

The aim of this paper is to examine the determinants of capital structure (profitability, size, risk and growth). The sample is composed of 39 Bahraini firms listed in Bahrain Stock Market. The study covered the period 2011-2015. Correlation and regression analysis have been used to identify the relationship between the capital structure determinants and debt leverages (book leverage and market leverage). Correlation analysis aims to identify this relationship at market level and at sectorial level. Regression analysis objective is to anticipate the models characterizing the relationships between determinants and capital leverages. Results of the analysis shows negative significant relationship between profitability and dependent variables, with more significance relationship with market leverage. This relationship is demonstrated in market level and in insurance and services sectors between profitability and book leverage. When the market leverage is the dependent variable this relationship is valid in market level and in banking, hotels, insurance and services sectors. Positive significant relationship has been found between size and both leverages in market level. Similar result is detected on sectorial level in banking, industrial, investment and services when the dependent variable is book leverage. Size-market leverage relationship is positive and significant also in insurance, investment and services sectors. The relationship risk—book leverage is significant only on sectorial level in Industrial, insurance and investment sectors. In term of market leverage—risk relationship, significant relationship is detected in market level and in investment and services sectors. Regression analysis results present a significant linear model reflecting the relationship between determinants of capital structure and leverages.

Explorations in statistics: regression

2011 ◽  
Vol 35 (4) ◽  
pp. 347-352 ◽  
Author(s):  
Douglas Curran-Everett
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Statistical Regression ◽  
Essential Component ◽  
The Relationship ◽  
Residual Plots

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This seventh installment of Explorations in Statistics explores regression, a technique that estimates the nature of the relationship between two things for which we may only surmise a mechanistic or predictive connection. Regression helps us answer three questions: does some variable Y depend on another variable X; if so, what is the nature of the relationship between Y and X; and for some value of X, what value of Y do we predict? Residual plots are an essential component of a thorough regression analysis: they help us decide if our statistical regression model of the relationship between Y and X is appropriate.

scholarly journals The role of financial awareness for viable and sustainable small-medium enterprises in Kwa-Zulu Natal, Durban

2021 ◽  
Author(s):  
◽  
Thabiso Sthembiso Msomi
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Financial Accounting ◽  
Access To Finance ◽  
Pearson’S Correlation ◽  
Absolute Value ◽  
Pearson's Correlation ◽  
R Value ◽  
Financial Awareness ◽  
The Relationship

The objective of this study is to examine financial awareness for viable and sustainable smallmedium-enterprises in Kwa-Zulu Natal, Durban. In this study, the researcher examined factors for SME sustainability and viability as they influence organisational survival. The specific objectives are outlined as follows: to examine the influence of financial awareness on SME viability and sustainability; to establish the relationship between financial accounting skills and sustainable SMEs; to establish the relationship between financial awareness and financial accounting skills; and to determine the Influences of budgeting and financial awareness on SME sustainability. The quantitative research method was adopted for this study and the purposive sampling technique was chosen to select the participants for this study. The study collected primary data from respondents who are owners of SMEs in the retail, construction, manufacturing sectors, etc. Data was analysed using SPSS. A total of 310 research questionnaires was administered and 304 research questionnaires were returned for analysis (giving a 98% response rate). A regression analysis and Pearson’s correlation analysis were conducted to address the specific objectives of the study. The study identified access to market, access to finance and financial accounting skills as the independent variables, while SME sustainability was the dependent variable of the regression model. The findings suggest that access to finance has the largest absolute value (0.425), which indicates that access to finance uniquely accounts for the larger proportion of the variance in the regression model. The outcome of Pearson’s correlation shows moderate correlation (r value is 0.531) between financial accounting skills and sustainable SMEs. Moreover, there was a weak correlation (r value is 0.457) between financial awareness and financial accounting skills. The outcome of the regression analysis suggests that budgeting has the largest absolute value (0.372), which indicates that budgeting uniquely accounts for the largest proportion of the variance in the regression analysis. The Exploratory Factor Analysis revealed nine factors that are significant to ensure sustainability and viability. The implication of the outcome is that access to finance and budgeting accounts for SME sustainability. Based on the findings from this research, it is recommended that SMEs owners should pay much attention to access to finance and budgeting in running their businesses. Again, employee performance reviews contribute to enhancing the financial accounting skills and knowledge of staff of SMEs as well. They should seek expert or professional advice before taking a loan and they should avoid loan sharks as the interest charged by loan sharks are very high which may lead to potential debt trap. It is suggested that Government agencies should help SMEs to market their products and keep their businesses viable.

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 Anthropogenic and catchment characteristic signatures in the water quality of Swiss rivers: a quantitative assessment

2018 ◽  
Author(s):  
Martina Botter ◽  
Paolo Burlando ◽  
Simone Fatichi
Keyword(s):  
Water Quality ◽  
Anthropogenic Impacts ◽  
Stream Water ◽  
Anthropogenic Activities ◽  
Quality Data ◽  
Anthropogenic Factors ◽  
Water Quality Data ◽  
Natural And Anthropogenic Factors

Abstract. The hydrological and biogeochemical response of rivers carries information about solute sources, pathways, and transformations in the catchment. We investigate long-term water quality data of eleven Swiss catchments with the objective to discern the influence of catchment characteristics and anthropogenic activities on delivery of solutes in stream water. Magnitude, trends and seasonality of water quality samplings of different solutes are evaluated and compared across catchments. Subsequently, the empirical dependence between concentration and discharge is used to classify different solute behaviors. Although the influence of catchment geology, morphology and size is sometime visible on in-stream solute concentrations, anthropogenic impacts are much more evident. Solute variability is generally smaller than discharge variability. The majority of solutes shows dilution with increasing discharge, especially geogenic species, while sediment-related solutes (e.g. Total Phosphorous and Organic Carbon species) show higher concentrations with increasing discharge. Both natural and anthropogenic factors impact the biogeochemical response of streams and, while the majority of solutes show identifiable behaviors in individual catchments, only a minority of behaviors can be generalized across catchments that exhibit different natural, climatic and anthropogenic features.

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