scholarly journals CONSTRUCTION OF MATHEMATICAL MODELS USING THE METHODS OF CORRELATION AND REGRESSION ANALYSIS

2021 ◽  
pp. 29-36
Author(s):  
Olga Prishchenko ◽  
Nadezhda Cheremskaya ◽  
Tetyana Chernogor
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Regression Analysis ◽  
Functional Relationship ◽  
Functional Dependence ◽  
Mathematical Expression ◽  
Regression Equation ◽  
Statistical Hypothesis ◽  
Correlation And Regression Analysis ◽  
The Relationship

The article discusses the construction of a mathematical model using the methods of correlation and regression analysis in determining the functional relationship between the quantities. When conducting an experiment, it is often necessary to establish the interdependence between two or more quantities in order to obtain an empirical formula. In some cases, this is a simple task, because these connections are almost obvious or known in advance. As a rule, to establish the relationship between different indicators, factors and characteristics is not a trivial task. There is a need to use some hypothesis in the form of functional dependence. In other words, it is necessary to replace this functional dependence with a fairly simple mathematical expression. Such a mathematical expression can be a linear equation or a polynomial. In order to use such experimental data to determine such a mathematical or functional relationship between variables, the methods of correlation and regression analysis are used. Correlation analysis provides an answer to the statistical hypothesis of the absence or presence of a relationship between variables with some predetermined confidence probability. Determination of the functional dependence between different values on their experimental values is carried out using regression analysis. It is based on the well-known method of least squares. Proposing one or another regression equation, the researcher determines both the very existence of the relationship between variables and its mathematical form. Regression analysis considers the relationship between the dependent quantity and non-dependent variables. This relationship is represented using a mathematical model, that is, an equation that connects the dependent and independent variables. Processing of experimental data using correlation and regression analysis allows us to build a statistical mathematical model in the form of a regression equation. Thus, the methods of correlation and regression analysis are closely related.

scholarly journals SPA SERVICES DISTRIBUTION AND SPECIALIZATION IN KUJAWSKO-POMORSKIE, POMORSKIE AND PODKARPACKIE VOIVODESHIPS COMMUNES

Turyzm ◽  
2017 ◽  
Vol 27 (1) ◽  
pp. 57-63
Author(s):  
Paweł Stelmach
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Functional Relationship ◽  
Data Bank ◽  
Data Sets ◽  
Local Data ◽  
Correlation And Regression Analysis ◽  
Central Statistical ◽  
Central Statistical Office ◽  
The Relationship

Abstract The objective of the article is to identify and explain the relationship between spa services distribution and spa specialization in Kujawsko-Pomorskie, Pomorskie and Podkarpackie Voivodeships spa communes. Correlation and regression analysis were used based on data from the Local Data Bank and unpublished data sets from the Central Statistical Office of Poland. In order to explain the relation between spa services distribution and spa specialization, time-series analysis was used. In five of nine researched communes (Horyniec-Zdrój, Solina, Ustka, Ciechocinek and Inowrocław) there is a functional relationship between spa services distribution and spa specialization.

Economic and Mathematical Modeling of Russia’s Economic Security in the Period Under Sanctions

2020 ◽  
Vol 152 ◽  
pp. 32-39
Author(s):  
Galina I. Smirnova ◽  
◽  
Maria E. Listopad ◽  
Keyword(s):  
Mathematical Modeling ◽  
Mathematical Model ◽  
Regression Analysis ◽  
Russian Federation ◽  
Statistical Significance ◽  
Economic Security ◽  
Regression Equation ◽  
Correlation And Regression Analysis ◽  
Time Period ◽  
The Russian Federation

For the study, the time period of introducing economic sanctions against the Russian Federation was selected. In consideration are taken the data of Rosstat in terms of finding the values of indicators of our state’s economic security (2013–2017). A correlation and regression analysis of this system, consisting of 15 indicators, was carried out. An economic-mathematical model of the sanctions impact on the economic security of Russia was compiled. To solve this problem, the authors used a correlation-regression analysis, the regression equation was found and statistical significance of the constructed model was substantiated. The findings were recommended to specialists in the sphere of improving the state’s economic security.

Mathematical Model of the Sintering Process of Iron-Copper Bearings

2007 ◽  
Vol 23 ◽  
pp. 119-122
Author(s):  
Cristina Teișanu ◽  
Stefan Gheorghe ◽  
Ion Ciupitu
Keyword(s):  
Experimental Data ◽  
Mechanical Properties ◽  
Mathematical Model ◽  
Regression Analysis ◽  
Function Optimization ◽  
Chemical Compositions ◽  
Sintering Process ◽  
Molybdenum Disulphide ◽  
Iron Copper ◽  
Antifriction Properties

The most important features of the self-lubricating bearings are the antifriction properties such as friction coefficient and wear resistence and some mechanical properties such as hardness, tensile strength and radial crushing strength. In order to improve these properties new antifriction materials based on iron-copper powders with several additional components (tin, lead and molybdenum disulphide) have been developed by PM techniques. To find the optimal relationship between chemical compositions, antifriction and mechanical properties, in this paper a mathematical model of the sintering process is developed, which highlighted the accordance of the model with data by regression analysis. For the statistical processing of the experimental data the VH5 hardness values of the studied materials were considered. The development of mathematical model includes the enunciation of the model, the establishment of the performance function (optimization) and the establishment of the model equations and verifying. The accordance of the model with experimental data has been highlighted by regression analysis

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.

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.

Automatic Parameter Identification Applied to a Railroad Car Dynamic Draft Gear Model

1974 ◽  
Vol 96 (4) ◽  
pp. 460-465 ◽  
Author(s):  
E. D. Ward ◽  
R. G. Leonard
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Parameter Identification ◽  
Functional Relationship ◽  
Nonlinear Functional ◽  
Draft Gear ◽  
Identification Technique ◽  
Train Dynamics ◽  
Relationship Of ◽  
The Mathematical Model

One of the most important components in simulating track-train dynamics is the mathematical model of the connection between two cars, the draft gear-coupler combination. In this paper an automatic parameter identification technique is presented which can be used to generate a nonlinear functional relationship of dynamic draft gear characteristics using experimental data.

scholarly journals Revelation of a functional dependence of the sum of two uniaxial strengths/hardness on elastic work/total work of indentation

2006 ◽  
Vol 21 (4) ◽  
pp. 895-903 ◽  
Author(s):  
Dejun Ma ◽  
Taihua Zhang ◽  
Chung Wo Ong
Keyword(s):  
Functional Relationship ◽  
Functional Dependence ◽  
Indentation Test ◽  
Fatigue Tests ◽  
Total Work ◽  
Finite Element Analyses ◽  
Instrumented Indentation ◽  
The Relationship ◽  
True Hardness ◽  
Instrumented Indentation Test

Dimensional and finite element analyses were used to analyze the relationship between the mechanical properties and instrumented indentation response of materials. Results revealed the existence of a functional dependence of (engineering yield strength σE,y + engineering tensile strength σE,b)/Oliver & Pharr hardness on the ratio of reversible elastic work to total work obtained from an indentation test. The relationship links up the Oliver & Pharr hardness with the material strengths, although the Oliver & Pharr hardness may deviate from the true hardness when sinking in or piling up occurs. The functional relationship can further be used to estimate the sum σE,y + σE,b according to the data of an instrumented indentation test. The σE,y + σE,b value better reflects the strength of a material compared to the hardness value alone. The method was shown to be effective when applied to aluminum alloys. The relationship can further be used to estimate the fatigue limits, which are usually obtained from macroscopic fatigue tests in different modes.

On the Relationship Between Added Mass and Added Damping

2002 ◽  
Author(s):  
H. G. D. Goyder
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Strong Dependence ◽  
Fluid Pressure ◽  
Theoretical Models ◽  
Added Mass ◽  
Measured Data ◽  
Interesting Consequence ◽  
Phase Angles ◽  
The Relationship

A surprising relationship exists between the amplitude and phase of a vibrating system. For an item vibrating in a fluid flow the phase of the fluid pressure (relative to the motion of the structure) can be deduced from the amplitude of the pressure. This strong dependence of the phase on the amplitude provides a means for checking the correctness of measured data and severely constrains the form that a mathematical model can take. The relationship between amplitude and phase is completely general and the paper gives various examples from theoretical models and experimental data. One interesting consequence of this relationship follows from the observation that phase angles only exist because of damping. Hence damping, due to the fluid, is linked to added mass.

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