THE MODEL FOR MANAGING INVESTMENTS IN THE MOSCOW ENVIRONMENT BASED ON A POWER REGRESSION EQUATION

2020 ◽  
Vol 2 (7) ◽  
pp. 91-99
Author(s):  
E. V. KOSTYRIN ◽  
◽  
M. S. SINODSKAYA ◽  
Keyword(s):  
Solid Waste ◽  
Influencing Factors ◽  
Pair Correlation ◽  
Approximation Error ◽  
Regression Equation ◽  
Regression Equations ◽  
The Impact ◽  
The Relationship ◽  
Average Approximation ◽  
Average Approximation Error

The article analyzes the impact of certain factors on the volume of investments in the environment. Regression equations describing the relationship between the volume of investment in the environment and each of the influencing factors are constructed, the coefficients of the Pearson pair correlation between the dependent variable and the influencing factors, as well as pairwise between the influencing factors, are calculated. The average approximation error for each regression equation is determined. A correlation matrix is constructed and a conclusion is made. The developed econometric model is implemented in the program of separate collection of municipal solid waste (MSW) in Moscow. The efficiency of the model of investment management in the environment is evaluated on the example of the growth of planned investments in the activities of companies specializing in the export and processing of solid waste.

scholarly journals Estimation of Fertility due to Contraception in Nepal

2018 ◽  
Vol 3 (2) ◽  
pp. 137-150
Author(s):  
Bijaya Mani Devkota
Keyword(s):  
Total Fertility Rate ◽  
Contraceptive Use ◽  
Population Policy ◽  
Unmet Need ◽  
Fertility Rate ◽  
Regression Equations ◽  
Background Characteristics ◽  
Unintended Pregnancies ◽  
The Impact ◽  
The Relationship

  Fertility has an important role for demographic transition and total fertility rate (TFR) which is one component measurements of fertility. Absences of complete and reliable data, a large number of indirect techniques have been developed to estimate demographic parameters. Some of these techniques are based on stable population theory and others are regression equations between the dependent variables, the TFR and the independent variables, the socio economic as well as demographic variables. The unwanted or unintended pregnancies can be avoided through the use of contraceptives; it becomes very important to estimate the births averted or pregnancies stopped by use of contraception. Though there is increase in the use of contraception, still many couples do not use contraception in spite of the fact that they require to use contraception. To satisfy this unmet need of contraception is one of the policy targets of national population policy for population stabilization. In this study, 12862 married females between 15-49 years of age, whose marital duration is more than 5 years, have been taken to study the distribution on different background characteristics and their behavior. Firstly, a regression study was done to know the impact on contraceptive use and further multivariate study has been carried out to know the effect of background characteristics and behavior on absence of birth five years jointly at different sub division. This method is based on the relationship between the Total fertility rate (TFR) and contraceptive prevalence rate (CPR).By using this modified estimate of TFR, birth averted for different area. The variables are CPR that about 71.4 percent variation in TFR can be explained by the first regression approach. The second is based on the relationship between total fertility rate (TFR) and Additive combination of CPR and proportion of currently married females having open birth interval (NPV) explained about 82percent of the variation in TFR. The findings revealed that the TFR calculated by the present method are quite close to the observed values of the TFR. Estimates of births averted and the percent change in births in the absence of contraception, based on the two methods are fairly consistent.

scholarly journals Analysis of Stability Factors of Roadway Roof and Determination of Unsupported Roof Distance

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Sen Yang ◽  
Xinzhu Hua ◽  
Xiao Liu ◽  
Chen Li
Keyword(s):  
Influencing Factors ◽  
Geological Conditions ◽  
Mine Roadway ◽  
Flac 3D ◽  
Roof Stability ◽  
Analysis Of Stability ◽  
The Stability ◽  
The Impact ◽  
The Relationship

To determine the impact of influencing factors on unsupported roof stability in coal mine roadway, a mechanical model of the unsupported roof was built. FLAC 3D numerical simulation was utilized to study the stability of the unsupported roof under the influence of the depth of the roadway, the thickness of the roof, and the unsupported-support distance. In view of the key influencing factors, the geological conditions of the site, and the relationship between the tensile stress and tensile strength of the unsupported roof, the maximum unsupported roof distance during roadway excavation was determined. Considering the surplus safety factor of the unsupported roof, the reasonable unsupported roof distance during the excavation of roadway 150802 was finally determined to be 2.08 m. The comprehensive roadway excavation speed increased by 62.7%, achieving a monthly progress over 500 m.

scholarly journals Quality of Service and its Impact in Achieving Competitive Advantage

2019 ◽  
Vol 27 (2) ◽  
pp. 208-227
Author(s):  
Adnan Shamkhi Jabir ◽  
Kamil Shkaier Al.watifi ◽  
Abbas kh. Aljanabi
Keyword(s):  
Quality Of Service ◽  
Competitive Advantage ◽  
Simulation Method ◽  
Petroleum Products ◽  
Regression Equations ◽  
Simple Correlation ◽  
The Status ◽  
The Impact ◽  
The Relationship

This study aims to determine the impact of the (QoS) variable on the competitive advantage variable. To achieve this, the study relied on the quality of service in terms of reliability, responsiveness, flexibility, safety and connectivity and its competitive advantage in terms of cost, quality, flexibility, and delivery. The study tried to answer the questions of the problem, the most important of which is the quality of service in the organization in question? What is the level of competitive advantage offered by the organization in question? Is there a relationship between quality of service and competitive advantage in the organization in question? The study was conducted in the General Company for the distribution of petroleum products / Central Euphrates Distribution Authority / Babel Branch. The questionnaire was used to obtain the necessary data, as well as personal interviews. The opinions of 150 workers were analyzed in addition to 35 customers. The simple correlation coefficient was used to measure the correlation between the variables and the (T) test to determine the significance of the simple and multiple regression equations and also to use the analysis of the (Anova). The simulation method was used to generate data (views) of all the resolution sections that were pre-designed according to the five-dimensional Likert scale for the sample size (n=150). The study sought to achieve a set of objectives, the most important of which is to identify the relationship between quality of service, competitive advantage and the impact of quality of service on the competitive advantage, to make some recommendations that can contribute to improving the status of the organization in question. The study concluded with a number of conclusions, the most important of which is the organization's interest in responding quickly to the customer's needs when requesting service, noting the importance of the organization to provide most of the needs of the citizens of fuel on demand despite the difficult conditions of the country. The most important recommendations included the organization's interest in quickly responding to customers' requests, so it is important for them to maintain and develop these gains.

Heart rate as an indicator of oxygen consumption: influence of body condition in the king penguin

2001 ◽  
Vol 204 (12) ◽  
pp. 2133-2144 ◽  
Author(s):  
G. Froget ◽  
P. J. Butler ◽  
Y. Handrich ◽  
A. J. Woakes
Keyword(s):  
Heart Rate ◽  
Oxygen Consumption ◽  
Body Condition ◽  
Active Oxygen ◽  
The Body ◽  
Regression Equations ◽  
Oxygen Pulse ◽  
Rate Of Oxygen Consumption ◽  
The Impact ◽  
The Relationship

SUMMARY The use of heart rate to estimate field metabolic rate has become a more widely used technique. However, this method also has some limitations, among which is the possible impact that several variables such as sex, body condition (i.e. body fat stores) and/or inactivity might have on the relationship between heart rate and rate of oxygen consumption. In the present study, we investigate the extent to which body condition can affect the use of heart rate as an indicator of the rate of oxygen consumption. Twenty-two breeding king penguins (Aptenodytes patagonicus) were exercised on a variable-speed treadmill. These birds were allocated to four groups according to their sex and whether or not they had been fasting. Linear regression equations were used to describe the relationship between heart rate and the rate of oxygen consumption for each group. There were significant differences between the regression equations for the four groups. Good relationships were obtained between resting and active oxygen pulses and an index of the body condition of the birds. Validation experiments on six courting king penguins showed that the use of a combination of resting oxygen pulse and active oxygen pulse gave the best estimate of the rate of oxygen consumption V̇O2. The mean percentage error between predicted and measured V̇O2 was only +0.81% for the six birds. We conclude that heart rate can be used to estimate rate of oxygen consumption in free-ranging king penguins even over a small time scale (30min). However, (i) the type of activity of the bird must be known and (ii) the body condition of the bird must be accurately determined. More investigations on the impact of fasting and/or inactivity on this relationship are required to refine these estimates further.

The Impact of Factors in Job Satisfaction Towards Employees’ Intention to Leave in Chain Hotels

2015 ◽  
Vol 21 (4) ◽  
pp. 714-718
Author(s):  
Marion Hutagalung ◽  
Tatum Syarifah Adiningrum
Keyword(s):  
Job Satisfaction ◽  
Work Environment ◽  
Influencing Factors ◽  
Employee Turnover ◽  
Turnover Rate ◽  
Intention To Leave ◽  
Factors Affecting ◽  
Job Promotion ◽  
The Impact ◽  
The Relationship

Employee turnover is an expensive cost in the management of any venture. The objective of this paper is to examine the relationship between factors of job satisfaction to employee intention to leave at Arion Swiss Belhotel in Jakarta and Bandung. The survey was conducted to 240 employees in nine departments in both hotels, using a questionnaire adapting JDI. Findings indicated that the highest influencing factors affecting employees’ turnover was the work environment, followed by pay, and opportunity for job promotion. There is no difference between employees in both hotels in their perception towards each of the variables; as well there is no difference between layers of management. The overall finding results can be used for the hotels to set the guidelines to improve the employees’ job satisfaction and reducing the turnover rate.

scholarly journals Use of Modern Regression Analysis in the Dielectric Properties of Foods

Foods ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1472
Author(s):  
Yu-Kai Weng ◽  
Jiunyuan Chen ◽  
Ching-Wei Cheng ◽  
Chiachung Chen
Keyword(s):  
Regression Analysis ◽  
Dielectric Properties ◽  
Influencing Factors ◽  
Polynomial Regression ◽  
Treatment Method ◽  
Regression Equations ◽  
Constant Variance ◽  
Dependent Variables ◽  
Qualitative Variables ◽  
The Relationship

The dielectric properties of food materials is used to describe the interaction of foods with electromagnetic energy for food technology and engineering. To quantify the relationship between dielectric properties and influencing factors, regression analysis is used in our study. Many linear or polynomial regression equations are proposed. However, the basic assumption of the regression analysis is that data with a normal distribution and constant variance are not checked. This study uses sixteen datasets from the literature to derive the equations for dielectric properties. The dependent variables are the dielectric constant and the loss factor. The independent variables are the frequency, temperature, and moisture content. The dependent variables and frequency terms are transformed for regression analysis. The effect of other qualitative factors, such as treatment method and the position of subjects on dielectric properties, are determined using categorical testing. Then, the regression equations can be used to determine which influencing factors are important and which are not. The method can be used for other datasets of dielectric properties to classify influencing factors, including quantitative and qualitative variables.

scholarly journals ANALYZING IMPACT FACTORS OF AIRPORT TAXIING DELAY BASED ON ADS-B DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1251-1255 ◽  
Author(s):  
J. Li ◽  
X. Wang ◽  
Y. Xu ◽  
Q. Li ◽  
C. He ◽  
...  
Keyword(s):  
Influencing Factors ◽  
Delay Time ◽  
Environmental Data ◽  
Impact Factors ◽  
The Impact ◽  
The Relationship

Identifying the factors that cause taxiing delay on airports is a prerequisite for optimizing aircraft taxiing schemes, and helps improve the efficiency of taxiing system. Few of current studies had quantified the potential influencing factors and further investigated their intrinsic relationship. In view of these problems, this paper uses ADS-B data to calculate taxiing delay time by restoring taxiing route and identifying key status points, and further analyzes the impact factors of airport taxiing delay by investigating the relationship between delay time and environmental data such as weather, wind, visibility etc. The case study in Guangzhou Baiyun Airport validates the effectiveness of the proposed method.

DIGITAL COMPETITIVENESS OF UKRAINE: GENERAL TRENDS AND IMPACT MODELING

2018 ◽  
Author(s):  
Nadiya Yavorska ◽  
◽  
Tetyana Danko ◽  
Keyword(s):  
Inverse Relationship ◽  
New Technologies ◽  
Statistical Significance ◽  
Regression Equation ◽  
Coefficient Of Determination ◽  
Multiple Regression Equation ◽  
Individual Factors ◽  
Decisive Influence ◽  
The Impact ◽  
The Relationship

The object of the study is the digital competitiveness of the country and its impact on GDP. The paper summarizes the methodology for determining the rating of global digital competitiveness and investigates the impact of digital competitiveness on GDP using econometric analysis methods. The methodological basis of the study was the fundamental principles of economic theory, statistics and econometrics. To develop a statistical model of the relationship between digital competitiveness and GDP, correlation analysis was performed using the pairwise regression equation, and to influence individual factors - a linear multiple regression equation. The parameters of the constructed models by the method of least squares are estimated and their statistical significance is checked. The results of the study show that there is a close inverse relationship between the rating on the Digital Competitiveness Index and GDP. This is due to the fact that the linear correlation coefficient is -0.819, and the value of the coefficient of determination (0.6712) shows the decisive influence of digital competitiveness on GDP. Verification of the statistical significance of the constructed model allowed to recognize it as statistically reliable, which allows to use it for forecasting. Instead, the resulting econometric model of the relationship between individual factors of digital competitiveness rating and GDP is characterized by a strong inverse relationship between the two factors "Knowledge" and "Technology" and a direct relationship between the factor "Readiness for the future". The factor of "Knowledge", which characterizes the process of digital transformation of Ukraine through understanding, studying and creating new technologies, has a decisive influence on the volume of GDP. The developed model of the relationship between individual factors of digital competitiveness rating and GDP, as adequate and statistically significant, can be used for further analysis and forecasting. It is proved that the process of digitalization is an urgent need for the existence of the economic system at present, namely the introduction of digital technologies can increase the competitiveness of the country on the world stage.

Agriculture under Climate Change in the Northern Territories of Russia

2020 ◽  
pp. 41-46
Author(s):  
A.S. Shcherbakova ◽  
Keyword(s):  
Climate Change ◽  
Pair Correlation ◽  
Climatic Data ◽  
Ordinary People ◽  
Intergovernmental Panel ◽  
The North ◽  
Northern Territories ◽  
Relationship Of ◽  
The Impact ◽  
The Relationship

A special report published in October 2018 by the Intergovernmental Panel on Climate Change on the effects of global warming at 1.5 °C caused another resonance among the scientific community, experts, politicians and ordinary people [20]. It has been prove that northern territories are most affect by climate change. Because of this report, it becomes relevant to study the impact of climate change on agriculture in the North, which is the most climate-dependent in comparison with other sectors of the economy. The work is devoted to assessing the impact of agro-climatic indicators on productivity and gross harvest of the main agricultural crops of some regions of the Far North and equivalent areas for 1960-2018. The analysis of the relationship of pair correlation between the yield of cereals, potatoes, vegetables and selected climatic indicators relating to the growing season is carry out. Agro-climatic resources for half a century of time in the studied regions are analyzed. Each region was considered in the context of the available meteorological stations and their climatic data.

scholarly journals Regression analysis to predict the impact of dietary neutral detergent fiber on carcass yield in swine1

2019 ◽  
Vol 3 (4) ◽  
pp. 1270-1274 ◽  
Author(s):  
Jose A Soto ◽  
Mike D Tokach ◽  
Steve S Dritz ◽  
Márcio A D Gonçalves ◽  
Jason C Woodworth ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Large Intestine ◽  
Negative Impact ◽  
Statistical Significance ◽  
Regression Equation ◽  
Neutral Detergent Fiber ◽  
Yield Reduction ◽  
Regression Equations ◽  
Carcass Yield ◽  
The Impact

Abstract Research has shown that carcass yield in swine is reduced when ingredients with high neutral detergent fiber (NDF) content. Carcass yield reduction from feeding high-fiber ingredients results from an increase in the weight of intestinal contents. NDF has been shown to result in the digestive contents to swell in the large intestine by absorbing water thus increasing the fecal volume in the large intestine. Considering the financial implications of changing carcass yield, the objective of this project was to develop a regression equation to estimate carcass yield from dietary NDF and strategies where high-NDF ingredients are taken out of the diet in the last dietary phases before slaughter (withdrawal period; WP). Data from 8 experiments (43 observations) originated from 6 journal articles and 1 technical memo were used to develop the regression equation. The WP of high NDF ingredients was either none or ranged from 5 to 63 d in the experiments. Treatment diets of each trial were reformulated to obtain dietary nutrient content using the NRC ingredient library (NRC, Nutrient requirements of swine. 11th ed, 2012). Composition of experimental diets was used to calculate dietary net energy, crude protein, crude fiber, NDF, and acid detergent fiber in the last two dietary phases. These dietary compositions along with the number of days of WP were used to develop regression equations. The model was determined using a step-wise selection procedure starting with guided forward selection through individual predictor variables, with a statistical significance at P < 0.05 used to determine inclusion of terms in the final model. The regression analysis showed that WP, NDF level in the dietary phase prior to the final phase (NDF1), NDF level in the last dietary phase before marketing (NDF2), and the interaction between NDF2 and WP were the most important variables in the dataset to predict carcass yield. The resulting regression equation was as follows: carcass yield, % = 0.03492 ± 0.02633 × WP (d) – 0.05092 ± 0.02862 × NDF1 (%) – 0.06897 ± 0.02931 × NDF2 (%) – 0.00289 ± 0.00216 × (NDF2 [%] × WP [d]) + 76.0769 ± 1.33730. In conclusion, high levels of NDF up to slaughter had a negative impact on carcass yield. Increasing the length of the WP improved carcass yield; however, the effect of WP was dependent on the level of NDF2. The equation herein provides a tool to estimate of the impact of dietary NDF on carcass yield.

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