scholarly journals Formation a Мathematical Modeling of Unemployment Rate in Russian Federation

2021 ◽  
Vol 3 (4) ◽  
pp. 243-249
Author(s):  
Natalya Antipina ◽  
Marina Seliverstova
Keyword(s):  
Mathematical Modeling ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Regression ◽  
Russian Federation ◽  
Unemployment Rate ◽  
Linear Regression Model ◽  
Regression Method ◽  
Multiple Regression Method ◽  
The Russian Federation

The research analyzed the unemployment rate in the Russian Fe­deration using economic and mathematical modeling. The оbject of the research is the dynamics of the unemployment rate in the Russian Federation in the period from 2005 to 2020, depending on some factors that make an impact on the factor under consideration. As a result of this investigation, carried out using the multiple regression method, a linear regression model was obtained, which can be employed for further analysis and forecasting of the unemployment rate in Russia.

scholarly journals Proving the Efficiency of Alternative Linear Regression Model Based on Mean Square Error (MSE) and Average Width using Aquaculture Data

2019 ◽  
Vol 8 (2S3) ◽  
pp. 377-381
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Mean Square Error ◽  
Linear Regression Model ◽  
Regression Method ◽  
Alternative Methods ◽  
Linear Regression Method ◽  
Mean Square ◽  
Average Width ◽  
Alternative Method

Multiple linear regressions (MLR) model is an important tool for investigating relationships between several response variables and some predictor variables. This method is very powerful and commonly used in finance, economic, medical, agriculture and many more. The main objective of this paper is to compare mean square error (MSE) and the average width between alternative linear regression models and linear regression model. The alternative method in this study is a combination of four methods, namely multiple linear regression method, the bootstrap method, a robust regression method and fuzzy regression through the construction of algorithms by using SAS software. Typically, the alternative method optimized by minimizing the mean square error (MSE) and average width. The results of the study showed a positive improvement for the estimation of parameters generated through these alternative methods

scholarly journals Pengaruh Modal Terhadap Pendapatan Pedagang (Studi kasus: Pasar Angso Duo Kota Jambi)

2020 ◽  
Vol 11 (2) ◽  
pp. 137
Author(s):  
Sudirman Sudirman ◽  
Ubaidillah Ubaidillah
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Regression Method ◽  
Primary Data ◽  
Linear Regression Method ◽  
Simple Linear Regression ◽  
Simple Linear Regression Model ◽  
Positive Effect ◽  
Market Trader

research aims to find out the effect of variable capital on the income of traders in Angso Duo Market Jambi City. The study used primary data by disseminating qusioners to 92 respondents. The method used in this study is a simple linear regression method. while econoetry analysis using a simple linear regression model is carried out to identify the variables that affect. Variables used are capital variables as free variables and income variables as bound variables. The results of the analysis showed that the capital had a positive effect on the earnings of Angso Duo Market traders. This means that if the market trader increases his capital, then the income of the market trader will also increase.

A multiple regression method for estimating Li in tourmaline from electron microprobe analyses

2016 ◽  
Vol 80 (6) ◽  
pp. 1129-1133 ◽  
Author(s):  
A. Pesquera ◽  
P. P. Gil-Crespo ◽  
F. Torres-Ruiz ◽  
J. Torres-Ruiz ◽  
E. Roda-Robles
Keyword(s):  
Linear Regression ◽  
Electron Microprobe ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Structural Formula ◽  
Regression Method ◽  
Cation Deficiency ◽  
Multiple Regression Method ◽  
Normalization Scheme ◽  
Li Content

AbstractLithium cannot be determined by electron microprobe, but it may be an essential component in tourmalinesupergroup minerals. Therefore, its estimation is important for structural formula calculation and nomenclature. In this paper, we present a method to estimate Li content in tourmaline from microprobe data based on a multiple linear-regression model, which is not reliant on a particular normalization scheme. The results derived from this model are reasonably accurate, particularly for low-Mg tourmalines (<2 wt.% MgO) with Li2O contents higher than ∼0.3 wt.%. Furthermore, it provides a better fitness compared with estimations of Li assuming that Li fills any cation deficiency at the Y site.

scholarly journals Analisis pengaruh inflasi, pendidikan dan pertumbuhan ekonomi terhadap tingkat pengangguran terbuka dan kemiskinan di Provinsi Jambi

2021 ◽  
Vol 10 (2) ◽  
pp. 75-82
Author(s):  
Muhammad Azis ◽  
Yulmardi Yulmardi ◽  
Nurhayani Nurhayani
Keyword(s):  
Economic Growth ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Unemployment Rate ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Secondary Data ◽  
Average Development ◽  
Education And Economic Growth

The purposes of this study are 1) To determine and analyze the development of inflation, education, economic growth, open unemployment, and poverty in Jambi Province; and 2) To determine and analyze the effect of inflation, education, and economic growth, on the open unemployment rate and poverty in Jambi Province. The data used in this research is secondary data. The model used in this study is a multiple linear regression model. The results show that the average development of inflation is 11.35%, education is 1.25%, economic growth is -2.64%, TPT is 1.99%, and poverty is -2.74%. Furthermore, the analysis results show that directly inflation and economic growth have a significant effect on TPT in Jambi Province (P<0.05). At the same time, indirectly, only education variables affect poverty in Jambi Province (P<0.05). Keywords: Inflation, Education, Economic growth, Open unemployment, Poverty

scholarly journals The Impact of Fiscal Deficit on Inflation Rate - Empirical Evidence Case of Eurozone

2019 ◽  
Vol 12 (2) ◽  
pp. 1814-1819
Author(s):  
Nexhat Kryeziu ◽  
Esat Durguti
Keyword(s):  
Growth Rate ◽  
Interest Rate ◽  
Linear Regression ◽  
Regression Model ◽  
Unemployment Rate ◽  
Public Debt ◽  
Linear Regression Model ◽  
Inflation Rate ◽  
Government Bond ◽  
The Impact

The purpose of this working paper is to investigate if determinants have an impact on inflation rate in Eurozone Countries by using times series data for 17 countries from year 1997 to 2017, in yearly basis in total 375 observations. The study used quantitative research approach and secondary data and is analyzed by using linear regression model measures: Inflation rate as a dependent variable, and five independent variables such us: GDP to growth rate, Deficit to GDP rate, Public debt to GDP rate, Government bond interest rate and Unemployment rate. Linear regression model was applied to investigate the impact of GDP to growth rate, deficit to the GDP rate, Public debt to the GDP rate, Government bond interest rate, and Unemployment rate to the dependent variable Inflation rate. From the Linear Regression Model coefficients for inflation rate as a dependent variable shows that three of five variables have a significance one with negative significance and two positive significance. The empirical result shows that the three of five ratios that we mentioned above have a strong influence on the Inflation rate.

scholarly journals Multi-criteria approach to pair-multiple linear regression models constructing

2021 ◽  
Vol 21 (1) ◽  
pp. 88-99
Author(s):  
Mikhail P. Bazilevskiy ◽  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Multiple Regression ◽  
Linear Regression Model ◽  
Regression Models ◽  
Multiple Linear Regression Model ◽  
Linear Regression Models ◽  
Deming Regression ◽  
Multiple Linear Regression Models

A pair-multiple linear regression model which is a synthesis of Deming regression and multiple linear regression model is considered. It is shown that with a change in the type of minimized distance, the pair-multiple regression model transforms smoothly from the pair model into the multiple linear regression model. In this case, pair-multiple regression models retain the ability to interpret the coefficients and predict the values of the explained variable. An aggregated quality criterion of regression models based on four well-known indicators: the coefficient of determination, Darbin – Watson, the consistency of behaviour and the average relative error of approximation is proposed. Using this criterion, the problem of multi-criteria construction of a pair-multiple linear regression model is formalized as a nonlinear programming problem. An algorithm for its approximate solution is developed. The results of this work can be used to improve the overall qualitative characteristics of multiple linear regression models.

scholarly journals Descriptive epidemiological study of glaucoma in the Far Eastern Federal District covering the years 2012 to 2019

2021 ◽  
Vol 20 (2) ◽  
pp. 23-29
Author(s):  
L. P. Dogadova ◽  
E. V. Girenok ◽  
E. V. Markelova ◽  
V.  Y. Melnikov
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Epidemiological Study ◽  
Russian Federation ◽  
Federal District ◽  
Coefficient Of Determination ◽  
Magadan Region ◽  
The Russian Federation ◽  
The Republic ◽  
Far Eastern

PURPOSE. To conduct a descriptive epidemiological study of glaucoma in the Far Eastern Federal District covering the years 2012 to 2019.METHODS. The study uses data of the Federal Research Institute for Health Organization and Informatics (FRIHOI) covering the 2012–2019 time period, as well as data from the register of the Unified Interdepartmental Information and Statistical System (UIISS) and the Federal State Statistics Service (FSSS). Statistical data processing was carried out using Microsoft Excel 2019. Diagrams and a cartogram were built to visualize the obtained data. The reliability of the trend line was determined by the value of approximation. A trend is a tendency of changes in the studied time series. In this work, we used a linear approximation — a straight line that best describes the time course of incidence and prevalence. The significance of linear regression was checked using the F-test to determine the quality of the regression model. The coefficient of determination was also used to indicate the dependence of the variability of prevalence on time. A linear regression model was used to predict the prevalence of glaucoma in the Russian Federation and the Far Eastern Federal District; 91% of the total variability of prevalence in the Russian Federation is explained by a change in the time parameter, while 86% in the Far Eastern Federal District indicates a high accuracy of the selection of trend equations.RESULTS. According to the study, in the 2012–2019 years there was a significant increase in the incidence of glaucoma in the Primorsky Krai (PK) amounting to 8%. Over the observed period, a significant increase in the prevalence of glaucoma is noted in the Republic of Buryatia (6.9%), and in the Magadan Region (5%). At the same time, the highest incidence and prevalence of glaucoma was noted in the Republic of Sakha (Yakutia) — 105.4 cases and 1551.6 cases per 100 000 population. The expected prevalence of glaucoma in the Russian Federation (RF) in 2020 is 895–999.7 per 100 000 population, in 2021 — 908–1020.2; in the Far Eastern Federal District (FEFD) in 2020 — 783.7–961.3 per 100 000 population, in 2021 — 799.5–989.8. The largest proportion of glaucoma was found among the population of the Magadan Region (16%) and Yakutia (13.8%), the smallest in the Amur Region (5%) and the Chukotka Autonomous Okrug (5.7%).CONCLUSION. The dynamics of glaucoma incidence in the Far Eastern Federal District is uneven, which corresponds to the epidemiological situation in the Russian Federation as a whole. But the prevalence and proportion of glaucoma in the structure of diseases of the eye and adnexa in the FEFD are characterized by negative dynamics in comparison with country-wide. At the same time, even within the regions of the FEFD, the incidence and prevalence of glaucoma is mosaic, which predisposes to studying the influence of factors on glaucoma incidence.

scholarly journals Multiple Regression-Based Transactions Evidence Timber Appraisal for Minnesota's State Forests

1996 ◽  
Vol 13 (3) ◽  
pp. 129-134 ◽  
Author(s):  
Donald G. MacKay ◽  
Melvin J. Baughman
Keyword(s):  
Linear Regression ◽  
Natural Resources ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Multiple Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Fiscal Year ◽  
Appraisal System ◽  
Sale Price

Abstract A transactions evidence appraisal system for timber tracts administered by the Minnesota Department of Natural Resources was developed and tested. A multiple linear regression model was developed from data on timber tracts sold by the Minnesota Department of Natural Resources at auction in fiscal year 1991. This model was tested on fiscal year 1992 auction sales for which prices were known. Factors related to tract sale price included: (1) volume of different products on the site, (2) tract location, (3) distance from the tract to the nearest mill, (4) stocking level, and (5) seasonal harvesting restrictions. The regression model predicted sale prices nearly as well as the Minnesota Department of Natural Resources appraisal system and required substantially less information. North. J. Appl. For. 13(3):129-134.

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