scholarly journals IMPACT OF MACROECONOMIC INDICATORS ON PUBLIC DEBT OF SLOVAK REPUBLIC

2019 ◽  
Vol 20 (4) ◽  
pp. 734-753 ◽  
Author(s):  
Miroslava Knapková ◽  
Martin Kiaba ◽  
Samuel Hudec
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Public Debt ◽  
Slovak Republic ◽  
Simple Linear Regression ◽  
Bond Yields ◽  
Scientific Methods ◽  
Gdp Growth Rate ◽  
Macroeconomic Indicators ◽  
Using Data

The paper focuses on impact of macroeconomic indicators on the development of public debt in Slovakia. The aim of the paper was to identify those macroeconomic indicators which influence the most significantly public debt in Slovakia and to elaborate and verify simple model for public debt prediction. Research was based on the analysis of chosen macroeconomic indicators. Selection of macroeconomic indicators resulted from theoretical knowledge and study of various research papers. Authors used several scientific methods, such as content-causal analysis, comparison, mathematical and statistical methods, including simple linear regression. Macroeconomic indicators, which authors proved to be statistically significant, are GDP growth rate, openness of economy, size of public sector, government bond yields, and unemployment rate. Authors elaborated model of the public debt development in Slovakia by using a simple linear regression model. Regression model was calculated using the data for 1995-2016. Authors confirmed correctness of the model by using data for 2017. Research was limited by the fact, that there are limited data available for analysis (time series of 22 years) because of short existence of independent Slovakia. It will be necessary to continue with the research and to verify correctness of chosen indicators in longer period.

Online estimation of VFA, alkalinity and bicarbonate concentrations by electrical conductivity measurement during anaerobic fermentation

2012 ◽  
Vol 65 (7) ◽  
pp. 1281-1289 ◽  
Author(s):  
Cesar-Arturo Aceves-Lara ◽  
Eric Latrille ◽  
T. Conte ◽  
Jean-Philippe Steyer
Keyword(s):  
Electrical Conductivity ◽  
Hydrogen Production ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Anaerobic Fermentation ◽  
Simple Linear Regression ◽  
Conductivity Measurements ◽  
Bicarbonate Concentration ◽  
On Line

This paper describes the use of electrical conductivity for measurement of volatile fatty acids (VFA), alkalinity and bicarbonate concentrations, during the anaerobic fermentation process. Two anaerobic continuous processes were studied: the first was a laboratory reactor for hydrogen production from molasses and the second was a pilot process for anaerobic digestion (AD) of vinasses producing methane. In the hydrogen production process, the total VFA concentration, but not bicarbonate concentration, was well estimated from the on-line electrical conductivity measurements with a simple linear regression model. In the methane production process, the bicarbonate concentration and the VFA concentration were well estimated from the simultaneous on-line measurements of pH and electrical conductivity by means of non-linear regression with neural network models. Moreover, the total alkalinity concentration was well estimated from electrical conductivity measurements with a simple linear regression model. This demonstrates the use of electrical conductivity for monitoring the AD processes.

scholarly journals The Impact of Public Debt on the Economic Growth of Jordan: An Empirical Study (2000- 2015)

2017 ◽  
Vol 6 (2) ◽  
pp. 114 ◽  
Author(s):  
Tawfiq Ahmad Mousa ◽  
Abudallah. M. LShawareh
Keyword(s):  
Economic Growth ◽  
Empirical Study ◽  
Regression Model ◽  
Least Squares ◽  
Public Debt ◽  
Least Squares Method ◽  
Negative Impact ◽  
External Debt ◽  
Using Data ◽  
The Impact

In the last two decades, Jordan’s economy has been relied on public debt in order to enhance the economic growth. As such, an understanding  of the dynamics between public debt and economic growth is very important in addressing the obstacles to economic growth. The study investigates the impact of public debt on economic growth using data from 2000 to 2015. The study employs least squares method and regression model to capture the impact of public debt on economic growth. The results of the analysis indicate that there is a negative impact of total public debt, especially the external debt on economic growth. 

scholarly journals PM2.5 Concentration Estimation Based on Image Processing Schemes and Simple Linear Regression

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2423 ◽  
Author(s):  
Jiun-Jian Liaw ◽  
Yung-Fa Huang ◽  
Cheng-Hsiung Hsieh ◽  
Dung-Ching Lin ◽  
Chin-Hsiang Luo
Keyword(s):  
Image Processing ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Simple Linear Regression ◽  
Simple Linear Regression Model ◽  
Concentration Estimation ◽  
Single Feature ◽  
The Difference ◽  
Pm2.5 Concentration

Fine aerosols with a diameter of less than 2.5 microns (PM2.5) have a significant negative impact on human health. However, their measurement devices or instruments are usually expensive and complicated operations are required, so a simple and effective way for measuring the PM2.5 concentration is needed. To relieve this problem, this paper attempts to provide an easy alternative approach to PM2.5 concentration estimation. The proposed approach is based on image processing schemes and a simple linear regression model. It uses images with a high and low PM2.5 concentration to obtain the difference between these images. The difference is applied to find the region with the greatest impact. The approach is described in two stages. First, a series of image processing schemes are employed to automatically select the region of interest (RoI) for PM2.5 concentration estimation. Through the selected RoI, a single feature is obtained. Second, by employing the single feature, a simple linear regression model is used and applied to PM2.5 concentration estimation. The proposed approach is verified by the real-world open data released by Taiwan’s government. The proposed scheme is not expected to replace component analysis using physical or chemical techniques. We have tried to provide a cheaper and easier way to conduct PM2.5 estimation with an acceptable performance more efficiently. To achieve this, further work will be conducted and is summarized at the end of this paper.

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