scholarly journals A linear regression pattern for electricity price forecasting in the Iberian electricity market

2019 ◽  
pp. 117-127
Author(s):  
Ângela Paula Ferreira ◽  
Jenice Gonçalves Ramos ◽  
Paula Odete Fernandes
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Electricity Price ◽  
Price Forecasting ◽  
Electricity Prices ◽  
Market Participants ◽  
Electricity Price Forecasting ◽  
The Impact

The Iberian Market for Electricity resulted from a cooperation process developed by the Portuguese and Spanish administrations, aiming to promote the integration of the electrical systems of both countries. This common market consists of organized markets or power exchanges, and non-organised markets where bilateral over-the-counter trading takes place with or without brokers. Within this scenario, electricity price forecasts have become fundamental to the process of decision-making and strategy development by market participants. The unique characteristics of electricity prices such as non-stationarity, non-linearity and high volatility make this task very difficult. For this reason, instead of a simple time forecast, market participants are more interested in a causal forecast that is essential to estimate the uncertainty involved in the price. This work focuses on modelling the impact of various explanatory variables on the electricity price through a multiple linear regression analysis. The quality of the estimated models obtained validates the use of statistical or causal methods, such as the Multiple Linear Regression Model, as a plausible strategy to achieve causal forecasts of electricity prices in medium and long-term electricity price forecasting. From the evaluation of the electricity price forecasting for Portugal and Spain, in the year of 2017, the mean absolute percentage errors (MAPE) were 9.02% and 12.02%, respectively. In 2018, the MAPE, evaluated for 9 months, for Portugal and Spain equals 7.12% and 6.45%, respectively.

scholarly journals The Impact of Inflation on Economic Growth

2019 ◽  
Vol 8 (1) ◽  
pp. 01-09
Author(s):  
Nexhat Kryeziu ◽  
Esat Ali Durguti
Keyword(s):  
Economic Growth ◽  
Growth Rate ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Serial Correlation ◽  
Inflation Rate ◽  
Positive Impact ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
The Impact

The main purpose of this study is to investigate inflation rate and its impact on the growth rate or to GDP growth for Eurozone countries, using panel data for the period 1997-2017, on an annual basis with a total of 257 observations. For conducting the study, and achieving results, a multiple linear regression model with the least squares regression is used. Moreover, multiple linear regression analysis has been applied in order to investigate whether Inflation rate, as an independent variable, has any significant impact on economic growth. Consequently, in order to test the data used in the model we have applied diagnostic tests, such as Durbin-Watson test to analyze the correlation of serial correlation, as well as the Breusch-Pagan test for heteroskedasticity. The tests’ results give us strong indications that the model has no relation between of serial correlation and there is no heteroskedasticity either. The study conducted shows results generated from the model, and according to the econometric results indicate that Inflation rate has positive impact on the economic growth rate for euro area.

Predictive Factors for Hamstring Autograft Diameter in Anterior Cruciate Ligament Reconstruction

2019 ◽  
Author(s):  
Willem M.P. Heijboer ◽  
Mathijs A.M. Suijkerbuijk ◽  
Belle L. van Meer ◽  
Eric W.P. Bakker ◽  
Duncan E. Meuffels
Keyword(s):  
Anterior Cruciate Ligament ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Acl Reconstruction ◽  
Cruciate Ligament ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Gracilis Tendon ◽  
Thigh Circumference ◽  
Anterior Cruciate

AbstractMultiple studies found hamstring tendon (HT) autograft diameter to be a risk factor for anterior cruciate ligament (ACL) reconstruction failure. This study aimed to determine which preoperative measurements are associated with HT autograft diameter in ACL reconstruction by directly comparing patient characteristics and cross-sectional area (CSA) measurement of the semitendinosus and gracilis tendon on magnetic resonance imaging (MRI). Fifty-three patients with a primary ACL reconstruction with a four-stranded HT autograft were included in this study. Preoperatively we recorded length, weight, thigh circumference, gender, age, preinjury Tegner activity score, and CSA of the semitendinosus and gracilis tendon on MRI. Total CSA on MRI, weight, height, gender, and thigh circumference were all significantly correlated with HT autograft diameter (p < 0.05). A multiple linear regression model with CSA measurement of the HTs on MRI, weight, and height showed the most explained variance of HT autograft diameter (adjusted R 2 = 44%). A regression equation was derived for an estimation of the expected intraoperative HT autograft diameter: 1.2508 + 0.0400 × total CSA (mm2) + 0.0100 × weight (kg) + 0.0296 × length (cm). The Bland and Altman analysis indicated a 95% limit of agreement of ± 1.14 mm and an error correlation of r = 0.47. Smaller CSA of the semitendinosus and gracilis tendon on MRI, shorter stature, lower weight, smaller thigh circumference, and female gender are associated with a smaller four-stranded HT autograft diameter in ACL reconstruction. Multiple linear regression analysis indicated that the combination of MRI CSA measurement, weight, and height is the strongest predictor.

scholarly journals Effects of Total Quality Management (TQM) Dimensions on Innovation—Evidence from SMEs

2021 ◽  
Vol 13 (18) ◽  
pp. 10095
Author(s):  
Marina Godinho Antunes ◽  
Pedro Ribeiro Mucharreira ◽  
Maria Rosário Texeira Justino ◽  
Joaquín Texeira-Quirós
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Continuous Improvement ◽  
Product Innovation ◽  
Research Study ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Process Innovation ◽  
Total Quality ◽  
Innovation Strategies

This research study aims to study and identify which dimensions of TQM have influence on and support innovation strategies within the Portuguese small and medium enterprises (SMEs), in the context of products or services’ innovation and process innovation, as well as to analyze the extent to which this relationship occurs. To examine the linkage between TQM dimensions and innovation strategies, concerning innovation products and innovation processes, a multiple linear regression analysis was chosen and an eight-predictor multiple linear regression model was proposed. The data was collected through a questionnaire sent by email. This research study allows to conclude that several dimensions of TQM, such as benchmarking, quality/conception and product design, and continuous improvement, have a significant and positive association with product innovation. Although the data analysis/measurement of the results dimension has a significant association with product innovation, this association is negative. Conversely, several TQM dimensions, such as leadership/management’s commitment, benchmarking, involvement/empowerment of employees, and continuous improvement, revealed a positive and significant association with process innovation. Our research is of crucial importance for the knowledge of Portuguese SMEs and the fundamental factors that companies must address to both improve their efficiency and be more competitive, thereby increasing profitability and ensuring financial sustainability in the medium and long term.

scholarly journals The Impact of Leadership, Motivation and Discipline on Employee Performance (a Study on Employees of PT Surabaya Marine)

2021 ◽  
Author(s):  
Eva Trisnawati Chumairoh ◽  
Trianggoro Wiradinata
Keyword(s):  
Regression Analysis ◽  
Data Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Employee Performance ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Independent Variables ◽  
Leadership Motivation ◽  
The Impact

This purpose of this study was to determine the effect of leadership, motivation and discipline on employee performance. The independent variables were leadership (X1), motivation (X1) and discipline (X3). The dependent variable was employee performance (Y). This was explanatory research with a quantitative approach. The study was conducted at PT Surabaya Marine with employees. Data analysis was carried out through multiple linear regression analysis with the F test and t test. Employee performance was strongly influenced by leadership, motivation and discipline. Keywords: leadership, motivation, discipline, employee performance

scholarly journals ANALISIS PENGARUH LIKUIDITAS, STRUKTUR AKTIVA, FREE CASH FLOW, DAN UKURAN PERUSAHAAN TERHADAP KEBIJAKAN UTANG (DER) PADA PT JASA MARGA Tbk PERIODE 2009-2018

KEUNIS ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 92
Author(s):  
Anita Kristina ◽  
Prihatiningsih Prihatiningsih ◽  
Ida Savitri Kusmargiani
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Cash Flow ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Multiple Linear Regression Analysis ◽  
Free Cash Flow ◽  
Company Size ◽  
Analysis Model ◽  
Debt Policy

<em>This research is based on the problems of the company PT Jasa Marga Tbk which has a problem with the performance of the company which is indicated by the DER of companie increased during the period 2009-2018. The purpose of this research is to analyze the influance of direction and significance of Liquidity, Asset Structure, Free Cash Flow and Company Size on Debt Policy (DER) in PT Jasa Marga Tbk for the period 2009-2018. The population and sample in this study were PT Jasa Marga Tbk. The analysis model used in this study is a multiple linear regression model. The results of the multiple linear regression analysis showed that (1) the influance of Liquidity (CR) on Debt Policy (DER) was positive and significant (2) the influance Asset Structure on Debt Policy (DER) was negative and not significant (3) the influance of Free Cash Flow on Debt Policy (DER) is positive and significant (4) the influance of Company Size on Debt Policy (DER) is positive and significant.</em>

scholarly journals Development of a Multiple Linear Regression Model for Meteorological Drought Index Estimation Based on Landsat Satellite Imagery

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3393
Author(s):  
Seon Woo Kim ◽  
Donghwi Jung ◽  
Yun-Jae Choung
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Drought Index ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Meteorological Drought ◽  
Drought Indices ◽  
Index Estimation ◽  
Cumulative Precipitation

Climate polarization due to global warming has increased the intensity of drought in some regions, and the need for drought estimation studies to help minimize damage is increasing. In this study, we constructed remote sensing and climate data for Boryeong, Chungcheongnam-do, Korea, and developed a model for drought index estimation by classifying data characteristics and applying multiple linear regression analysis. The drought indices estimated in this study include four types of standardized precipitation indices (SPI1, SPI3, SPI6, and SPI9) used as meteorological drought indices and calculated through cumulative precipitation. We then applied statistical analysis to the developed model and assessed its ability as a drought index estimation tool using remote sensing data. Our results showed that its adj.R2 value, achieved using cumulative precipitation for one month, was very low (approximately 0.003), while for the SPI3, SPI6, and SPI9 models, the adj.R2 values were significantly higher than the other models at 0.67, 0.64, and 0.56, respectively, when the same data were used.

A Mathematical Comparison of the Lachance - Traill Matrix Correction Procedure with Statistical Multiple Linear Regression Analysis in XRF Applications

1992 ◽  
Vol 36 ◽  
pp. 1-10
Author(s):  
Anthony J. Klimasara
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Mathematical Meaning ◽  
Matrix Correction ◽  
Independent Variables ◽  
Xrf Analysis

AbstractIt will be shown that the Lachance-Traill XRF matrix correction equations can be derived from the statistical multiple linear regression model. By selecting and properly transforming the independent variables and then applying the statistical multiple linear regression model, the following form of the matrix correction equation is obtained:Furthermore, it will be shown that the Lachance-Traill influence coefficients have a deeper mathematical meaning. They can be related to the multiple regression coefficients of the transformed system:Finally, it will be proposed that the Lachance-Traill model is equivalent to the statistical multiple linear regression model with the transformed independent variables. Knowing these facts will simplify correction subroutines in Quantitative/Empirical XRF Analysis programs. These mathematical facts have already been implemented and presented in a paper: “Automated Quantitative XRF Analysis Software in Quality Control Applications” (Pacific-International Congress on X-ray Analytical Methods, Hawaii, 1991).This demonstrates that the Lachance-Traill model has a strong mathematical foundation and is naturally justified mathematically.

Quantification of Concrete Waste Generated in Housing Construction Sites via Site Sampling: Development of a Multiple Linear Regression Model

2015 ◽  
Vol 802 ◽  
pp. 676-681
Author(s):  
Siti Hafizan Hassan ◽  
Hamidi Abdul Aziz ◽  
Izwan Johari ◽  
Mohd Nordin Adlan
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Estimation Method ◽  
Multiple Linear Regression Model ◽  
Real Data ◽  
Housing Construction ◽  
Construction Sites ◽  
Average Percentage

Waste generated in construction sites has recently increased and has become an uncontrollable cause of environmental problems and profit loss to contractors. The lack of real data or research on such wastes is due to the lack of suitable policies regarding this issue. The actions of contractors are not controlled by rules on this issue. This situation leads to the lack of action or awareness on the side of the contractor. Concrete waste is also part of the waste generated in construction sites. We determine the concrete waste generated in construction stages and conduct multiple linear regression analysis of the amount of column waste generated. The methodology employed in this study involves site observations, interviews with site personnel, and sampling at housing construction sites. The estimation method is utilized for the sampling of concrete waste. Results show that the average percentage of column waste is 13.93% and that of slab waste is 0.34%. These percentage values are derived from the total order of the concrete. The difference is due to the sizes of structures and method of handling. The regression model obtained from the sample data on column waste resulted in an adjustedR2value of 0.895. Therefore, the model predicts approximately 89.5% of the factors involved in concrete waste generation.

scholarly journals Influence of the key account manager in the provisioning management: Evidence from staple companies during the events of COVID-19

Accounting ◽  
2022 ◽  
Vol 8 (2) ◽  
pp. 161-170 ◽  
Author(s):  
Luis-Ricardo Flores-Vilcapoma ◽  
Cynthia-Paola A lbengrin-Mendoza ◽  
Gabriela-Briggite Gomez-Rojas ◽  
Yuri Sánchez-Solis ◽  
Wagner Vicente-Ramos
Keyword(s):  
Experimental Design ◽  
Linear Regression ◽  
Correlation Analysis ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Cross Sectional ◽  
Key Account ◽  
The Impact

The purpose of this research was to evaluate the degree of influence exercised by the Key Account Manager in the provisioning management in the main companies called Staple in Peru, during the events of COVID-19. The research was of type quantitative, cross-sectional and temporal, with a non-experimental design, using a multiple linear regression model and correlation analysis to determine the impact that exists between the variables. The data belongs to the Industrias San Miguel company, distributed in a weekly period from June 2019 to March 2021, which gives 88 observations. The results allow us to conclude that the Key Account Manager is an important manager of the supply of goods during the crisis caused by COVID-19 in staple companies.

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