scholarly journals CONSIDERATIONS ON THE VALIDITY AND APPLICABILITY OF THE UEK METHOD

2019 ◽  
Vol 52 (2) ◽  
Author(s):  
Anna Pajor ◽  
Barbara Kawa
Keyword(s):  
Linear Regression ◽  
Least Squares Method ◽  
Ordinary Least Squares ◽  
Full Column Rank ◽  
Sustainable Rural Development ◽  
Explanatory Variables ◽  
First Case ◽  
Regression Parameters ◽  
The Matrix ◽  
Ordinary Least Squares Method

In Popławski and Kaczmarczyk (2013) a method referred to as UEK was presented and used as a tool in the analysis of sustainable rural development. The purpose of this paper is to demonstrate the methodological inappropriateness of that method. In the linear regression model, the matrix of explanatory variables can have either less than full or full column rank. While all regression parameters are non-estimable in the first case, the well-known and widely used ordinary least squares method can be applied in the second one.

TSS concentration in sewers estimated from turbidity measurements by means of linear regression accounting for uncertainties in both variables

2004 ◽  
Vol 50 (11) ◽  
pp. 81-88 ◽  
Author(s):  
J.-L. Bertrand-Krajewski
Keyword(s):  
Linear Regression ◽  
Least Squares Method ◽  
Ordinary Least Squares ◽  
Regression Method ◽  
Data Series ◽  
Time Interval ◽  
Sewer System ◽  
Regression Parameters ◽  
T Values ◽  
Ordinary Least Squares Method

In order to replace traditional sampling and analysis techniques, turbidimeters can be used to estimate TSS concentration in sewers, by means of sensor and site specific empirical equations established by linear regression of on-site turbidity T values with TSS concentrations C measured in corresponding samples. As the ordinary least-squares method is not able to account for measurement uncertainties in both T and C variables, an appropriate regression method is used to solve this difficulty and to evaluate correctly the uncertainty in TSS concentrations estimated from measured turbidity. The regression method is described, including detailed calculations of variances and covariance in the regression parameters. An example of application is given for a calibrated turbidimeter used in a combined sewer system, with data collected during three dry weather days. In order to show how the established regression could be used, an independent 24 hours long dry weather turbidity data series recorded at 2 min time interval is used, transformed into estimated TSS concentrations, and compared to TSS concentrations measured in samples. The comparison appears as satisfactory and suggests that turbidity measurements could replace traditional samples. Further developments, including wet weather periods and other types of sensors, are suggested.

scholarly journals The effect of production costs on the provisioning management of materials: Evidence from paper industry in Peru

2021 ◽  
pp. 99-106 ◽  
Author(s):  
Luis-Ricardo Flores-Vilcapoma ◽  
Yuri Sanchez-Solis ◽  
Wagner Vicente-Ramos
Keyword(s):  
Linear Regression ◽  
Inventory Management ◽  
Empirical Investigation ◽  
Least Squares Method ◽  
Paper Industry ◽  
Ordinary Least Squares ◽  
Production Costs ◽  
Supply Management ◽  
The Impact ◽  
Ordinary Least Squares Method

This paper presents an empirical investigation to evaluate the impact of purchase, storage and inventory management on the production costs of materials supply management in the Peruvian paper industry. A linear regression model was used under the ordinary least squares method to determine the causal relationship between the provisioning of materials and production costs. It was concluded that the evaluation of the effect between the study variables was inversely proportional, that is, as the management of purchase, storage, and inventory in companies improve, the production costs may also be reduced.

scholarly journals Despesas públicas e o nível de desenvolvimento humano dos estados brasileiros: uma análise do IDHM 2000 e 2010

2018 ◽  
Vol 17 (1) ◽  
pp. 29-54
Author(s):  
Taíse Fatima Mattei ◽  
Fernanda Mendes Bezerra ◽  
Gilmar Ribeiro de Mello
Keyword(s):  
Human Development ◽  
Public Expenditure ◽  
Least Squares Method ◽  
Positive Impact ◽  
Ordinary Least Squares ◽  
Public Expenditures ◽  
Positive Contribution ◽  
Development Level ◽  
Per Capita ◽  
Ordinary Least Squares Method

Resumo: Uma das formas de um governo atuar na economia é na alocação dos gastos públicos. Os gastos públicos têm o objetivo de contribuir para o fornecimento de serviços públicos para a população, e também para a geração de investimentos na economia. Muitas vezes, questiona-se se esses gastos podem, de fato, contribuir para a melhoraria da vida das pessoas. Dessa forma, entender se as despesas públicas têm relação com o nível de desenvolvimento humano é importante para a tomada de decisão dos governantes e para melhorar a gestão dos gastos públicos, principalmente para direcionar em quais áreas devem ser gastos os recursos. Diante disso, o objetivo com este artigo foi verificar qual a contribuição das despesas públicas per capita por funções para o nível de desenvolvimento humano dos estados brasileiros. O método adotado é a regressão múltipla estimada pelo método de mínimos quadrados ordinários utilizando dados em painel. Os dados são provenientes do IBGE, PNUD e IPEA DATA. Os resultados sugerem que algumas despesas possuem contribuição positiva para o desenvolvimento dos estados. Analisando o modelo com todas as despesas na mesma regressão, saúde e educação apresentaram relação positiva para aumentar o IDHM. Analisando os modelos em regressões isoladas, as despesas com assistência e previdência, educação, saúde e segurança e transporte confirmaram impacto positivo sobre o nível de desenvolvimento dos estados. Destaca-se a importância das despesas com saúde e educação, as quais se apresentaram influenciáveis no desenvolvimento nas duas abordagens realizadas.Palavras-chave: Despesas Públicas. Índice de Desenvolvimento Humano. Estados brasileiros. Public expenditure and human development level of Brazilian states: an analysis of IDHM 2000 and 2010 Abstract: One of the ways for a government to act in the economy is in the allocation of public expenditures. Public expenditures aim to contribute to the provision of public services to the population, as well as to the generation of investments in the economy. It is often questioned whether such expenditures can actually contribute to the improvement of people's lives. Thus, understanding whether public expenditures arerelated to the level of human development is important for government decision-making, and to improve the management of public expenditures, mainly to target the areas in which resources should be spent. Therefore, the objective of this article is to verify the contribution of public expenditure per capita by functions to the level of human development of the Brazilian states. The method adopted is the multiple regression estimated by the ordinary least squares method using panel data. The data comes from IBGE, UNDP and IPEA DATA. The results suggest that some expenses have a positive contribution to the development of the states. Analyzing the model with all expenses in the same regression, health and education presented positive relation to increase the HDI. Analyzing the models in isolated regressions, assistance and pension, education, health, safety and transportation expenses confirmed a positive impact on the level of development of the states. It is important to highlight the importance of health and education expenditures, which were influential in the development of the two approaches.Keywords: Public Expenditure. Human Development Index. Brazilian states.

scholarly journals GM(1,1;λ) with Constrained Linear Least Squares

Axioms ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 278
Author(s):  
Ming-Feng Yeh ◽  
Ming-Hung Chang
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Model Fitting ◽  
Ordinary Least Squares ◽  
Linear Least Squares ◽  
Unknown Parameters ◽  
Constrained Problems ◽  
Linear Least Squares Problems ◽  
Ordinary Least Squares Method ◽  
Better Than

The only parameters of the original GM(1,1) that are generally estimated by the ordinary least squares method are the development coefficient a and the grey input b. However, the weight of the background value, denoted as λ, cannot be obtained simultaneously by such a method. This study, therefore, proposes two simple transformation formulations such that the unknown parameters, and can be simultaneously estimated by the least squares method. Therefore, such a grey model is termed the GM(1,1;λ). On the other hand, because the permission zone of the development coefficient is bounded, the parameter estimation of the GM(1,1) could be regarded as a bound-constrained least squares problem. Since constrained linear least squares problems generally can be solved by an iterative approach, this study applies the Matlab function lsqlin to solve such constrained problems. Numerical results show that the proposed GM(1,1;λ) performs better than the GM(1,1) in terms of its model fitting accuracy and its forecasting precision.

The Impact of Money Demand Motivation on Money Supply Indonesia

2021 ◽  
Vol 3 (1) ◽  
pp. 10-16
Author(s):  
Arin Jannah Dinonasih ◽  
Keyword(s):  
Money Supply ◽  
Money Demand ◽  
Least Squares Method ◽  
Negative Relationship ◽  
Ordinary Least Squares ◽  
Significant Negative Relationship ◽  
Precautionary Motive ◽  
The Impact ◽  
Ordinary Least Squares Method ◽  
Annual Period

This study aims to investigate the impact of money demand motive on a money supply based on keyness theory. The method used in this study is the ordinary least squares method with an annual period from 2011 to 2020. We find that In Indonesia, the money demand motive has a significant effect on money supply where the transaction motive has a significant negative relationship with the money supply. A precautionary motive has a significant positive correlation with the money supply. The motive of speculation has a significant positive relationship with the money supply.

Robust Weighted Least Squares Estimation of Regression Parameter in the Presence of Outliers and Heteroscedastic Errors

2014 ◽  
Vol 71 (1) ◽  
Author(s):  
Bello Abdulkadir Rasheed ◽  
Robiah Adnan ◽  
Seyed Ehsan Saffari ◽  
Kafi Dano Pati
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Robust Regression ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Least Square ◽  
Model Parameters ◽  
Constant Variance ◽  
Regression Parameters ◽  
M Estimation

In a linear regression model, the ordinary least squares (OLS) method is considered the best method to estimate the regression parameters if the assumptions are met. However, if the data does not satisfy the underlying assumptions, the results will be misleading. The violation for the assumption of constant variance in the least squares regression is caused by the presence of outliers and heteroscedasticity in the data. This assumption of constant variance (homoscedasticity) is very important in linear regression in which the least squares estimators enjoy the property of minimum variance. Therefor e robust regression method is required to handle the problem of outlier in the data. However, this research will use the weighted least square techniques to estimate the parameter of regression coefficients when the assumption of error variance is violated in the data. Estimation of WLS is the same as carrying out the OLS in a transformed variables procedure. The WLS can easily be affected by outliers. To remedy this, We have suggested a strong technique for the estimation of regression parameters in the existence of heteroscedasticity and outliers. Here we apply the robust regression of M-estimation using iterative reweighted least squares (IRWLS) of Huber and Tukey Bisquare function and resistance regression estimator of least trimmed squares to estimating the model parameters of state-wide crime of united states in 1993. The outcomes from the study indicate the estimators obtained from the M-estimation techniques and the least trimmed method are more effective compared with those obtained from the OLS.

Harmonizing biomass tables by generalized least squares

1985 ◽  
Vol 15 (2) ◽  
pp. 331-340 ◽  
Author(s):  
T. Cunia ◽  
R. D. Briggs
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Weighted Least Squares ◽  
Regression Function ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
Sample Tree ◽  
Tree Data ◽  
Regression Techniques ◽  
Ordinary Least Squares Method

To construct biomass tables for various tree components that are consistent with each other, one may use linear regression techniques with dummy variables. When the biomass of these components is measured on the same sample trees, one should also use the generalized rather than ordinary least squares method. A procedure is shown which allows the estimation of the covariance matrix of the sample biomass values and circumvents the problem of storing and inverting large covariance matrices. Applied to 20 sets of sample tree data, the generalized least squares regressions generated estimates which, on the average were slightly higher (about 1%) than the sample data. The confidence and prediction bands about the regression function were wider, sometimes considerably wider than those estimated by the ordinary weighted least squares.

Personal welfare and intellectual capital: the case of football coaches

2014 ◽  
Vol 15 (1) ◽  
pp. 189-202 ◽  
Author(s):  
Eduardo Tomé ◽  
Iuliia Naidenova ◽  
Marina Oskolkova
Keyword(s):  
Intellectual Capital ◽  
Least Squares Method ◽  
Core Competencies ◽  
Ordinary Least Squares ◽  
Positive Influence ◽  
Content Type ◽  
Football Coaches ◽  
Proxy Indicators ◽  
Personal Welfare ◽  
Ordinary Least Squares Method

Purpose – The purpose of this paper is to present a framework that helps to analyze the dependence between personal welfare and individual (personal) intellectual capital (IIC). The authors also introduce the system of proxy indicator for personal intellectual capital (IC) of football coaches. Design/methodology/approach – This paper employs the idea that personal welfare depends on personal IC, particularly, talent. That is why initially the literature analysis of welfare phenomenon was provided. Then the system of available proxy indicators of football coaches’ IC was designed. To achieve the purpose a linear function is estimated with the help of ordinary least squares method. Findings – The chosen set of IC proxy indicators explain the significant part of coaches’ salary. Such proxies as improvement in the championship table and coach's image in media have a significant and positive influence on coach's salary. Whereas, lowering the position of the club does not considerably affect the coach's wealth. A clubs’ financial capacities and budget also influences coaches’ salaries. Research limitations/implications – Traditional limitation of proxy indicators-based studies is connected with their eligibility and complexity. Practical implications – The possibility to codify IC of a person enables to analyze core competencies necessary in a particular activity or profession for success achievement. Moreover a policy of inequality reduction should take into account that intangible assets are at the base of those persons wealth. Originality/value – This is the first paper that employs IC concept to people wealth while previous literature is dedicated to companies’ or countries’ IC.

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