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EN
There are many discussions and arguments about accuracy of the results of statistic regression analysis and neural network regression analysis among experts. Of course all of them look for the best method usable in practice. The objective of the contribution is to use a case study to answer the question which of the methods provides better results. As a case study will be used time series of US Gross Domestic Product. It starts in 1966 and ends in 2014. The future development of the variable for the following 20 years will be calculated. Software Statistica 12 developed by Dell corporation will be used for both analyses. The first one will use multiple regression of the software. The second one will use data mining section with its neural networks. Generalized Regression Neural Networks, Multi-layer Perceptron Network, Radial Basis Function Neural Networks, Linear Neural Networks will by calculated. The results are two curves, the first based on statistic regression analysis. The second curve is provided by the best model of neural networks. Both the curves will describe development of the US GDP in 20 next years.
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The aim of this paper is to create and in terms of the Czech Republic also to apply a simultaneous model for determination of macroeconomic impacts of the trading of emission allowances. Another aim is to determine its adequateness of application both from econometric and economic point of view, or alternatively to define its limits and restrictions. At first, prerequisites and characteristics of the model are defined. The application of the model in terms of the Czech Republic is performed next followed by economic and econometric verification. The conclusion of the paper contains evaluation of the impacts of the changes in revenue from allowances on select basic macroeconomic indicators, as for instance inflation, unemployment, government purchases or net export. In case revenue from trading of emission allowances in the Czech Republic rise by 1 billion CZK, unemployment falls by 0.15 % and the government expenses will increase by approximately 1.032 billion CZK and net export will increase as well, but only by 194 million CZK. Regarding inflation, the result of simultaneous model or the paired regression is the fact that revenue from allowances and inflation are independent on each other.
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Calculations carried out made it possible to characterize changes in the accident hazard, in numerical terms, based on the course of the regression line. An analysis of the distribution, by regions, of the accident hazard and, above all, the showing of changes in this hazard is an important contribution to the actions taken to improve the road transport system. The rate of decrease in the number of accidents undergoes changes, which are particularly conspicuous in some voivodships (provinces) against the background of the general process of decline in the nationwide number of accidents. A graph to characterize this hazard has been presented, plotted with taking into account the changes in the number of accidents, recorded in monthly steps for a period of 7 years. The rate of these changes was also evaluated, in medium-term and short-term intervals. The calculation results were used to ascertain whether a decline in the total number of accidents in Poland is accompanied by similar changes taking place in individual regions. The symptoms of increasing share of the number of accidents that occur in a few regions in the total number of accidents taking place in Poland may be a cause for alarm.
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The compensation of thermal errors in machine tools is one of the major challenges in ensuring positioning accuracy during cutting operations. There are numerous methods for both the model-based estimation of the thermal tool center point (TCP) deflection and for controlling the thermal or thermo-elastic behavior of the machine tool. One branch of thermal error estimation uses regression models to map temperature sensors directly onto the TCP-displacement. This can, e.g., be accomplished using linear models, artificial neural networks or characteristic diagrams. One of the main limitations of these models is the poor extrapolation behavior with regard to untrained load cases. This paper presents a new method for updating characteristic diagram based compensation models by combining existing models with new measurements. This allows the optimization of the compensation for serial production load cases without the effort of computing a new model. The new method was validated on a 5-axis machining center.
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This paper presents the results of investigating harmonic levels on medium voltage motors at loading conditions in air separation plants. The essential results of the measurement of the medium voltage motor harmonics are summarized in the values for the total harmonic distortion (THD). Motors loading case is used to assess the current and voltage harmonic distortions. Proper system analysis is important when adding any new motor starting and controlling equipment It will be able to better suggest the most appropriate starting and control method. Two medium voltage motors of air separation unit' measurement results and simulations are summarized. Both current and voltage harmonic distortions are fitted linear regression model. The predicting THD values can be used for this kind of process for future planning.
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The aim of the article is to present how to carry out the classical and logistic regression analyses in sample surveys, describing the socio-economic phenomena, to which complex a sample was drawn. The object of the study are households in Poland, surveyed in the household budgets survey, conducted annually by the Central Statistical Office. The essence of the methods analysis of complex samples is based on taking into account an appropriate design sampling scheme in the estimation which includes stratification, weigh-ing, multistage sampling and adjustments for non-sampling errors. Parameters’ estimates and their variances’ estimates which measure the precision of the parameters’ estimates are different when using appropriate procedures for complex samples from the results which would be obtained if the procedures for simple sample were used. The SAS procedures for regression from complex samples were applied. It was possible due to the significant advances in computational techniques including the development of modeling software as well as increase its availability to users.
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Regression analysis is perhaps the best known and most widely used method used for the analysis of dependence; that is, for examining the relationship between a set of independent variables (X’s) and a single dependent variable (Y). In general regression, the model is a linear combination of independent variables that corresponds as closely as possible to the dependent variable [Lattin, Carroll, Green 2003, p. 38]. The aim of the article is to present two suitable adaptations for a regression analysis of symbolic interval-valued data (centre method and centre and range method) and to compare their usefulness when dealing with noisy variables and/or outliers. The empirical part of the paper presents the results of simulation studies based on artificial and real data, without noisy variables and/or outliers and with noisy variable and outliers. The results are compared according to the values of two coefficients of determination 2 RL and 2 . RU The results show that usually the centre and range method obtains better results even when the data set contains noisy variables and outliers, but in some cases the centre method obtains better results than the centre and range method.
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Content available remote Prediction of vo2max based on age, body mass, and resting heart rate
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Purpose. The aim of the present study was to develop a non-exercise regression model for predicting maximal oxygen uptake (VO2max) using age, body mass, and resting heart rate as predictor variables. Methods. The VO2max of 1502 active football players aged 16-35 years was measured using the Astrand Bike Test. The obtained data were analyzed by calculating basic statistical parameters and performing correlation and regression analysis. Results. The results of regression analysis indicated that all three independent variables could significantly (p = 0.000) predict the VO2max of the studied athletes. Measured VO2max showed significant correlation (0.688) with predicted VO2max. Student’s paired samples t test indicated no significant differences between measured VO2max and predicted VO2max (p = 0.782). Conclusions. The results suggest that the nonexercise variables of age, body mass, and resting heart rate, may significantly predict the endurance abilities of athletes (VO2max).
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Content available remote New methods of dating in archaeology
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A modification of statistical method to determine the relative age of wood samples based on the analysis of dendroseries has been developed. The main difference of the method proposed here from the one used earlier lies in the fact that there is the possibility to match several samples simultaneously. Using the methods of statistical analysis, we have the possibility to ascertain what is reliability of determination of the relative age. Approaches used in our method are typical for regression analysis. The correlation coefficient between the analyzed sample and the group of matching samples (the comparison groups) serve as a measure concordance. It is so-called multiple correlation coefficient. The Fisher F-statistics is used to determine the significance of matching. The method is applied for cross-dating of wood samples of archaeological monuments in the Southern Siberia.
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It is a well-known problem of milling machines, that waste heat from motors, friction effects on guides, the environment and the milling process itself greatly affect positioning accuracy and thus production quality. An economic and energy-efficient method of correcting this thermo-elastic positioning error is to gather sensor data (temperatures, axis positions, etc.) from the machine tool and the process and to use that information to predict and correct the resulting tool center point displacement using regression analysis. This paper compares multilinear characteristic diagrams, B-spline characteristic diagrams, Radial Basis Function fitting and Wavelet fitting in general and also in the context of thermal error compensation. The demonstrations are made using FEM simulation data from a machine tool demonstrator. The results show that all of the above kernel types, if properly used, are able to create good compensation models. However, high-dimensional multivariate analysis usually only works by adding grid structures and regularization.
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Content available remote Simulation approach to optimal stopping in some blackjack type problems
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In the paper, an unbounded blackjack type optimal stopping problem is considered. A decision maker (DM) observes sequentially the values of an infinite sequence of nonnegative random variables. After each observation, the DM decides whether to stop or to continue. If the DM decides to stop at a given moment, the obtains a payoff dependent on the sum of already observed values. The greater the sum, the more the DM gains, unless the sum exceeds a given positive number. If so, the decision maker loses all or part of the payoff. It turns out that under some elementary assumptions the optimal stopping rule (OSR) for such a problem has a very simple, so-called threshold form. However, even in very simple cases, the value of the problem has no closed analytical form. Therefore, it is very hard to evaluate the value directly. Thus, in order to find the relationship between the problem design parameters and the value of the problem, is proposed studying the relation via Monte Carlo simulations combined with regression analysis The same approach is adopted to examine the OSR risk characteristics.
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Content available Predicting students performance in giant slalom
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The aim of this research is to determine the impact of specific motor knowledge of alpine skiing on success in giant slalom race of students. On a sample of 18 students of the Faculty of Physical Education and Sport there has been used set of four variables of specific motor knowledge of alpine skiing, as predictor variables, to determine the impact on the criterion variable modified giant slalom race. It was found that the variables dynamic long radius turns and skiing with the changes of rhythm and tempo together have a greatest predictor validity, at statistically significant level of p = 0.01, and that they are critical for success in modified giant slalom race of students. It can be concluded that the impact on the result in the modified giant slalom race for students have the level of mastering of advanced elements of ski technique, which at this level of knowledge is crucial in giant slalom competition. The results of this study may be of importance in creating programs for different levels of mastering of skiing techniques in both advanced ski school and some stages of competitive skiing, which is of great importance for the result in giant slalom and skiing in general.
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Credit risk is the most important risk among all other risks in the banking business, because almost over 80% of bank balance sheets relate to this segment of banking risk management. One of the biggest problems of commercial banks in Bosnia and Herzegovina are non-performing loans whose share in total loans has increased significantly since the onset of the global financial crisis. The main objective of the research is to determine which of the macroeconomic variables have the strongest impact on the increase of return on average equity and whether it is possible to reduce the credit risk of banks with adequate legislation as the main factor in the slowdown in credit expansion. The main goal will be to divide the impact of an independent variable, i.e. the share of liquid assets in total assets and whether its increase indirectly affects the return on equity and indirectly, the credit risk. The quantitative model used in this study will be the Merton model. Testing will be conducted through multiple regression analysis for the period 2008-2016 with the help of the software package STATA.
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Content available remote Linear-wavelet networks
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This paper proposes a nonlinear regression structure comprising a wavelet network and a linear term. The introduction of the linear term is aimed at providing a more parsimonious interpolation in high-dimensional spaces when the modelling samples are sparse. A constructive procedure for building such structures, termed linear-wavelet networks, is described. For illustration, the proposed procedure is employed in the framework of dynamic system identification. In an example involving a simulated fermentation process, it is shown that a linear-wavelet network yields a smaller approximation error when compared with a wavelet network with the same number of regressors. The proposed technique is also applied to the identification of a pressure plant from experimental data. In this case, the results show that the introduction of wavelets considerably improves the prediction ability of a linear model. Standard errors on the estimated model coefficients are also calculated to assess the numerical conditioning of the identification process.
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Nowadays predicting transportation costs is more and more important. Most significant part of inland navigation’s costs are the costs of fuel. Fuel consumption is related to operating conditions of ship’s propulsion system and its resistance. On inland waterways the ship resistance is strictly related to the depth of the waterway. There is a tendency to build a formula that allows its user to calculate the resistance of any inland waterway vessel, but researches claim that most of them are accurate only for particular types of ships and/or operating conditions. The paper presents selected methods of calculating ship resistance on inland waterways. These methods are examined for different types of ships and different conditions using results of model tests. The performed comparison enabled selecting the best option for pushboats and pushed barge trains, but also showed that any of the tested methods is good enough to be used for calculating the resistance of motor cargo vessels. For this reason, based on known equations and using the regression method, the authors have formulated a new method to calculate the resistance of motor cargo vessels on limited waterway. The method makes use of ship’s geometry and depth of waterway in relation to ship’s speed. Correlating the ship’s speed with its resistance and going further with fuel consumption, enables to calculate the costs of voyage depending on the delivery time. The comparison of the methods shows that the new equation provides good accuracy in all examined speed ranges and all examined waterway depths.
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In this study, the aim was to model the toxic effect of copper (Cu) and analyse the removal of Cu in aqueous Saharan and non-Saharan mediums by Lemna minor. Two separate test groups were formed: with Saharan dust (S) and without Saharan dust (WS). These test groups were exposed to 3 different Cu concentrations (0.05, 0.50 and 5.00 ppm). Time, concentration, and group-dependent removal efficiencies were compared using the non-parametric Mann-Whitney U test and statistically significant differences were found. The optimum removal values were tested at the highest concentration 79.6% in the S medium and observed on the 4th day for all test groups. The lowest removal value (16%) was observed at 0.50 ppm on the 1st day in the WS medium. When the S medium and WS medium were compared, in all test groups Cu was removed more successfully in the S medium than the WS medium contaminated by Cu in 3 different concentrations of (0.05 ppm, 0.50 ppm, 5.00 ppm). The regression analysis was also tested for all prediction models. Different models were performed and it was found that cubic models show the highest predicted values (R2). The R2 values of the estimation models were found to be at the interval of 0.939–0.991 in the WS medium and 0.995–1.000 in the S medium.
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The study is based on the research analyses of K.V. Petrides’ (2011) trait emotional intelligence construct verified by his Trait Emotional Intelligence Questionnaire-TEIQue. Verification of the EI trait construct by stepwise regression analysis confirmed that it is determined only to a certain extent by the Big Five personality factors theory (14%) (by the TIPI questionnaire, Gosling, 2003) and by perception and experiencing of positive (15%) and negative (13%) mental states (by the SEHW questionnaire, Džuka & Dalbert, 2002). Thus, the emotional intelligence trait as a consistent construct partially captures individual variability of emotional aspects otherwise scattered across personality theories.
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The paper investigates the relationship between the market concentration and the price-cost margin in the Czech food processing industry during 2003-2014. Estimated econometric models with Fixed Effects supported hypothesis assuming the positive impact of the market concentration on the price-cost margin controlling for productivity. The increase in the productivity was associated with the increase of the price-cost margins. Based on the results, policy makers should promote market competition in the food processing industry, for example through the reduction of entry barriers. Another option worth consideration is to support R&D activities potentially leading to market transformation and increase in efficiency.
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Factor models observe the sensitivity of an asset return as a function of one or more factors. This paper analyzes returns on fourteen stocks of the Croatian capital market in the period from January 2004 to October 2009 using inflation, industrial production, interest rates, market index and oil prices as factors. Both the direction and strength of the relation between the change in factors and returns are investigated. The analyses included fourteen stocks and their sensitivities to factors were estimated. The results show that the market index has the largest statistical significance for all stocks and a positive relation to returns. Interest rates, oil prices and industrial production also marked a positive relation to returns, while inflation had a negative influence. Furthermore, cross-sectional regression with the estimated sensitivities used as independent variables and returns in each month as dependent variables is performed. This analysis resulted in time series of risk premiums for each factor. The most important factor affecting stock prices proved to be the market index, which had a positive risk premium. A statistically significant factor in 2004 and 2008 was also inflation, marking a negative risk premium in 2004 and a positive one in 2008. The remaining three factors have not shown as significant.
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