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Wyświetlanie 1-15 z 18
Tytuł:
Forecasting production volume in a plastics enterprise
Autorzy:
Grzelak, Małgorzata
Powiązania:
https://bibliotekanauki.pl/articles/970665.pdf
Data publikacji:
2019-03-31
Wydawca:
Uniwersytet Gdański. Wydział Ekonomiczny
Tematy:
multiple regression model
production scheduling
readiness
forecasting
Opis:
The functioning of production enterprises is based on satisfying the needs of customers through the timely manufacture of products in accordance with the demand existing on the market. The availability of the offered range of products is guaranteed by a correct preparation of forecasts of potential orders. This article presents a multiple-regression-method-based tool supporting the planning of production volumes in an enterprise depending on the calendar month. Reliability analysis of the developed model through the analysis of residuals and their autocorrelations and partial autocorrelations is also presented. Key words: multiple regression model, production scheduling, readiness, forecasting JEL classification: C2, C22.
Źródło:
Współczesna Gospodarka; 2019, 10, 1 (32); 69-78
2082-677X
Pojawia się w:
Współczesna Gospodarka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Determinants of the experts evaluation of journals in economic sciences
Autorzy:
Osiewalski, Jacek
Osiewalska, Anna
Powiązania:
https://bibliotekanauki.pl/articles/703006.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
scientometrics
multiple regression statistical model
model reduction
Opis:
In 2015 an important part of the official evaluation of Polish scientific journals was left to experts’ judgement. In this paper we try to establish which observable factors (with available data) are closely related to the outcome of experts’ evaluation of Polish journals in economic sciences. Using the multiple regression statistical model we show that only 5 variables (out of 17) significantly explain almost 50% of the empirical variance of the experts’ evaluation. The determinants of particular interest, not entering the formal criteria and not related to the impact on global science, are: the number of citations mainly in Polish journals and the affiliation with the Polish Academy of Sciences.
Źródło:
Nauka; 2017, 1
1231-8515
Pojawia się w:
Nauka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Dynamic modelling of an anaerobic reactor treating coffee wet wastewater via multiple regression model
Autorzy:
Guardia-Puebla, Yans
Llanes-Cedeño, Edilberto
Domínguez-León, Ana Velia
Arias-Cedeño, Quirino
Sánchez-Girón, Victor
Morscheck, Gert
Eichler-Löbermann, Bettina
Powiązania:
https://bibliotekanauki.pl/articles/1841946.pdf
Data publikacji:
2021
Wydawca:
Instytut Technologiczno-Przyrodniczy
Tematy:
coffee wet wastewater
modelling
multiple regression model
upflow anaerobic sludge blanket
UASB
Opis:
A multiple regression model approach was developed to estimate buffering indices, as well as biogas and methane productions in an upflow anaerobic sludge blanket (UASB) reactor treating coffee wet wastewater. Five input variables measured (pH, alkalinity, outlet VFA concentration, and total and soluble COD removal) were selected to develop the best models to identify their importance on methanation. Optimal regression models were selected based on four statistical performance criteria, viz. Mallow’s Cp statistic (Cp), Akaike information criterion (AIC), Hannan–Quinn criterion (HQC), and Schwarz–Bayesian information criterion (SBIC). The performance of the models selected were assessed through several descriptive statistics such as measure of goodness-of-fit test (coefficient of multiple determination, R2; adjusted coefficient of multiple determination, Adj-R2; standard error of estimation, SEE; and Durbin–Watson statistic, DWS), and statistics on the prediction errors (mean squared error, MSE; mean absolute error, MAE; mean absolute percentage error, MAPE; mean error, ME and mean percentage error, MPE). The estimated model reveals that buffering indices are strongly influenced by three variables (volatile fatty acids (VFA) concentration, soluble COD removal, and alkalinity); while, pH, VFA concentration and total COD removal were the most significant independent variables in biogas and methane production. The developed equation models obtained in this study, could be a powerful tool to predict the functionability and stability for the UASB system.
Źródło:
Journal of Water and Land Development; 2021, 50; 229-239
1429-7426
2083-4535
Pojawia się w:
Journal of Water and Land Development
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Machine learning methods applied to sea level predictions in the upper part of a tidal estuary
Autorzy:
Guillou, N.
Chapalain, G.
Powiązania:
https://bibliotekanauki.pl/articles/2078822.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Instytut Oceanologii PAN
Tematy:
multiple regression model
artificial neural network
multilayer perceptron
regression function
machine learning algorithm
sea level
Opis:
Sea levels variations in the upper part of estuary are traditionally approached by relying on refined numerical simulations with high computational cost. As an alternative efficient and rapid solution, we assessed here the performances of two types of machine learning algorithms: (i) multiple regression methods based on linear and polynomial regression functions, and (ii) an artificial neural network, the multilayer perceptron. These algorithms were applied to three-year observations of sea levels maxima during high tides in the city of Landerneau, in the upper part of the Elorn estuary (western Brittany, France). Four input variables were considered in relation to tidal and coastal surge effects on sea level: the French tidal coefficient, the atmospheric pressure, the wind velocity and the river discharge. Whereas a part of these input variables derived from large-scale models with coarse spatial resolutions, the different algorithms showed good performances in this local environment, thus being able to capture sea level temporal variations at semi-diurnal and spring-neap time scales. Predictions improved furthermore the assessment of inundation events based so far on the exploitation of observations or numerical simulations in the downstream part of the estuary. Results obtained exhibited finally the weak influences of wind and river discharges on inundation events.
Źródło:
Oceanologia; 2021, 63, 4; 531-544
0078-3234
Pojawia się w:
Oceanologia
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
PORÓWNANIE MODELU REGRESJI WIELORAKIEJ ORAZ DRZEWA REGRESYJNEGO NA PRZYKŁADZIE INDEKSU KORUPCJI
CORRUPTION INDEX ANALYSIS USING MULTIPLE REGRESSION MODEL AND REGRESSION TREE
Autorzy:
Gostkowski, Michał
Gajowniczek, Krzysztof
Jałowiecki, Piotr
Powiązania:
https://bibliotekanauki.pl/articles/453786.pdf
Data publikacji:
2014
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Katedra Ekonometrii i Statystyki
Tematy:
Failed States Index
indeks korupcji
model regresji wielorakiej
drzewo regresyjne
corruption index
multiple regression model
regression tree
Opis:
W pracy przedstawiono wyniki badań nad modelowaniem tzw. indeksu korupcji (ang. Failed States Index). Zbudowano i porównano model regresji wielorakiej z drzewem regresyjnym. Badania zostały oparte na podstawie danych publikowanych przez niezależną organizację The Fund for Peace. Jako potencjalne zmienne zostały wybrane zmienne udostępnione na stronie internetowej Banku Światowego. Wstępne wyniki jednoznacznie wskazują, że drzewo regresyjne lepiej odzwierciedla zmienność parametru niż model regresji wielorakiej.
This paper presents the results of research on corruption index modeling (Failed States Index). The multiple regression model was constructed and compared with the regression tree. The research was based on the data published by an independent organization The Fund for Peace. Predictors were selected from a set of variables available on the website of the World Bank. The preliminary results clearly indicate that the regression tree better reflects the variation of the parameter than the multiple regression model.
Źródło:
Metody Ilościowe w Badaniach Ekonomicznych; 2014, 15, 3; 65-74
2082-792X
Pojawia się w:
Metody Ilościowe w Badaniach Ekonomicznych
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Orientirovochnye prognozy kharakteristic skorosti vetra v Evrope s uchetom izmenchivosti raspredelenija poverkhnostnykh temperatur Severnojj Atlantiki na primere Severnogo Prichernomorja
Projections of wind speed characteristics in Europe with the accounting apportionment superficial temperatures in North Atlantic an example of Black Sea
Autorzy:
Kholopcev, A.
Akcenova, A.
Powiązania:
https://bibliotekanauki.pl/articles/76861.pdf
Data publikacji:
2013
Wydawca:
Komisja Motoryzacji i Energetyki Rolnictwa
Tematy:
forecasting
wind speed
temperature
surface water
North Atlantic Ocean
Black Sea
multiple regression model
correlation
identification
Opis:
On the example of the Northern Black Sea Coast representative items shown that allowance for the identification of predictive multiple-regression models of the interannual variability of SST anomalies identified variations in the North Atlantic waters, enables efficient evaluation of monthly mean values of wind speed with a lead time of at least 3 years.
Źródło:
Motrol. Motoryzacja i Energetyka Rolnictwa; 2013, 15, 5
1730-8658
Pojawia się w:
Motrol. Motoryzacja i Energetyka Rolnictwa
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Use of a multiple regression model to determine the parameters of vessel traffic flow in port areas
Autorzy:
Nowy, A.
Gucma, L.
Powiązania:
https://bibliotekanauki.pl/articles/117457.pdf
Data publikacji:
2020
Wydawca:
Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:
maritime traffic engineering
vessel traffic flow
port areas
multiple regression model
fairway
fairway parameters
Automatic Identification System (AIS)
AIS Data
Opis:
The paper presents the method of determining ships traffic stream parameters by means of regression method. The aim of the studies was to determine the correlation between the ship's parameters and the parameters of the fairway. Developing the presented model with information on the position of the vessel's antenna and information on the accuracy of position determination will allow creating a model for predicting the parameters of waterways.
Źródło:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2020, 14, 2; 443-449
2083-6473
2083-6481
Pojawia się w:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Predictive Modelling for Characterisation of Organics in Pit Latrine Sludge from Unplanned Settlements in Cities of Malawi
Autorzy:
Kalulu, K.
Thole, B.
Mkandawire, T.
Kululanga, G.
Powiązania:
https://bibliotekanauki.pl/articles/124540.pdf
Data publikacji:
2018
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
Akaike Information Criterion
biochemical oxygen demand
chemical oxygen demand
faecal sludge characteristics
multiple linear regression model
Opis:
The limited availability of data on faecal sludge characteristics remains one of the major challenges faced by developing countries in proper management of faecal sludge. In view of the limited financial resources and expertise in these developing countries, there is a need to come up with less-resource-intensive approaches for faecal sludge characterisation. Despite being used substantially in wastewater, there is limited evidence on the use of predictive modelling as a tool for cost-effective characterisation of faecal sludge. In this study, first order multiple linear regression modelling is investigated as a less-resource-intensive approach for accurate prediction of organics (biochemical oxygen demand and chemical oxygen demand) in pit latrine sludge. The predictor variables explored in the modelling include pH, electrical conductivity, total solids, total volatile solids, fixed solids and moisture content. The modelling uses data collected from 80 latrines in unplanned settlements of four cities in Malawi. The study shows that it is possible to reliably predict chemical oxygen demand and biochemical oxygen demand in pit latrine sludge using electrical conductivity and total solids, which require low levels of resources and expertise to determine.
Źródło:
Journal of Ecological Engineering; 2018, 19, 3; 141-145
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Short-term forecasting of natural gas demand by rural consumers using regression models
Prognozowanie krótkookresowe zapotrzebowania odbiorców wiejskich na gaz ziemny z wykorzystaniem modeli regresyjnych
Autorzy:
Necka, K.
Trojanowska, M.
Powiązania:
https://bibliotekanauki.pl/articles/791988.pdf
Data publikacji:
2014
Wydawca:
Komisja Motoryzacji i Energetyki Rolnictwa
Tematy:
short-term forecast
multiple regression
natural gas
rural consumer
regression model
Źródło:
Teka Komisji Motoryzacji i Energetyki Rolnictwa; 2014, 14, 4
1641-7739
Pojawia się w:
Teka Komisji Motoryzacji i Energetyki Rolnictwa
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Zależność czas-koszt w przewidywaniu czasu realizacji budowy
Time-Cost Relationship for Predicting Construction Duration
Autorzy:
Czarnigowska, A.
Sobotka, A.
Powiązania:
https://bibliotekanauki.pl/articles/391201.pdf
Data publikacji:
2013
Wydawca:
Politechnika Lubelska. Wydawnictwo Politechniki Lubelskiej
Tematy:
model Bromilowa
regresja wieloczynnikowa
CART
Bromilow’s model
multiple regression
Opis:
W artykule podjęto próbę stworzenia modelu czasu realizacji budowy w funkcji cech charakteryzujących przedsięwzięcie, w tym kosztu. Model oparto na analizie tych cech zrealizowanych przedsięwzięć, które są znane lub możliwe do założenia we wczesnych etapach planowania, lecz bez analizy technologii i organizacji robót. Model taki mógłby być przydatny inwestorom do szacowania czasu budowy na wczesnych etapach przygotowania inwestycji, szczególnie do analiz wykonalności. Model mógłby być również podstawą do porównań czasu lub tempa robót w zależności od cech przedsięwzięcia.
The paper aims at creating a model of road construction duration in the function of project qualities including the construction cost. The qualities considered are likely to be defined or possible to be estimated at early stages of project planning, giving no consideration to construction method or organisation of works. Potentially, the model might be applied by construction clients in their feasibility studies. It could also be used for comparing construction duration or construction rate with respect to project qualities.
Źródło:
Budownictwo i Architektura; 2013, 12, 1; 23-30
1899-0665
Pojawia się w:
Budownictwo i Architektura
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Determination of soil infiltration rate equation based on soil properties using multiple linear regression
Autorzy:
Harisuseno, Donny
Cahya, Evi N.
Powiązania:
https://bibliotekanauki.pl/articles/1844413.pdf
Data publikacji:
2020
Wydawca:
Instytut Technologiczno-Przyrodniczy
Tematy:
infiltration rate
model performance
multiple linear regression
soil property
Opis:
Infiltration process plays important role in water balance concept particularly in runoff analysis, groundwater recharged, and water conservation. Hence, increasing knowledge concerning infiltration process becomes essential for water manager to gain an effective solution to water resources problems. This study employed multiple linear regression for estimating infiltration rate where the soil properties used as the predictor variable and measured infiltration rate as the response variable. Field measurement was conducted at sixteen points to obtain infiltration rate using double ring infiltrometer and soil properties namely soil porosity, silt, clay, sand content, degree of saturation, and water content. The result showed that measured infiltration rate had an average initial infiltration rate (f0) of 6.92 mm∙min–1 and final infiltration rate (fc) of 1.49 mm∙min–1. Soil porosity and sand content showed a positive correlation with infiltration rate by 0.842, 0.639, respectively, while silt, clay, water content, and degree of saturation exhibited a negative correlation by –0.631, –0.743, –0.66 and –0.49, respectively. Three types of regression equations were established based on type of soil properties used as predictor variables. The model performance analysis was conducted for each equation and the result shows that the equation with five predictor variables fMLR_3 = – 62.014 + 1.142 soil porosity – 0.205 clay, – 0.063 sand – 0.301, silt + 0.07 soil water content with R2 (0.87) and Nash–Sutcliffe (0.998) gave the best result for estimating infiltration rate. The study found that soil porosity contributes mostly to the regression equation that indicates great influence in controlling soil infiltration behavior.
Źródło:
Journal of Water and Land Development; 2020, 47; 77-88
1429-7426
2083-4535
Pojawia się w:
Journal of Water and Land Development
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Modele ekonometryczne jako narzędzie sterowania procesami technologicznymi
Econometric models as a tool for technological process control
Autorzy:
Wołkowicz, Artur
Powiązania:
https://bibliotekanauki.pl/articles/424875.pdf
Data publikacji:
2015
Wydawca:
Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
Tematy:
exponential smoothing model with creeping trend
Brown model
regression function
multiple and threshold regression
linear programming
Opis:
This paper presents a proposal for process control applications based on econo-metric models. They are a tool which aim is to determine short-term forecasts, which are the basis to control the devices of production infrastructure. The article describes the application of the method of forecast errors corrective device in a real production process. Econometric models are presented: the exponential smoothing model and creeping trend adaptive model with harmonic scales. The calculations are used and the regression function is indicated by the linear programming problem. The method is presented on the example of classical tech-nological process used in the energy sector. The study indicates the possibility of another perspective on the control processes, not necessarily based on the existing methods of regu-lation. The idea of this study is to demonstrate the possibility of using econometrics in the industry.
Źródło:
Econometrics. Ekonometria. Advances in Applied Data Analytics; 2015, 2 (48); 67-77
1507-3866
Pojawia się w:
Econometrics. Ekonometria. Advances in Applied Data Analytics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Statistical yielding models of some irrigated vegetable crops in dependence on water use and heat supply
Autorzy:
Vozhehova, Raisa
Kokovikhin, Sergii
Lykhovyd, Pavlo V.
Balashova, Halyna
Lavrynenko, Yuriy
Biliaieva, Iryna
Markovska, Olena
Powiązania:
https://bibliotekanauki.pl/articles/292829.pdf
Data publikacji:
2020
Wydawca:
Instytut Technologiczno-Przyrodniczy
Tematy:
linear model
multiple linear regression analysis
onion
potato
tomato
yield modelling
Opis:
Statistical analysis is helpful for better understanding of the processes which take place in agricultural ecosystems. Particular attention should be paid to the processes of crops’ productivity formation under the influence of natural and anthropogenic factors. The goal of our study was to provide new theoretical knowledge about the dependence of vegetable crops’ productivity on water supply and heat income. The study was conducted in the irrigated conditions of the semi-arid cold Steppe zone on the fields of the Institute of Irrigated Agriculture of NAAS, Kherson, Ukraine. We studied the historical data of productivity of three most common in the region vegetable crops: potato, tomato, onion. The crops were cultivated by using the generally accepted in the region agrotechnology. Historical yielding and meteorological data of the period 1990–2016 were used to develop the models of the vegetable crops’ productivity. We used two approaches: development of pair linear models in three categories (“yield – water use”, “yield – sum of the effective air temperatures above 10°C”); development of complex linear regression models taking into account such factors as total water use, and temperature regime during the crops’ vegetation. Pair linear models of the crops’ productivity showed that the highest effect on the yields of potato and onion has the water use index (R2 of 0.9350 and 0.9689, respectively), and on the yield of tomato – temperature regime (R2 of 0.9573). The results of pair analysis were proved by the multiple regression analysis that revealed the same tendencies in the crop yield formation depending on the studied factors.
Źródło:
Journal of Water and Land Development; 2020, 45; 190-197
1429-7426
2083-4535
Pojawia się w:
Journal of Water and Land Development
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Costs of facade systems execution
Koszty wykonania systemów elewacyjnych
Autorzy:
Leśniak, Agnieszka
Wieczorek, Damian
Górka, Monika
Powiązania:
https://bibliotekanauki.pl/articles/230531.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
system elewacyjny
szacowanie kosztów
model statystyczny
regresja wielokrotna
regresja krokowa
facade system
cost estimation
statistical model
multiple regression
stepwise regression
Opis:
Cost estimation in the pre-design phase both for the contractor as well as the investor is an important aspect from the point of view of budget planning for a construction project. Constantly growing commercial market, especially the one of public utility constructions, makes the contractor, at the stage of development the design concept, initially estimate the cost of the facade, e.g. office buildings, commercial buildings, etc., which are most often implemented in the form of aluminum-glass facades or ventilated elevations. The valuation of facade systems is of an individual calculation nature, which makes the process complicated, time-consuming, and requiring a high cost estimation work. The authors suggest using a model for estimating the cost of facade systems with the use of statistical methods based on multiple and stepwise regression. The data base used to form statistical models is the result of quantitative-qualitative research of the design and cost documentation of completed public facilities. Basing on the obtained information, the factors that shape the costs of construction façade systems were identified; which then constitute the input variables to the suggested cost estimation models.
Celem artykułu jest próba oszacowania kosztów wykonania systemów elewacyjnych obiektów użyteczności publicznej z wykorzystaniem modeli matematycznych, a w szczególności regresji wielorakiej oraz regresji krokowej postępującej. Dane użyte do budowy funkcji regresji zostały opracowane przez autorów na podstawie analizy dokumentacji projektowych, wykonawczych i kosztorysowych obiektów użyteczności publicznej. Opracowano bazę danych zawierającą główne czynniki wpływające na kształtowanie się kosztów wykonania systemów elewacyjnych obiektów użyteczności publicznej. Systemy elewacyjne realizowane były w formie fasad aluminiowo-szklanych oraz elewacji wentylowanych.
Źródło:
Archives of Civil Engineering; 2020, 66, 1; 81-95
1230-2945
Pojawia się w:
Archives of Civil Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Intelligent sensing and monitoring : respiratory motion prediction for tumor following radiotherapy
Autorzy:
Ichiji, K.
Homma, N.
Sakai, M.
Bukovsky, I.
Zhang, X.
Osanai, M.
Abe, M.
Sugita, N.
Yoshizawa, M.
Powiązania:
https://bibliotekanauki.pl/articles/91582.pdf
Data publikacji:
2012
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
intelligent sensing
monitoring
respiratory motion
tumor
radiotherapy
time-varying seasonal autoregressive model
TVSAR model
multiple regression
MR
multilayer perceptron
MLP
support vector regression
SVR
Opis:
This paper presents a medical application of the intelligent sensing and monitoring, a new lung tumor motion prediction method for tumor following radiation therapy. An essential core of the method is accurate estimation of complex fluctuation of time-varying periodical nature of lung tumor motion. Such estimation is achieved by using a novel multiple time-varying seasonal autoregressive (TVSAR) model in which several windows of different time-lengths are used to calculate correlation based fluctuation of periodic nature in the motion. The proposed method provides the prediction as a combination of those based on different window lengths. Multiple regression (MR), multilayer perceptron (MLP) and support vector regression (SVR) are used to combine and the prediction performances are evaluated by using clinical lung tumor motion. The proposed methods with the combined predictions showed high accurate prediction and are superior to the single different predictions. The average errors of MR, MLP, and SVR were 0.8455,0.8507, and 0.7530 mm at 0.5 s ahead, respectively. The results are clinically sufficient and thus clearly demonstrate that the proposed TVSAR with an appropriate combination method is useful for improving the prediction performance.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2012, 2, 4; 331-342
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
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