scholarly journals Effects of game location, quality of opposition, number of foreign players and anthropometric characteritics in elite handball games

Kinesiology ◽  
2017 ◽  
Vol 49 (2) ◽  
pp. 194-201 ◽  
Author(s):  
Thierry Debanne ◽  
Guillaume Laffaye
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Regression Models ◽  
Multiple Regression Model ◽  
Previous Season ◽  
High Contribution ◽  
Anthropometric Characteristics ◽  
Multiple Regression Models ◽  
The Difference

The aims of this study were (1) to investigate the influence of game location, quality of opposition, age of players, and anthropometric characteristics of backcourt and pivot players as well as the number of foreign players in a team on goal difference between the teams in the French (LNH) and German (Bundesliga) national men’s professional leagues, and (2) to predict goal difference of match final scores in these two national leagues using a multiple regression model. Archival data were obtained from the open access official websites to collect a sample of 165 handball matches (LNH [N=89], and Bundesliga [N=76]) of the first part of the 2015/2016 regular season. The linear regression model predicted the winner in 79% of cases with a mean accuracy of four goals. The coefficients of determination found in both multiple regression models were r²=.67 and r²=.49 for the LNH and the Bundesliga, respectively. The models revealed a high contribution of the difference in the end-of-previous season goal-average per match ranking and a moderate contribution of the game location to goal difference. The models also highlighted differences in the way games were won in these leagues with a contribution of foreigners, anthropometric characteristics and age only in the Bundesliga.

Some Conditions Affecting the Utility of Subjectively Weighted Models in Decision Making

1979 ◽  
Vol 49 (2) ◽  
pp. 583-590 ◽  
Author(s):  
Lars Nystedt ◽  
Kevin R. Murphy
Keyword(s):  
Decision Making ◽  
Regression Model ◽  
Multiple Regression ◽  
Regression Models ◽  
Multiple Regression Model ◽  
Linear Multiple Regression ◽  
Multiple Regression Models ◽  
Highly Correlated ◽  
Degree Of Overlap

The accuracy of multiple regression models, models employing subjective weights and models employing relative subjective weights in reproducing judgments was studied. Multiple regression models were most accurate. When subjects were divided into two groups according to the degree of configurality shown in their matrix of subjective weights, striking differences were found in the degree of overlap of the multiple regression models and the models employing subjective weights. In particular, when subjective policies were essentially linear, the predicted judgments produced by these policies were highly correlated with the predicted judgments of the multiple regression models. When subjective policies were highly configural, the subjective models accounted for variance in judgments not accounted for by the linear multiple regression model.

scholarly journals FAKTOR-FATOR YANG MEMPENGARUHI WISATAWAN MENGUNJUNGI PANTAI ZAKAT DI KOTA BENGKULU

2018 ◽  
Vol 6 (1) ◽  
Author(s):  
Syamsul Huda ◽  
Jalal Ikhwan
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Hypothesis Test ◽  
Multiple Regression Model ◽  
Test Stage ◽  
Dependent Variables ◽  
Independent Variable ◽  
To Come ◽  
Test Result

Syamsul Huda, Jalal Ikhwan; The research aims to determine the number of variables that affect tourists visiting Zakat beach in Bengkulu city. The population used is the visitors who visit Zakat beach. The type of data implemented is the primery daya. The way of implement it is by spreading out of questioners. The research puts on two variables. First, it is the independent variable. It consist of servicing, the trimming of the facilities, safety and earning. Where as the dependent variables focuses on the readability of the visitors to visit Zakat beach. The test stage are done by utilizing spss. To create the hypothesis test stages, it can apply the multiple regression model and t-statistic test. Building upon the hypothesis test result, it has been uncovered that the quality of servicing and safety have significant effects to the readibility of the visitors to come to see Zakat beach in Bengkulu city of Bengkulu province. It can be concluded that the trimming of the facility and earnings do not make significant effects towards the viusitors readability to pay a visit to Zakat beach.Key Words: Service, Facilities, Security and Income.

Prediction of Cotton Ring Yarn Evenness Properties from Process Parameters by Using Artificial Neural Network and Multiple Regression Analysis

2011 ◽  
Vol 366 ◽  
pp. 103-107 ◽  
Author(s):  
Bo Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Process Parameters ◽  
Regression Models ◽  
Absolute Error ◽  
Multiple Regression Model ◽  
Ring Yarn ◽  
Multiple Regression Models ◽  
Artificial Neural

The artificial neural network and multiple regression models have been developed to predict the evenness of cotton ring yarn with process parameters such as front roller speed, spindle speed, nip gauge, back draft zone time and roving twist. The efficiencies of prediction of the two models have been experimentally verified, and the predicted evennesses of cotton ring yarns from both the models have been compared statistically. An attempt has been made to study the effect of process parameters on yarn evenness. The MSE and mean absolute error of ANN modelare lower than that of multiple regression model. The results show that the performances of prediction of ANN models are more accurate than those of multiple regression models.

Analysis of Water Quality of Lake Hachiroko in Japan Using a Fuzzy Multiple Regression Model with ALOS AVNIR-2 Data

2016 ◽  
Vol 20 (6) ◽  
pp. 992-1003 ◽  
Author(s):  
Dejian Wang ◽  
◽  
Yoichi Kageyama ◽  
Makoto Nishida ◽  
Hikaru Shirai ◽  
...  
Keyword(s):  
Water Quality ◽  
Regression Model ◽  
Multiple Regression ◽  
Near Infrared ◽  
Multiple Regression Model ◽  
Fuzzy Regression ◽  
Quality Data ◽  
Water Quality Data ◽  
Using Data

The distribution of water pollution is often assessed by remote sensing. In this study, we develop a fuzzy multiple regression model and analyze water quality using data collected by the Advanced Visible and Near Infrared Radiometer type-2 (AVNIR-2) of the Advanced Land Observing Satellite at different time points. We conduct a fuzzy multiple regression analysis of the AVNIR-2 data and direct measurements of the local water quality of Lake Hachiroko in Japan. The relationship between the AVNIR-2 and water quality data are analyzed by solving both min and max problems. We compare the estimated water quality maps with the actual distributions in the study area, and determine that the proposed method enables us to derive water quality conditions effectively from the AVNIR-2 data. Furthermore, by comparing maps created using AVNIR-2 data collected at different times, we obtain results revealing temporal changes in water quality. In addition, we compare maps created using the fuzzy multiple regression and fuzzy regression models. We demonstrate that the former offers a greater number of solutions and provides more details about water quality.

scholarly journals Comprehensive approach to the tuberculosis indicators assessment of the children population in the Republic of Crimea

2021 ◽  
pp. 78-84
Author(s):  
T. N. Golubova ◽  
N. M. Ovsannikova ◽  
Z. R. Makhamova
Keyword(s):  
Multiple Regression ◽  
Regression Models ◽  
Correlation Coefficients ◽  
Pulmonary Tb ◽  
Pairwise Correlations ◽  
Multiple Regression Models ◽  
Childhood Tb ◽  
The Republic ◽  
Paired Correlation

Introduction. Childhood tuberculosis (TB) control is relevant due to the peculiarities of its course in this age group, and the TB incidence in children is an important prognostic epidemiological indicator.Aim. Use of multivariate statistical analysis to estimate and predict childhood TB indicators in the Republic of Crimea (RC).Materials and methods. The official TB statistics in the Republic of Crimea for 2014-2018 are used. The calculated means of the indicators are checked for normality using the Kolmogorov-Smirnov and Shapiro-Wilk tests. Pearson correlation analysis is applied to determine pair correlation relationships. Stepwise multiple regression analysis is carried out to determine group conditionality of the indicators, where coefficients, with which significant pairwise correlations are found, are selected as independent variables. Based on the results, multiple regression equations are made to predict the values of dependent variables. The data is processed using Statistica 10.0 software.Results. For childhood TB incidence, strong direct correlations are established with the incidence and prevalence of pulmonary TB among children. The paired correlation coefficient between the incidence of childhood TB and childhood lung TB and the detection of active TB patients in preventive examinations of children varied in the range of 0.63-0.72. For the prevalence of TB among children, strong direct correlations were found with the incidence of TB and pulmonary TB in children. Multiple correlation coefficients for the incidence and prevalence of childhood TB exceeded the values of paired correlation coefficients and were in the range of 0.93 to 0.98 (p<0.001), indicating greater significance of group conditionality of the indicators. Determination coefficients R2 were between 0.87 and 0.96. Multiple regression models were built for the childhood TB incidence, childhood lung TB incidence, childhood TB prevalence, childhood lung TB prevalence.Conclusion. The found strong direct pairwise correlations for childhood TB incidence and prevalence and childhood pulmonary TB incidence and prevalence can serve as prognostic criteria and reflect the quality of antituberculosis interventions. High values of paired correlation coefficient between childhood TB incidence and childhood pulmonary TB and detection of patients with active TB in preventive examinations of children are a criterion of quality of both TB services and primary care, which can prevent the spread of TB and improve the epidemic situation of TB in Crimea. The calculated multiple regression models for the studied indicators can serve the needs of practical forecasting in Healthcare.

scholarly journals Prediction of 3000-m Running Performance Using Classic Physiological Respiratory Responses

2018 ◽  
Vol 6 (3) ◽  
pp. 18
Author(s):  
Thiago F. Lourenço ◽  
Fernando O. C. da Silva ◽  
Lucas S. Tessutti ◽  
Carlos E. da Silva ◽  
Cesar C. C. Abad
Keyword(s):  
Multiple Regression ◽  
Regression Models ◽  
Ventilatory Threshold ◽  
Maximal Oxygen Consumption ◽  
Multiple Regression Model ◽  
Running Performance ◽  
Significance Level ◽  
Physiological Variables ◽  
Multiple Regression Models ◽  
Respiratory Responses

Introduction: Knowing which physiological variables predict running performance could help coaches to optimize training prescription to improve running performance. Objective: The present study investigated which physiological respiratory responses could predict 3000-m running performance. Methods: Seventeen amateur runners (29.82±7.1years; 173.12±9.0cm; 64.59±9.3kg) performed a maximal graded running test on a treadmill. The ventilatory threshold (VT), respiratory compensation point (RCP), and maximal oxygen consumption (VO2max) were assessed, as well as the respective velocities (vVT, vRCP, vVO2max). After 72 to 96 hours the runners performed the 3000-m running field test. The relationships between variables were performed using Pearson product momentum correlations. Thereafter, simple and multiple regression models were applied. The significance level adopted was 5% (p<0.05). Results: The majority of physiological responses were positive and well related to each other (r≥0.70; p<0.05). Despite vVT, vRCP, and vVO2max demonstrating a higher and inverse relationship with 3000-m time (r=-0.92; r =-0.96; r =-0.89; p<0.05), the multiple regression model indicated that vRCP and vVO2max are the best variables to predict 3000-m performance in experienced amateur road runners (R2=0.94). The equation proposed by the model was: 3000-m(s)=1399.21–[31.65*vRCP(km.h-1)]–[12.06*vVO2max (km.h-1)]. Conclusion: The vRCP and vVO2max may be used to predict 3000-m performance using only a maximal running test on a treadmill. In practical terms, coaches and physical conditioners can use performance in the 3000-m to select different exercise running intensities to prescribe exercise training intensities.

scholarly journals Estimating Sound Seeds per Cone in White Spruce

1989 ◽  
Vol 65 (4) ◽  
pp. 266-270
Author(s):  
W. H. Fogal ◽  
I. S. Alemdag
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Regression Models ◽  
White Spruce ◽  
Multiple Regression Model ◽  
Independent Variables ◽  
Independent Variable

White spruce cones were collected near Petawawa, Sudbury, and Oakville, Ontario during the 1982 and 1984 crop years. Cones were used to examine several regression models for estimating the number of sound seeds per cone as a function of seeds per cone section, cone length, and/or diameter. For the simplest models, using only one independent variable, highest coefficients of determination (R2) were found with sound seed per section. A multiple regression model, including all three independent variables and their interactions, was identified. It provided higher coefficients of determination than the model using sound seed per cone section as a single independent variable but did not provide much more precision for estimating sound seeds per cone. Regressions were specific for locations and crop years.

scholarly journals Simulative Verification of the Possibility of using Multiple Regression Models for Real Estate Appraisal

2019 ◽  
Vol 27 (3) ◽  
pp. 109-123
Author(s):  
Sebastian Kokot ◽  
Sebastian Gnat
Keyword(s):  
Real Estate ◽  
Multiple Regression ◽  
Regression Models ◽  
Negative Impact ◽  
Real Estate Market ◽  
Real Property ◽  
The Real ◽  
Real Estate Valuation ◽  
Multiple Regression Models

Abstract The possibility of using multiple regression models in real estate valuation is the subject of disputes, both in theory and in practice. Econometric modelling is a difficult process, since a number of issues of substantive and numerical nature occur during that process. Modern technologies enable quick and easy model estimation with the use of virtually any quality of data. Naturally, it provokes property appraisers to use such models in the practice of real property valuation, particularly in mass appraisal, frequently without taking those issues into account. Consequently, the models obtained and applied in practice turn out to be of poor quality and, objectively speaking, should not serve as the basis for determining real estate value. The specificity of the real estate market and of the real properties themselves as objects traded in that market additionally exert a negative impact on the quality of the obtained models. In this article, the authors present the results of research which involved a simulation of various types of disturbances of a model artificially developed database of real estate prices and attributes as well as their impact on the quality of estimated models. The research will make it possible to answer the question of the degree and type of disturbances that are permissible in the functioning of a real estate market if the estimated models are to still satisfy the qualitative requirements defined for them, and thereby produce accurate valuation results. A model database will be disturbed by the deviation of prices from model prices and by reducing its size. Radom generators were used to obtain database disturbances.

scholarly journals Comparison of a Multiple Regression Model and a two-Regime Model of a Vertical Refraction/ Porovnanie Viacnásobného Regresného Modelu A Dvojrežimového Modelu Vertikálnej Refrakcie

2015 ◽  
Vol 61 (1) ◽  
pp. 32-41
Author(s):  
Štefan Sokol ◽  
Mroslav Lipták ◽  
Marek Bajtala
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Meteorological Parameters ◽  
Multiple Regression Model ◽  
Physical Principle ◽  
Threshold Function ◽  
Explanatory Variables ◽  
Different Types ◽  
The Impact

Abstract Accuracy of the trigonometric measurement of elevations is affected by the systematic influence of a vertical refraction, which is caused by changes of meteorological parameters. Submitted paper deals with a modelling of the impact of the vertical refraction using selected meteorological parameters. At first, a concise derivation of a physical principle of the vertical refraction is given. Then, a multiple regression model and its extension into a form of two-regime model are given. Division into two regimes provides a threshold function, which expresses the dependence of the original explanatory variables. Different types of the threshold function are considered and finally a comparison of the quality of the proposed models and application of a chosen model on the results of repeated trigonometric measurements is given.

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