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 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.

Function development for appraising brittleness of intact rocks using genetic programming and non-linear multiple regression models

2016 ◽  
Vol 33 (1) ◽  
pp. 13-21 ◽  
Author(s):  
Manoj Khandelwal ◽  
Roohollah Shirani Faradonbeh ◽  
Masoud Monjezi ◽  
Danial Jahed Armaghani ◽  
Muhd Zaimi Bin Abd Majid ◽  
...  
Keyword(s):  
Genetic Programming ◽  
Multiple Regression ◽  
Regression Models ◽  
Linear Multiple Regression ◽  
Multiple Regression Models ◽  
Non Linear

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.

Optimal Cost Sampling for Decision Making with Multiple Regression Models

1989 ◽  
Vol 20 (1) ◽  
pp. 14-26 ◽  
Author(s):  
Mark Eakin ◽  
Lawrence L. Schkade ◽  
Mary Whiteside
Keyword(s):  
Decision Making ◽  
Multiple Regression ◽  
Regression Models ◽  
Optimal Cost ◽  
Multiple Regression Models

scholarly journals Evaluation and reparametrization of mathematical models for prediction of the leaf area of Megathyrsus maximus cv. BRS Zuri

2020 ◽  
Vol 8 (3) ◽  
pp. 214-219
Author(s):  
Patrick Bezerra Fernandes ◽  
Rodrigo Amorim Barbosa ◽  
Maria Da Graça Morais ◽  
Cauby De Medeiros-Neto ◽  
Antonio Leandro Chaves Gurgel ◽  
...  
Keyword(s):  
Regression Model ◽  
Leaf Area ◽  
Multiple Regression ◽  
Leaf Blade ◽  
Multiple Regression Model ◽  
Leaf Length ◽  
Predictor Variables ◽  
Mato Grosso ◽  
Linear Multiple Regression ◽  
Model C

The aim of this study was to verify the precision and accuracy of 5 models for leaf area prediction using length and width of leaf blades of Megathyrsus maximus cv. BRS Zuri and to reparametrize models. Data for the predictor variables, length (L) and width (W) of leaf blades of BRS Zuri grass tillers, were collected in May 2018 in the experimental area of Embrapa Gado de Corte, Mato Grosso do Sul, Brazil. The predictor variables had high correlation values (P<0.001). In the analysis of adequacy of the models, the first-degree models that use leaf blade length (Model A), leaf width × leaf length (Model B) and linear multiple regression (Model C) promoted estimated values similar to the leaf area values observed (P>0.05), with high values for determination coefficient (>80%) and correlation concordance coefficient (>90%). Among the 5 models evaluated, the linear multiple regression (Model C: β0 = -5.97, β1 = 0.489, β2 = 1.11 and β3 = 0.351; R² = 89.64; P<0.001) and as predictor variables, width, length and length × width of the leaf blade, are the most adequate to generate precise and exact estimates of the leaf area of BRS Zuri grass.

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