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.

scholarly journals Forecasting Students’ Final Exam: Results Using Multiple Regression Analysis in an Undergraduate Business Statistics Course

2021 ◽  
pp. 30-40
Author(s):  
Gunawardena Egodawatte
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Multiple Regression Model ◽  
Predictor Variables ◽  
Teaching Tool ◽  
Final Examination ◽  
Final Exam ◽  
Final Model ◽  
Business Statistics ◽  
Statistics Course

This paper discusses the development of a multiple regression model to predict the final examination marks of students in an undergraduate business statistics course. The marks of a sample of 366 students in the Winter 2017 semester were used to fit the regression model. The final model contained three predictor variables namely two test marks and the homework assignment mark. The marks of another 194 students from Winter 2018 were used to validate the model. The model validation showed that it can be used for future cohorts of students for prediction. The two main objectives of the study were to use the model as a teaching tool in class and to use the model to predict final examination marks of future students.

Cross-Validation of a System for Predicting Training Success of Medex Trainees

1973 ◽  
Vol 33 (3) ◽  
pp. 917-918 ◽  
Author(s):  
Leroy A. Stone ◽  
James D. Brosseau
Keyword(s):  
Regression Model ◽  
Training Program ◽  
Multiple Regression ◽  
Cross Validation ◽  
Multiple Regression Model ◽  
Predictor Variables ◽  
Personnel Evaluation ◽  
Criterion Measure ◽  
Predicting Success ◽  
Training Success

An already developed multiple-regression model for predicting success of Medex trainees in their training program was cross-validated using a new group of Medex trainees. Six psychological test predictor variables (2 on the MMPI and 4 on the Strong) “held up” upon cross-validation. The results lent credence to the use of multidimensional judgment scaling for establishment of a personnel evaluation-grading criterion measure.

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.

Psychological Test Characteristics Associated with Training-Success in a Medex (Physician's Extension) Training Program

1973 ◽  
Vol 32 (1) ◽  
pp. 231-234 ◽  
Author(s):  
Leroy A. Stone ◽  
Gerald R. Bassett ◽  
James D. Brosseau ◽  
Judy Demers ◽  
John A. Stiening
Keyword(s):  
Regression Model ◽  
Training Program ◽  
Multiple Regression ◽  
Clinical Judgment ◽  
Multiple Regression Model ◽  
Psychological Test ◽  
Predictor Variables ◽  
Test Characteristics ◽  
Training Success ◽  
Do So

A multiple regression model for predicting trainee success in a Medex training program is reported. This model employs selected MMPI and Strong scales as predictor variables. Although the model has not yet been cross-validated (plans to do so are underway), elements of it seem consistent with evaluations based on clinical judgment.

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