The Macroergonomical Challenge of Industrial Teaming Arrangements' Organizational Structure

1989 ◽  
Vol 33 (13) ◽  
pp. 836-840 ◽  
Author(s):  
Robert L. Getty
Keyword(s):  
Organizational Structure ◽  
Multiple Regression ◽  
Principal Component ◽  
Boundary Spanning ◽  
Path Analytic

Industrial teaming arrangements that are formed by industry to take advantage of pooled resources have problems of coordination and communication in their operations. The central contention of this study is that the organizational structure is the precipitating cause of the resulting inefficiencies. Organizations that have their own self-referential rules are unable to mesh without causing conflict. The location of the majority of structural difficulties are in the interstitial, boundary spanning roles between teamed companies. A mailed questionnaire was developed and distributed to key individuals, generally occupying boundary spanning positions, who participate in teaming arrangements. The data from this industrial survey was analyzed by principal component, multiple regression and path analytic procedures. Studies in individual teaming arrangements are suggested to identify and solve structural issues that detract from their operations.

Relation of geographic variation in the skull of Microtus pennsylvanicus to climate

1983 ◽  
Vol 61 (6) ◽  
pp. 1232-1241 ◽  
Author(s):  
Richard R. Snell ◽  
Kimberly M. Cunnison
Keyword(s):  
Low Temperature ◽  
Geographic Variation ◽  
Multiple Regression ◽  
Minimum Spanning Tree ◽  
Climatic Factors ◽  
Principal Component ◽  
Microtus Pennsylvanicus ◽  
Annual Number ◽  
Simple Correlation ◽  
Morphometric Variation

Analyses of geographic variation in the skull of meadow voles (Microtus pennsylvanicus) indicate that phenetic distances among samples are not related to geographic distance: a minimum spanning tree based on average taxonomic distance superimposed on a map of 38 localities provides no particular phenetic clustering of those samples geographically proximate. A multiple regression of phenetic component one (skull size) onto orthogonally rotated climatic factors explains much less morphometric variation (25.6%) than a simple correlation with recorded extreme low temperature (38.9%). Multiple regression of phenetic principal component two (interorbital width) onto the same climatic factors explains minimally more morphological variation (42.1%) than a simple correlation with mean annual number of days with frost (41.7%). Microtus pennsylvanicus shows a pattern of size variation that is the reverse of Bergmann's rule: these voles are large where it is warm and small where it is cold. Since small size reduces total energy expenditure, we predict that during times of extreme low temperature (i) smaller voles will be less energetically stressed than larger voles and (ii) large size will be actively selected against.

scholarly journals The optimal number of partial least squares components in genomic selection for pork pH

2017 ◽  
Vol 47 (1) ◽  
Author(s):  
Fernanda Gomes da Silveira ◽  
Darlene Ana Souza Duarte ◽  
Lucas Monteiro Chaves ◽  
Fabyano Fonseca e Silva ◽  
Ivan Carvalho Filho ◽  
...  
Keyword(s):  
Least Squares ◽  
Genomic Selection ◽  
Multiple Regression ◽  
Partial Least Squares ◽  
Predictive Accuracy ◽  
Principal Component ◽  
Theoretical Background ◽  
Optimal Number ◽  
Number Of Components ◽  
Regression Techniques

ABSTRACT: The main application of genomic selection (GS) is the early identification of genetically superior animals for traits difficult-to-measure or lately evaluated, such as meat pH (measured after slaughter). Because the number of markers in GS is generally larger than the number of genotyped animals and these markers are highly correlated owing to linkage disequilibrium, statistical methods based on dimensionality reduction have been proposed. Among them, the partial least squares (PLS) technique stands out, because of its simplicity and high predictive accuracy. However, choosing the optimal number of components remains a relevant issue for PLS applications. Thus, we applied PLS (and principal component and traditional multiple regression) techniques to GS for pork pH traits (with pH measured at 45min and 24h after slaughter) and also identified the optimal number of PLS components based on the degree-of-freedom (DoF) and cross-validation (CV) methods. The PLS method out performs the principal component and traditional multiple regression techniques, enabling satisfactory predictions for pork pH traits using only genotypic data (low-density SNP panel). Furthermore, the SNP marker estimates from PLS revealed a relevant region on chromosome 4, which may affect these traits. The DoF and CV methods showed similar results for determining the optimal number of components in PLS analysis; thus, from the statistical viewpoint, the DoF method should be preferred because of its theoretical background (based on the "statistical information theory"), whereas CV is an empirical method based on computational effort.

Plant attributes leading to persistence in grazed annual medics (Medicago spp.) growing in rotation with wheat

1992 ◽  
Vol 43 (7) ◽  
pp. 1559 ◽  
Author(s):  
PS Cocks
Keyword(s):  
Principal Component Analysis ◽  
Multiple Regression ◽  
Seed Bank ◽  
Principal Component ◽  
Frost Tolerance ◽  
Late Flowering ◽  
Grazed Pasture ◽  
Short Internodes ◽  
Annual Medics ◽  
Sun Light

Attributes of 84 accessions of 12 medics (Medicago spp.) were measured in nursery rows in two successive years. Multiple regression, principal component, and cluster analyses were used to relate 14 attributes of the medics to persistence in the seed bank of grazed pasture growing in rotation with wheat. Principal component analysis distinguished between the attributes of the species. For example, M. rigidula had short petioles, high frost tolerance, many seeds per pod, and large leaves and seeds; while M. polymorpha had long petioles, low frost tolerance, few seeds per pod, and small seeds and leaves. M. noeana produced many flowers per raceme, small pods and seeds, long peduncles, and was hard-seeded and late flowering; while M. aculeata produced few flowers per raceme, large pods and seeds, short peduncles, and was soft-seeded and early flowering. M. trulncatula and M. rotata were intermediate. Long peduncles and high levels of hardseededness were the attributes most closely associated with persistence of the medics in grazed pasture. It was concluded that (1) long peduncles place the flowers above the canopy in spring where they are in full sun light, and (2) hardseededness levels of up to 90% protect seeds against germination in the cereal year. The results also suggest that small leaves and short internodes and petioles protect young plants against over-grazing in winter, and small pods and seeds are less likely than large pods and seeds to be selected and digested by grazing sheep in summer.

scholarly journals Multiple regression models and Artificial Neural Network (ANN) as prediction tools of changes in overall quality during the storage of spreadable processed Gouda cheese

2019 ◽  
Vol 245 (11) ◽  
pp. 2539-2547 ◽  
Author(s):  
J. Stangierski ◽  
D. Weiss ◽  
A. Kaczmarek
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Regression Models ◽  
Principal Component ◽  
Multiple Regression Models ◽  
Gouda Cheese ◽  
Artificial Neural ◽  
Validation Set ◽  
Artificial Neural Network Ann

Abstract The aim of the study was to compare the ability of multiple linear regression (MLR) and Artificial Neural Network (ANN) to predict the overall quality of spreadable Gouda cheese during storage at 8 °C, 20 °C and 30 °C. The ANN used five factors selected by Principal Component Analysis, which was used as input data for the ANN calculation. The datasets were divided into three subsets: a training set, a validation set, and a test set. The multiple regression models were highly significant with high determination coefficients: R2 = 0.99, 0.87 and 0.87 for 8, 20 and 30 °C, respectively, which made them a useful tool to predict quality deterioration. Simultaneously, the artificial neural networks models with determination coefficient of R2 = 0.99, 0.96 and 0.96 for 8, 20 and 30 °C, respectively were built. The models based on ANNs with higher values of determination coefficients and lower RMSE values proved to be more accurate. The best fit of the model to the experimental data was found for processed cheese stored at 8 °C.

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