The present paper aimed to shed light into the relationship between different dimensions of personality and behavioral maximizing and mental standards. To do this, 288 students of Shiraz University from different fields of study (149 females and 139 males) took part as participants. Furthermore, to collect the intended data, two instruments of Goldberg Big Five Personality and Schwartz and coll...

Interannual variability in the abundances of blue crab (Callinectes sapidus) in populations along the U.S. east coast is well documented, but the mechanisms driving these fluctuations remain poorly understood. Using principal component analysis and dynamic factor analysis we quantified the patterns in variability and the degree of synchrony among blue crab populations along the U.S. east coast ...

In this study, the factor analysis techniques is applied to water quality data sets obtained from the Sanganer Tehsil, Jaipur District, Rajasthan (India). The data obtained were standardized and subjected to principal components analysis (PCA) extraction to simplifying its interpretation and to define the parameters responsible for the main variability in water quality for Sanganer Tehsil in Ja...

Personnel differentiation has been viewed as one of the immeasurable competitive advantage for the service sector in the world. Such advantage or human resource based advantage is difficult for a competitor to imitate because the source of the advantage may not be very apparent to an outsider. If the service organization wants to achieve competitive advantage they must differentiate their perso...

Algorithms for distributed agreement are a powerful means for formulating distributed versions of existing centralized algorithms. We present a toolkit for this task and show how it can be used systematically to design fully distributed algorithms for static linear Gaussian models, including principal component analysis, factor analysis, and probabilistic principal component analysis. These alg...

The different methods of factor analysis first extract a set a factors from a data set. These factors are almost always orthogonal and are ordered according to the proportion of the variance of the original data that these factors explain. In general, only a (small) subset of factors is kept for further consideration and the remaining factors are considered as either irrelevant or nonexistent (...

The 21-item version of the Peters et al. Delusions Inventory (PDI-21) is a commonly used tool to measure delusional ideation in the normal population. Two recent principal component analyses have concluded that the PDI-21 has a seven-factor structure. Although these studies found identical factors associated with religiosity and grandiosity, the items loading on the remaining five factors, and ...

Environmental monitoring studies produce huge amounts of concentration values of chemicals spread at distant geographical sites and during different time periods. Moreover, the content of chemicals is also estimated at different environmental compartments (i.e. air, water, sediments, biota...). All these data values are difficult to cope and evaluate in a simple and fast way using simple univar...

In the analysis of MDLAP, this paper creatively combines the mathematical optimization model of cost-based multiple targets distribution location problem into a logistics location selection decision model with a multiple influencing factors, then put forward the method of data standardization processing, entropy weight, the method of principal component analysis and mathematical expressions to ...

Probabilistic finite automata (PFA) model stochastic languages, i.e. probability distributions over strings. Inferring PFA from stochastic data is an open field of research. We show that PFA are identifiable in the limit with probability one. Multiplicity automata (MA) is another device to represent stochastic languages. We show that a MA may generate a stochastic language that cannot be genera...