1,721,025 research outputs found
Determining Critical Success Factors Related to the Effect of Supply Chain Integration and Competition Capabilities on Business Performance
No full textSimonetti, Biagio/0000-0002-2540-7830This study analyzes those critical success factors related to supply chain integration (SCI) and competition capabilities (CC) and which have more effect on business performance (BP) by using a structural equation model. For this purpose, the relationship between integration, CC and BP has been analyzed. Data was obtained from the survey that applied to Turkish Small and Medium Sized Enterprises (SMEs) and we examined the critical factors by using a Structural Equation Model to analyze which factors have more effect on BP. As a result of the study it was found that there are positive associations between SCI and CC, and both SCI-CC and BP and it was also found that most critical factor that affects BP is reliability and the least important one is lower price
On Fuzzy Regression Adapting Partial Least Squares
Full text linkPartial Least Squared (PLS) regression is a model linking a dependent variable y to a set of X (numerical or categorical) explanatory variables. It can be obtained as a series of simple and multiple regressions of simple and multiple regressions. PLS is an alternative to classical regression model when there are many variables or the variables are correlated. On the other hand, an alternative method to regression in order to model data has been studied is called Fuzzy Linear Regression (FLR). FLR is one of the modelling techniques based on fuzzy set theory. It is applied to many diversified areas such as engineering, biology, finance and so on. Development of FLR follows mainly two paths. One of which depends on improving the parameter estimation methods. This enables to compute more reliable and more accurate parameter estimation in fuzzy setting. Second of which is related to applying these methods to data, which usually do not follow strict assumptions. The application point of view of FLR has not been examined widely except outlier case. For example, it has not been widely examined how FLR behaves under the multivariate case. To overcome such a problem in classic setting, one of the methods that are practically useful is PLS. In this paper, FLR is examined based on application point of view when it has several explanatory variables by adapting PLS
A dimensional reduction method for ordinal three-way contingency table
No full textFor the study of association in three-way, and more generally multiway, contingency tables the literature offers a large number of techniques that can be considered. When there is an asymmetric dependence structure between the variables the Marcotorchino index [Mar84] (as apposed to the Pearson chi-squared statistic) can be used to measure the strength of their association. When the variables have an ordinal structure, this information is often not take into account. In this paper we introduce a partition of the Marcotorchino index for three ordered categorical variables using a special class of orthogonal polynomials. A graphical procedure is also considered to obtain a visual summary of the asymmetrical relationship between the variables
new developments in ordinal non simmetrical correspondence analysis
No full textFor the study of association in two and three-way contingency tables the
literature offers a large number of techniques that can be considered.When there is
an asymmetric dependence structure between the variables, the Goodman-Kruskal
and Marcotorchino index (with respect to the Pearson chi-squared statistic) can be
used to measure the strength of their association when they are collected in two and
three way contingency tables, respectively. In the last years, special attention has
been paid to the graphical representation of the dependence structure between two
or more variables, preserving the information arising from the ordinal structure of
themodalities. In this paper, the authors synthesize themain proposals falling within
the framework called Ordinal Non Symmetrical Correspondence Analysis for two
and three way contingency tables
Residents’ attitudes towards tourism development: evaluation and management in Matera city destination
No full textFuzzy Correlation and Fuzzy Non-linear Regression Analysis
No full textIn this chapter, we will deal with fuzzy correlation and fuzzy non-linear regression analyses. Both correlation and regression analyses that are useful and widely employed statistical tools have been redefined in the framework of fuzzy set theory in order to comprehend relation and to model observations of variables collected as either qualitative or approximately known quantities which are no longer being utilized directly in classical sense. When fuzzy correlation and fuzzy non-linear regression are concern, dealing with several computational complexities emerging due to the nature of fuzzy set theory is a challenge. It should be noted that there is no well-established formula or method in order to calculate fuzzy correlation coefficient or to estimate parameters of the fuzzy regression model. Therefore, a rich literature will accompany with the readers. While extension principle based methods are utilized in the computational procedures for fuzzy correlation coefficient, the distance based methods preferred rather than mathematical programming ones are employed in parameter estimation of fuzzy regression models. That extension principle combined with either fuzzy arithmetic or non-linear programming is two different methods proposed in the literature will be examined with small but illustrative examples in detail for fuzzy correlation analysis. Fuzzy non-linear regression has been a relatively new studied method when compared to fuzzy linear regression. However, both employ similar tools. S-curve fuzzy regression and two types of quadratic fuzzy regression models in the literature will be discussed
Correspondence Analysis of Cumulative Frequencies using a Decomposition of Taguchi's Statistic
No full textTaguchi's statistic has long been known to be a more appropriate measure of association for ordinal variables than the Pearson chi-squared statistic. Therefore, there is some advantage in using Taguchi's statistic for performing correspondence analysis when a two-way contingency table consists of one ordinal categorical variable. This article will explore the development of correspondence analysis using a decomposition of Taguchi's statistic
Does Islamic Capital Asset Pricing Model Outperform Conventional Capital Asset Pricing Model?
No full textCumulative correspondence analysis of ordered categorical data from industrial experiments
No full textMost studies of quality improvement deal with ordered categorical data from industrial experiments. Accounting for the ordering of such data plays an important role in effectively determining the optimal factor level of combination. This paper utilizes the correspondence analysis to develop a procedure to improve the ordered categorical response in a multifactor state system based on Taguchi's statistic. Users may find the proposed procedure in this paper to be attractive because we suggest a simple and also popular statistical tool for graphically identifying the really important factors and determining the levels to improve process quality. A case study for optimizing the polysilicon deposition process in a very large-scale integrated circuit is provided to demonstrate the effectiveness of the proposed procedure.ordered categories, correspondence analysis, quality engineering, experimental design, Taguchi's statistic,
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