Multivariate Statistical Analysis of a Regional Subsistence-Settlement Model for Owens Valley

1979 ◽  
Vol 44 (3) ◽  
pp. 455-470 ◽  
Author(s):  
Robert L. Bettinger
Keyword(s):  
Factor Analysis ◽  
Statistical Analysis ◽  
Discriminant Analysis ◽  
Human Ecology ◽  
Multivariate Statistical Analysis ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Owens Valley ◽  
Multivariate Statistical

Despite their growing importance in the study of prehistoric human ecology, regional subsistence-settlement models continue to be developed and justified largely on intuitive grounds. This shortcoming can be at least partially overcome by using multivariate statistical techniques to clarify and refine these models. Such an approach is illustrated using classical factor analysis and discriminant analysis to explicate and improve a regional subsistence-settlement model previously developed for Owens Valley, eastern California.

scholarly journals Micro-SME Characterization From Financial Statements

2012 ◽  
Vol 11 (13) ◽  
pp. 1507
Author(s):  
Guillermo Ceballos-Santamaria ◽  
Juan-Jose Villanueva-Alvaro ◽  
Jose Mondejar-Jimenez
Keyword(s):  
Factor Analysis ◽  
Statistical Analysis ◽  
Small Businesses ◽  
Financial Statements ◽  
Developed Countries ◽  
Financial Statement ◽  
Statistical Techniques ◽  
General Interest ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical

In recent years, small businesses have created interest and research, because they represent the majority of the business fabric and account for over seventy per cent of jobs in developed countries. Governments of these countries share a general interest in knowing about Small and Medium-Sized Enterprises (SME). Based on this premise, the approach of this study is to characterize micro-SMEs in the province of Cuenca, Spain, by analysis of financial statements, specifically analyzing their structure in financial terms by use of univariate and multivariate statistical techniques allowing this kind of business in the province of Cuenca to be identified. The information used comes from the databases of SABI (Iberian Financial Statement Analysis Systems), DIRCE (Central Business Directory and CamerData, the database of the Chambers of Commerce. The statistical analysis is centered on a classic modal of exploratory factor analysis, and finally the main results arising from the study are presented.

scholarly journals Nuclear Magnetic Resonance Metabolomics with Double Pulsed-Field-Gradient Echo and Automatized Solvent Suppression Spectroscopy for Multivariate Data Matrix Applied in Novel Wine and Juice Discriminant Analysis

Molecules ◽  
2021 ◽  
Vol 26 (14) ◽  
pp. 4146
Author(s):  
José Enrique Herbert-Pucheta ◽  
José Daniel Lozada-Ramírez ◽  
Ana E. Ortega-Regules ◽  
Luis Ricardo Hernández ◽  
Cecilia Anaya de Parrodi
Keyword(s):  
Statistical Analysis ◽  
Discriminant Analysis ◽  
Field Gradient ◽  
Multivariate Statistical Analysis ◽  
Data Matrix ◽  
Cabernet Sauvignon ◽  
Gradient Echo ◽  
Multivariate Statistical ◽  
Orthogonal Projections ◽  
Pulsed Field

The quality of foods has led researchers to use various analytical methods to determine the amounts of principal food constituents; some of them are the NMR techniques with a multivariate statistical analysis (NMR-MSA). The present work introduces a set of NMR-MSA novelties. First, the use of a double pulsed-field-gradient echo (DPFGE) experiment with a refocusing band-selective uniform response pure-phase selective pulse for the selective excitation of a 5–10-ppm range of wine samples reveals novel broad 1H resonances. Second, an NMR-MSA foodomics approach to discriminate between wine samples produced from the same Cabernet Sauvignon variety fermented with different yeast strains proposed for large-scale alcohol reductions. Third a comparative study between a nonsupervised Principal Component Analysis (PCA), supervised standard partial (PLS-DA), and sparse (sPLS-DA) least squares discriminant analysis, as well as orthogonal projections to a latent structures discriminant analysis (OPLS-DA), for obtaining holistic fingerprints. The MSA discriminated between different Cabernet Sauvignon fermentation schemes and juice varieties (apple, apricot, and orange) or juice authentications (puree, nectar, concentrated, and commercial juice fruit drinks). The new pulse sequence DPFGE demonstrated an enhanced sensitivity in the aromatic zone of wine samples, allowing a better application of different unsupervised and supervised multivariate statistical analysis approaches.

Multivariate statistic for researchers

2016 ◽  
Author(s):  
Shamshuritawati Sharif ◽  
Hazlina Ali ◽  
Sharipah Soaad Syed Yahaya
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Valuable Resource ◽  
Multivariate Statistical ◽  
Multivariate Statistic ◽  
Linear Discriminant ◽  
New Ideas ◽  
Young Researchers

This book is a valuable resource for those engaged in multivariate statistical techniques. Most chapters include a set of problems and solution that enable readers to overcome the drawback of the classical techniques.It covers a theoretical disadvantage of correlation and covariance test, Hotellings T2 statistic, local influence, and linear discriminant analysis to inspire new or young researchers with new ideas for theoretical improvement.This book is also worthy for people who want to learn multivariate statistics extensively.

STATISTICAL STUDIES ON SCAPOLITES

1964 ◽  
Vol 1 (1) ◽  
pp. 23-34 ◽  
Author(s):  
Gerard V. Middleton
Keyword(s):  
Solid Solution ◽  
Factor Analysis ◽  
Trace Element ◽  
Statistical Techniques ◽  
Trace Element Data ◽  
Multivariate Statistical Techniques ◽  
Factor Analyses ◽  
Multivariate Statistical ◽  
Element Data ◽  
Statistical Studies

The multivariate statistical techniques of component and factor analyses, when applied to major and trace element data presented by Shaw (1960), identify the Marialite–Meionite solid solution in scapolites and tentatively suggest the existence of an independent end-member bearing Mg and (OH). Three trace element factors suggest the groupings[Formula: see text]It is suggested that factor analysis may be a useful technique to apply to other complex mineral groups.

scholarly journals HOW DO BRAZILIANS PERCEIVE FOOD WASTE?

2021 ◽  
Vol 14 (3) ◽  
pp. 246-260
Author(s):  
Giana De Vargas Mores ◽  
Leila Dal Moro ◽  
Yasmin Gomes Casagranda ◽  
Vitor Francisco Dalla Corte ◽  
Gabriele Girardi
Keyword(s):  
Factor Analysis ◽  
Food Waste ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
The Public ◽  
Factors Associated ◽  
Five Factors ◽  
Relevant Topic ◽  
Self Administered Questionnaire

This research aims to identify the profile and factors associated with the perception of Brazilianconsumers regarding food waste. The chosen technique was survey research, with the application ofa structured online and self-administered questionnaire with 664 Brazilian consumers. Descriptivestatistics were calculated and multivariate statistical techniques, such as factor analysis and multiplelinear regression. Five factors have different affirmations on assessing and dealing with the food,besides the behavior concerning food and its respective waste. The factor regarding education wasincluded in the survey, adapted from Richter (2017), which generated this factor. This is an additionalresult when applied to the Brazilian context. The waste is associated with behavioral factors. One ofthe main contributions was to present the use of metrics, which provide comparisons betweendifferent themes of food waste, providing proposals for the public policies and guidelines forminimizing this problem. The study helps with discussions based on a relevant topic for humanity andcontemplating the UN SDGs through a national diagnosis. Academics, public, private, and non-profitorganizations have increasingly brought the spotlight onto food waste. Implications of this study pointto the need for effective policies turned to mitigate food waste.

scholarly journals Factors for Marketing Innovation in Portuguese Firms CIS 2014

2019 ◽  
Vol 24 (4) ◽  
pp. 99 ◽  
Author(s):  
Patrícia Monteiro ◽  
Aldina Correia ◽  
Vítor Braga
Keyword(s):  
Discriminant Analysis ◽  
Intellectual Property Rights ◽  
Organizational Innovation ◽  
Academic Research ◽  
Statistical Techniques ◽  
Technological Innovations ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Marketing Innovation ◽  
Dummy Variable

Globalization, radical and frequent changes as well as the increasing importance of applying knowledge through the efficient implementation of innovation is critical under the current circumstances. Innovation has been the source of businesses competitive advantage, but it is not restricted to technological innovations, and thus marketing innovation also plays a central role. This is a significant topic in the marketing field and not yet deeply analysed in academic research. The main objective of this study is to understand what factors influence marketing innovation and to establish a business profile of firms that innovate or do not in marketing. We used multivariate statistical techniques, such as, multiple linear regression (with the Marketing Innovation Index as dependent variable) and discriminant analysis where the dependent variable is a dummy variable indicating if the firm innovates or not in marketing. The results suggest that there are several factors explaining marketing innovation, although in this study, we find that the factors contributing the most for marketing innovation are: the Organizational Innovation Index, customer and/or user suggestions, and intellectual property rights and licensing (IPRL). Most of the literature has studied these factors separately. This research studied such factors together, and it is clear that both organizational innovation and IPRL play an important role that drives firms to innovate in marketing, which differs from some literature; customer suggestions help in the process of marketing innovation, as some authors argue that customers do not always know what they want until they have it. In parallel, this study proved to be useful in understanding that the different values for the Marketing Innovation Index display no influence on the results, since they were equivalent when a dummy variable (innovated/not innovated in marketing) was used as a dependent variable. In practice, we realize that the factors are useful to clarify what Portuguese firms innovate or not in marketing, with no different results when we the four marketing innovation levels (design, distribution, advertising and price) are considered.

scholarly journals Performance of Linear Discriminant Analysis Using Different Robust Methods

2018 ◽  
Vol 11 (1) ◽  
pp. 284 ◽  
Author(s):  
Mufda Jameel Alrawashdeh ◽  
Taha Radwan Radwan ◽  
Kalid Abunawas Abunawas
Keyword(s):  
Statistical Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Multivariate Statistical Analysis ◽  
Robust Methods ◽  
Minimum Covariance Determinant ◽  
Multivariate Statistical ◽  
Linear Discriminant ◽  
Misclassification Probability ◽  
Conventional Banks

This study aims to combine the new deterministic minimum covariance determinant (DetMCD) algorithm with linear discriminant analysis (LDA) and compare it with the fast minimum covariance determinant (FastMCD), fast consistent high breakdown (FCH), and robust FCH (RFCH) algorithms. LDA classifies new observations into one of the unknown groups and it is widely used in multivariate statistical analysis. The LDA mean and covariance matrix parameters are highly influenced by outliers. The DetMCD algorithm is highly robust and resistant to outliers and it is constructed to overcome the outlier problem. Moreover, the DetMCD algorithm is used to estimate location and scatter matrices. The DetMCD, FastMCD, FCH, and RFCH algorithms are applied to estimate misclassification probability using robust LDA. All the algorithms are expected to improve the LDA model for classification purposes in banks, such as bankruptcy and failures, and to distinguish between Islamic and conventional banks. The performances of the estimators are investigated through simulation and actual data.

scholarly journals Water quality assessment and source identification of the Shuangji River (China) using multivariate statistical methods

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245525
Author(s):  
Junzhao Liu ◽  
Dong Zhang ◽  
Qiuju Tang ◽  
Hongbin Xu ◽  
Shanheng Huang ◽  
...  
Keyword(s):  
Water Quality ◽  
Discriminant Analysis ◽  
Principal Component ◽  
Water Quality Assessment ◽  
Temporal Analysis ◽  
Statistical Techniques ◽  
Complex Data ◽  
Monitoring Networks ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical

Multivariate statistical techniques, including cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and factor analysis (FA), were used to evaluate temporal and spatial variations in and to interpret large and complex water quality datasets collected from the Shuangji River Basin. The datasets, which contained 19 parameters, were generated during the 2 year (2018–2020) monitoring programme at 14 different sites (3192 observations) along the river. Hierarchical CA was used to divide the twelve months into three periods and the fourteen sampling sites into three groups. Discriminant analysis identified four parameters (CODMn, Cu, As, Se) loading more than 68% correct assignations in temporal analysis, while seven parameters (COD, TP, CODMn, F, LAS, Cu and Cd) to load 93% correct assignations in spatial analysis. The FA/PCA identified six factors that were responsible for explaining the data structure of 68% of the total variance of the dataset, allowing grouping of selected parameters based on common characteristics and assessing the incidence of overall change in each group. This study proposes the necessity and practicality of multivariate statistical techniques for evaluating and interpreting large and complex data sets, with a view to obtaining better information about water quality and the design of monitoring networks to effectively manage water resources.

scholarly journals Economic Causes of Extremism Intellectual (Thoughts) Views of Graduate Students Using Multivariate Statistical Techniques

2016 ◽  
Vol 9 (7) ◽  
pp. 160
Author(s):  
Hasan Abdullah Al-Dajah
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Statistical Analysis ◽  
Graduate Students ◽  
Multivariate Statistical Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Multivariate Statistical ◽  
The Impact

The present study investigated the impact of the economic reasons on the intellectual (thoughts) extremism, and the statement of the most important indicators in the economic factor that lead to extremism from the views of graduate students. The study problem based on the following question: What are economic factors leading to the extremism of the intellectual(Thoughts)? Correlation coefficient, Principal component analysis (PCA), varimax (F) rotated factor analysis, and dendrogram cluster analysis (DCA) were assessed for the economic impacts that leads to extremism(Thoughts). Multivariate statistical analysis of the dataset and correlation analysis suggested that the strong positive correlations are commonly associated in the poverty and lack of interest in remote areas for major cities Center. Multivariate statistical analysis such as principal component analysis, varimax rotated factor analysis, and dendrogram cluster analysis allowed the identification of three main factors controlling that lead to extremism from the views of graduate students. The extracted factors are as follows: low living expenses, poverty and substantial deprivation, and unequal opportunities and unemployment associations related to prevalence of corruption phase.

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