Exploratory Data Analysis, Display and Summary of Multivariate Data

2011 ◽  
pp. 63-92
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Multivariate Data ◽  
Exploratory Data

scholarly journals A Sensor-Based Methodology to Differentiate Pure and Mixed White Tequilas Based on Fused Infrared Spectra and Multivariate Data Treatment

Chemosensors ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 47
Author(s):  
Christian Hazael Pérez-Beltrán ◽  
Víctor M. Zúñiga-Arroyo ◽  
José M. Andrade ◽  
Luis Cuadros-Rodríguez ◽  
Guadalupe Pérez-Caballero ◽  
...  
Keyword(s):  
Data Analysis ◽  
Ir Spectra ◽  
Multivariate Data Analysis ◽  
Exploratory Data Analysis ◽  
Multivariate Data ◽  
Simple Application ◽  
Infrared Measurements ◽  
Exploratory Data ◽  
Pls Discriminant Analysis

Mexican Tequila is one of the most demanded import spirits in Europe. Its fast-raising worldwide request makes counterfeiting a profitable activity affecting both consumers and legal distillers. In this paper, a sensor-based methodology based on a combination of infrared measurements (IR) and multivariate data analysis (MVA) is presented. The case study is about differentiating two categories of white Tequila: pure Tequila (or ‘100% agave’) and mixed Tequila (or simply, Tequila). The IR spectra were treated and fused with a low-level approach. Exploratory data analysis was performed using PCA and partial least squares (PLS), whilst the authentication analyses were carried out with PLS-discriminant analysis (DA) and soft independent modeling for class analogy (SIMCA) models. Results demonstrated that data fusion of IR spectra enhanced the outcomes of the authentication models capable of differentiating pure from mixed Tequilas. In fact, PLS-DA presented the best results which correctly classified all fifteen commercial validation samples. The methodology thus presented is fast, cheap, and of simple application in the Tequila industry.

Visualization of Multivariate Data with Tail Trees

2007 ◽  
Vol 6 (2) ◽  
pp. 109-122 ◽  
Author(s):  
Jussi Klemelä
Keyword(s):  
Data Analysis ◽  
Point Cloud ◽  
Exploratory Data Analysis ◽  
Multivariate Data ◽  
Tree Structure ◽  
Financial Data ◽  
Data Set ◽  
Grand Tour ◽  
Root Node ◽  
Exploratory Data

We introduce graphical tools to visualize the shape, the location, and the orientation of a multivariate data set. We define a tree structure among the observations, called a tail tree. A tail tree is a tree whose root node corresponds to a center point of the data, and whose branches correspond to the tails of the data. We visualize a tail tree with a tail tree plot. Visualizing the tree structure among the observations makes it feasible to detect features from the data. A tail tree may also be used to define and enhance other visualizations. We define a tail frequency plot which visualizes the empirical probabilities of the disconnected tails of the point cloud. A tail tree induces a segmentation of the data which may be used to enhance a grand tour, graphical matrices, and parallel coordinate plots. We apply tail tree plots in exploratory data analysis of financial data.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Covid-19 Cases in India: A Visual Exploratory Data Analysis Model (Preprint)

2020 ◽  
Author(s):  
Jayesh S
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Case Fatality ◽  
Public Health Emergency ◽  
Virus Spread ◽  
Analysis Model ◽  
Case Fatality Ratio ◽  
First Case ◽  
Exploratory Data ◽  
The Government

UNSTRUCTURED Covid-19 outbreak was first reported in Wuhan, China. The deadly virus spread not just the disease, but fear around the globe. On January 2020, WHO declared COVID-19 as a Public Health Emergency of International Concern (PHEIC). First case of Covid-19 in India was reported on January 30, 2020. By the time, India was prepared in fighting against the virus. India has taken various measures to tackle the situation. In this paper, an exploratory data analysis of Covid-19 cases in India is carried out. Data namely number of cases, testing done, Case Fatality ratio, Number of deaths, change in visits stringency index and measures taken by the government is used for modelling and visual exploratory data analysis.

scholarly journals Multivariate Statistical Approach for Nephrines in Women with Obesity

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1393
Author(s):  
Ralitsa Robeva ◽  
Miroslava Nedyalkova ◽  
Georgi Kirilov ◽  
Atanaska Elenkova ◽  
Sabina Zacharieva ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Metabolic Syndrome ◽  
Data Analysis ◽  
Stress Response ◽  
Exploratory Data Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Obese Women ◽  
Exploratory Data

Catecholamines are physiological regulators of carbohydrate and lipid metabolism during stress, but their chronic influence on metabolic changes in obese patients is still not clarified. The present study aimed to establish the associations between the catecholamine metabolites and metabolic syndrome (MS) components in obese women as well as to reveal the possible hidden subgroups of patients through hierarchical cluster analysis and principal component analysis. The 24-h urine excretion of metanephrine and normetanephrine was investigated in 150 obese women (54 non diabetic without MS, 70 non-diabetic with MS and 26 with type 2 diabetes). The interrelations between carbohydrate disturbances, metabolic syndrome components and stress response hormones were studied. Exploratory data analysis was used to determine different patterns of similarities among the patients. Normetanephrine concentrations were significantly increased in postmenopausal patients and in women with morbid obesity, type 2 diabetes, and hypertension but not with prediabetes. Both metanephrine and normetanephrine levels were positively associated with glucose concentrations one hour after glucose load irrespectively of the insulin levels. The exploratory data analysis showed different risk subgroups among the investigated obese women. The development of predictive tools that include not only traditional metabolic risk factors, but also markers of stress response systems might help for specific risk estimation in obesity patients.

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