Assessment of the water quality and identification of pollution sources of Kaduna River in Niger State (Nigeria) using exploratory data analysis

2012 ◽  
Vol 28 (1) ◽  
pp. 31-37 ◽  
Author(s):  
Toochukwu Chibueze Ogwueleka
Keyword(s):  
Water Quality ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Pollution Sources ◽  
Exploratory Data

Techniques for Exploring and Presenting Data Applied to Lake Phosphorus Concentration

1980 ◽  
Vol 37 (2) ◽  
pp. 290-294 ◽  
Author(s):  
K. H. Reckhow
Keyword(s):  
Water Quality ◽  
Data Analysis ◽  
Information Transfer ◽  
Exploratory Data Analysis ◽  
Statistical Significance ◽  
Phosphorus Concentration ◽  
Data Set ◽  
Exploratory Data ◽  
Water Quality Models ◽  
Single Graph

Water quality sampling and data analysis are undertaken to acquire and convey information. Therefore, when data are presented, the form of this presentation should be such that information transfer is high. For example, a graph or table of average values is often an inadequate summary of batches of data. As an alternative, a technique is presented (that was developed for exploratory data analysis purposes) that can be used to display several sets of data on a single graph, indicating median, spread, skew, size of data set, and statistical significance of the median. This technique is useful in the study of phosphorus concentration variability in lakes. Additions to, and modifications of, this procedure are easily made and will often enhance the analysis of a particular problem. Some suggestions are made for useful modifications of the plots in the study and display of phosphorus lake data and models.Key words: limnology, exploratory data analysis, statistics, phosphorus, water quality, models, lakes

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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