Extracting activity patterns: Exploratory data analysis on a fucoidan extract data set with mixed variables

2021 ◽  
Vol 54 ◽  
pp. 102220
Author(s):  
Signe H. Ptak ◽  
Massimiliano Errico ◽  
Knud V. Christensen
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Activity Patterns ◽  
Data Set ◽  
Mixed Variables ◽  
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

scholarly journals Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen’s Self-Organizing Map

2006 ◽  
Vol 3 (4) ◽  
pp. 1487-1516 ◽  
Author(s):  
L. Peeters ◽  
F. Bação ◽  
V. Lobo ◽  
A. Dassargues
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Three Dimensional ◽  
Spatial Knowledge ◽  
Self Organizing Map ◽  
Data Set ◽  
Hydrochemical Data ◽  
Som Algorithm ◽  
Exploratory Data ◽  
Self Organizing

Abstract. The use of unsupervised artificial neural network techniques like the self-organizing map (SOM) algorithm has proven to be a useful tool in exploratory data analysis and clustering of multivariate data sets. In this study a variant of the SOM-algorithm is proposed, the GEO3DSOM, capable of explicitly incorporating three-dimensional spatial knowledge into the algorithm. The performance of the GEO3DSOM is compared to the performance of the standard SOM in analyzing an artificial data set and a hydrochemical data set. The hydrochemical data set consists of 141 groundwater samples collected in two detritic, phreatic, Cenozoic aquifers in Central Belgium. The standard SOM proves to be more adequate in representing the structure of the data set and to explore relationships between variables. The GEO3DSOM on the other hand performs better in creating spatially coherent groups based on the data.

scholarly journals Gender and Ultimatum in Pakistan: Revisited

2014 ◽  
Vol 53 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Saima Naeem ◽  
Asad Zaman
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Ultimatum Game ◽  
Original Data ◽  
Jel Classification ◽  
Data Set ◽  
Analysis Techniques ◽  
Exploratory Data ◽  
Confirmatory Data Analysis

Razzaque (2009) studied the role of gender in the ultimatum game by running experiments on students in various cities in Pakistan. He used standard confirmatory data analysis techniques, which work well in familiar contexts, where relevant hypotheses of interest are known in advance. Our goal in this paper is to demonstrate that exploratory data analysis is much better suited to the study of experimental data where the goal is to discover patterns of interest. Our exploratory re-analysis of the original data set of Razzaque (2009) leads to several new insights. While we re-confirm the main finding of Razzaque regarding the greater generosity of males, additional analysis suggests that this is driven by student subculture in Pakistan, and would not generalise to the population at large. In addition, we find strong effect of urbanisation. Our exploratory data analysis also offers considerable additional insights into the learning process that takes place over the course of a sequence of games. JEL Classification: C78, C81, C91, J16 Keywords: Ultimatum Game, Gender Differences, Exploratory Data Analysis

scholarly journals Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen's Self-Organizing Map

2007 ◽  
Vol 11 (4) ◽  
pp. 1309-1321 ◽  
Author(s):  
L. Peeters ◽  
F. Bação ◽  
V. Lobo ◽  
A. Dassargues
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Spatial Knowledge ◽  
Quality Data ◽  
Self Organizing Map ◽  
Data Set ◽  
Hydrochemical Data ◽  
Som Algorithm ◽  
Exploratory Data ◽  
Self Organizing

Abstract. The use of unsupervised artificial neural network techniques like the self-organizing map (SOM) algorithm has proven to be a useful tool in exploratory data analysis and clustering of multivariate data sets. In this study a variant of the SOM-algorithm is proposed, the GEO3DSOM, capable of explicitly incorporating three-dimensional spatial knowledge into the algorithm. The performance of the GEO3DSOM is compared to the performance of the standard SOM in analyzing an artificial data set and a hydrochemical data set. The hydrochemical data set consists of 131 groundwater samples collected in two detritic, phreatic, Cenozoic aquifers in Central Belgium. Both techniques succeed very well in providing more insight in the groundwater quality data set, visualizing the relationships between variables, highlighting the main differences between groups of samples and pointing out anomalous wells and well screens. The GEO3DSOM however has the advantage to provide an increased resolution while still maintaining a good generalization of the data set.

Quantitative and Visual Exploratory Data Analysis for Machine Intelligence

2021 ◽  
pp. 97-117
Author(s):  
Dharmendra Trikamlal Patel
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Machine Intelligence ◽  
Statistical Techniques ◽  
Data Sets ◽  
Future Trends ◽  
Data Set ◽  
Conventional View ◽  
Exploratory Data ◽  
Analyze Data

Exploratory data analysis is a technique to analyze data sets in order to summarize the main characteristics of them using quantitative and visual aspects. The chapter starts with the introduction of exploratory data analysis. It discusses the conventional view of it and describes the main limitations of it. It explores the features of quantitative and visual exploratory data analysis in detail. It deals with the statistical techniques relevant to EDA. It also emphasizes the main visual techniques to represent the data in an efficient way. R has extraordinary capabilities to deal with quantitative and visual aspects to summarize the main characteristics of the data set. The chapter provides the practical exposure of various plotting systems using R. Finally, the chapter deals with current research and future trends of the EDA.

Semiparametric models for antagonistic drug interactions

1994 ◽  
Vol 76 (5) ◽  
pp. 2224-2233 ◽  
Author(s):  
I. F. Troconiz ◽  
L. B. Sheiner ◽  
D. Verotta
Keyword(s):  
Data Analysis ◽  
Drug Interactions ◽  
Exploratory Data Analysis ◽  
Real Data ◽  
Data Set ◽  
Semiparametric Modeling ◽  
New Class ◽  
Semiparametric Approach ◽  
Exploratory Data ◽  
Model Determination

A new class of models to describe antagonistic drug interactions are presented. They are semiparametric in that they use nonparametric functions (splines) but are forced to obey certain constraints corresponding to reasonable assumptions. We propose the models primarily for exploratory data analysis, but they may also be definitive models for such purposes as predicting future responses. Certain problems that arise in semiparametric modeling, such as model selection, are addressed so that we can propose a relatively automatic and objective approach to model determination. We demonstrate the applicability of the class of models we propose to two real data set examples involving pain relief response to opioid agonists/antagonists. The results suggest that the semiparametric approach is particularly useful when unusual shapes link dose to response.

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.

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