Box Plots: Basic and Advanced

1990 ◽  
Vol 83 (2) ◽  
pp. 108-112
Author(s):  
James L. Mullenex
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Statistical Techniques ◽  
Graphical Representations ◽  
Box Plots ◽  
Exploratory Data

Box plots are used for the purpose of analyzing and displaying important features of sets of data. More specifically, box plots are used as graphical representations of five-number summaries. Box plots and five-number summaries are new statistical techniques that were developed by John W. Tukey of Bell Telephone Laboratories. They are parts of a larger set of modern statistical techniques known collectively as exploratory data analysis, or EDA.

scholarly journals Range-Box Plotting Relating to Discrete Distribution

MATEMATIKA ◽  
2018 ◽  
Vol 34 (2) ◽  
pp. 187-204 ◽  
Author(s):  
Mohd Bakri Adam ◽  
Babaginda Ibrahim Babura ◽  
Kathiresan Gopal
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Discrete Distribution ◽  
Box Plots ◽  
Long Time ◽  
Exploratory Data ◽  
Asymmetrical Shape ◽  
Box Plot ◽  
The Creation

The box plot has been used for a very long time since 70s in checking the existence of outliers and the asymmetrical shape of data. The existing box plot is constructed using ve values of statistics calculated from either the discrete or continous data. Many improvement of box plots have deviated from the elegant and simplier approach of exploratory data analysis by incorporating many other statistic values resulting the turning back of the noble philosophy behind the creation of box plot.The modication using range value with the minimum and maximum values are being incorporated to suit the need of selected discrete distribution when outliers is not an important criteria anymore. The new modication of box plot is not based on the asymmetrical shape of distribution but more on the spread of the data and partitioning data into range measure. The new propose name for the box plot with only three values of statistics is called range-box plot.

scholarly journals An Approach Based on the Exploratory Data Analysis to Relate the Wear Behavior with the Microstructure of Ductile Cast Irons

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Jesus A. Basurto-Hurtado ◽  
L. A. Morales-Hernández ◽  
Roque A. Osornio-Rios ◽  
Aurelio Dominguez-Gonzalez
Keyword(s):  
Image Processing ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Wear Behavior ◽  
Cast Irons ◽  
Morphological Image Processing ◽  
Box Plots ◽  
Exploratory Data ◽  
Morphological Image ◽  
Microstructure Of The Material

The aim of this work is to propose a new methodology to relate Ductile Cast Irons (DCIs) wear behavior with the separation distances and sizes of the graphite nodules through an Exploratory Data Analysis (EDA). This methodology consists of morphological image processing tools (compacity and size distribution curves), an EDA performed by the use of box plots and an EDA-based section classifying algorithm. This algorithm classifies the microstructure of DCIs into classes and levels grouping different behaviors of the separation distances and sizes of graphite nodules. Finally, it was found, through a number of tribological tests, that the obtained classes and levels have a different wear behavior. The results achieved by this methodology were compared with those of traditional techniques used to characterize the microstructure of the material.

Student-generated Data in Elementary Statistics

1990 ◽  
Vol 83 (4) ◽  
pp. 322-325
Author(s):  
Ken Kundert
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Mathematics Curriculum ◽  
Box Plots ◽  
Curve Smoothing ◽  
Statistics Course ◽  
School Classroom ◽  
Exploratory Data ◽  
High School Classroom ◽  
Elementary Statistics

The recent increased emphasis on the teaching of statistics in the high school classroom has focused primarily on the techniques of exploratory data analysis. Topics include stem-and-leaf plots, box plots, median-fit lines, and curve smoothing. A number ofhigh schools, however, still teach a course in statistics for the college-bound student. Included in this course are many of the classical topics of statistics generally found in an elementary statistics course taught to college students, with only intermediate algebra as a prerequisite. Although this article highlights selected topics in such a course and describes how student-generated data can be used to illustrate these topics, the basic idea can profitably be used throughout the mathematics curriculum.

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.

scholarly journals MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets

2019 ◽  
Author(s):  
Urminder Singh ◽  
Manhoi Hur ◽  
Karin Dorman ◽  
Eve Wurtele
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Permutation Test ◽  
Human Cancer ◽  
Easy Access ◽  
Differential Correlation ◽  
Box Plots ◽  
Scatter Plots ◽  
Hidden Knowledge ◽  
Exploratory Data

The diverse and growing omics data in public domains provide researchers with a tremendous opportunity to extract hidden knowledge. However, the challenge of providing domain experts with easy access to these big data has resulted in the vast majority of archived data remaining unused. Here, we present MetaOmGraph (MOG), a free, open-source, standalone software for exploratory data analysis of massive datasets by scientific researchers. Using MOG, a researcher can interactively visualize and statistically analyze the data, in the context of its metadata. Researchers can interactively hone-in on groups of experiments or genes based on attributes such as expression values, statistical results, metadata terms, and ontology annotations. MOG’s statistical tools include coexpression, differential expression, and differential correlation analysis, with permutation test-based options for significance assessments. Multithreading and indexing enable efficient data analysis on a personal computer, with no need for writing code. Data can be visualized as line charts, box plots, scatter plots, and volcano plots. A researcher can create new MOG projects from any data or analyze an existing one. An R-wrapper lets a researcher select and send smaller data subsets to R for additional analyses. A researcher can save MOG projects with a history of the exploratory progress and later reopen or share them. We illustrate MOG by case studies of large curated datasets from human cancer RNA-Seq, in which we assembled a list of novel putative biomarker genes in different tumors, and microarray and metabolomics from A. thaliana.

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.

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