The Use of Exploratory Data Analysis Techniques for Software Reliability Assessment and Prediction

1986 ◽  
pp. 337-351 ◽  
Author(s):  
Tony Bendell
Keyword(s):  
Data Analysis ◽  
Software Reliability ◽  
Exploratory Data Analysis ◽  
Reliability Assessment ◽  
Analysis Techniques ◽  
Exploratory 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

Exploratory Data Analysis Techniques to Determine the Dimensionality of Complex Nonlinear Phenomena: The L-to-H Transition at JET as a Case Study

2012 ◽  
Vol 40 (5) ◽  
pp. 1386-1394 ◽  
Author(s):  
Andrea Murari ◽  
Didier Mazon ◽  
N. Martin ◽  
Guido Vagliasindi ◽  
Michela Gelfusa
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Nonlinear Phenomena ◽  
Analysis Techniques ◽  
Exploratory Data

scholarly journals Exploratory versus experimental design: overcoming the prejudice of ‘data dredging’.

2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Polly Pascoe ◽  
Sarah Jane Jones
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Social Care ◽  
Mental Healthcare ◽  
Research Programme ◽  
Real World Data ◽  
Analysis Techniques ◽  
Health And Social Care ◽  
Exploratory Data ◽  
Data Dredging

As part of its most recent Efficiency Research Programme, which sought to advance understanding of workforce retention in health and social care, the Health Foundation have provided funding to a team within the Centre for Social Care, Health and Related Research at Birmingham City University. This project proposes to utilise exploratory data analysis techniques to investigate the underpinning contributory factors to nurse retention and ultimately patient safety across 10 acute and 10 mental healthcare providers. Exploratory data analysis techniques do not conform to the accepted hypothesis setting and testing associated with the traditional experimental research design within medicine and consequently, can be subject to criticism. However, these techniques afford bountiful opportunity to generate insight from real-world data; insight that could not be gleamed from experimental designs, therefore they warrant further investigation before being written off as ‘data dredging’. Exploratory data analysis techniques applied to large datasets that are routinely collected as part of standard care have the potential to revolutionise healthcare. Machine learning and artificial intelligence are becoming common place in everyday life but have been relatively slow to be adopted as a mechanism for improving healthcare. An aspect of the medical community’s reluctance to accept these techniques is a perceived lack of robustness in determining relationships, as hypotheses aren’t typically generated in advance and therefore the process exposes itself to unscrupulous actions where analyses are undertaken until a relationship is found. An element of this programme of work is to develop a set of standards for the application of these techniques to healthcare-generated data including, but not limited to and housing data in open access formats to improve transparency and reporting on findings. This session seeks to explore these issues with colleagues across disciplines, generating debate regarding novel approaches to complex issues.

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