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 The Role of Virtual Teams in Increasing the Number of Innovations and Developing Competitive Advantage - Exploratory Data Analysis Study –: دور الفرق الافتراضية في تحسين الابتكار وزيادة الميزة التنافسية - دراسة تحليلية –

2021 ◽  
Vol 5 (13) ◽  
pp. 162-143
Author(s):  
Fatimah Mohamed Mahdy Fatimah Mohamed Mahdy
Keyword(s):  
Data Analysis ◽  
Research And Development ◽  
Competitive Advantage ◽  
Virtual Teams ◽  
Exploratory Data Analysis ◽  
Global Companies ◽  
Modern Business ◽  
Exploratory Data ◽  
Positive Direct

This paper attempts to discover the role that virtual teams play in increasing the number of innovations in the research and development (R&D) department in global companies, and the extent to which this affects achieving a competitive advantage for the organizations under study (SAMSUNG, LG, IBM, and Toyota). The research was based on the method of exploratory analysis of data as the method of the study, and to achieve this goal, the researcher collected data on the number of hypothetical employees assigned to the research and development department in those companies compared to investments and sales and related to the number of innovations during the period between 2009- 2016. The research was based on the method of exploratory data analysis as a method for the study and analysis of the data used. The results of the research concluded that there is a positive direct relationship between the previous variables. Virtual teams are also one of the most important modern methods used in modern business enterprises and their necessity as a result of increasing response and shifting from serial work to simultaneous and parallel work to increase innovation, which leads to an increase in the competitive advantage of these organizations. The study recommends the need to pay attention to building effective virtual teams within organizations because of their essential advantages and to overcome the most important challenges that hinder the effectiveness and success of these teams. By increasing collaboration, interaction and efficiency leadership.

Descriptive Data Analysis: A Concept between Confirmatory and Exploratory Data Analysis

1987 ◽  
Vol 26 (02) ◽  
pp. 77-88 ◽  
Author(s):  
K. Abt
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Analytical Models ◽  
Controlled Study ◽  
Complex Data ◽  
Descriptive Data ◽  
Relevant Effect ◽  
Significance Levels ◽  
Exploratory Data ◽  
Confirmatory Data Analysis

SummaryConfirmatory Data Analysis (CDA) in randomized comparative (“controlled”) studies with many variables and/or time points of interest finds its limitations in the multiplicity of desired inferential statements which leads to unfeasibly small adjusted significance levels (“Bon-ferronization”) and, thereby, to unduly increased risks of not rejecting false hypotheses. In general, analytical models adequate for such complex data structures and suitable for practical use do not exist as yet. Exploratory Data Analysis (EDA), on the other hand, is usually intended to generate hypotheses and not to lead to final conclusions based on the results of the study.In this paper, it is proposed to fill the conceptual gap between CDA and EDA by “Descriptive Data Analysis” (“DDA”) which concept is mainly based on descriptive inferential statements. The results of a DDA in a controlled study are interpreted simultaneously on the basis of the investigator’s experience with respect to numerically relevant treatment effect differences and on “descriptive significances” as they appear in “near regular” patterns corresponding to the resulting relevant effect differences. A DDA may also contain confirmatory parts and/or tests on global hypotheses at a prechosen maximum risk α of erroneously rejecting true hypotheses. The paper is in parts expository and is addressed to investigators as well as statisticians.

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 Statistical inference for exploratory data analysis and model diagnostics

2009 ◽  
Vol 367 (1906) ◽  
pp. 4361-4383 ◽  
Author(s):  
Andreas Buja ◽  
Dianne Cook ◽  
Heike Hofmann ◽  
Michael Lawrence ◽  
Eun-Kyung Lee ◽  
...  
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Statistical Tests ◽  
Statistical Significance ◽  
Statistical Testing ◽  
Human Cognition ◽  
Model Diagnostics ◽  
The Real ◽  
Exploratory Data

We propose to furnish visual statistical methods with an inferential framework and protocol, modelled on confirmatory statistical testing. In this framework, plots take on the role of test statistics, and human cognition the role of statistical tests. Statistical significance of ‘discoveries’ is measured by having the human viewer compare the plot of the real dataset with collections of plots of simulated datasets. A simple but rigorous protocol that provides inferential validity is modelled after the ‘lineup’ popular from criminal legal procedures. Another protocol modelled after the ‘Rorschach’ inkblot test, well known from (pop-)psychology, will help analysts acclimatize to random variability before being exposed to the plot of the real data. The proposed protocols will be useful for exploratory data analysis, with reference datasets simulated by using a null assumption that structure is absent. The framework is also useful for model diagnostics in which case reference datasets are simulated from the model in question. This latter point follows up on previous proposals. Adopting the protocols will mean an adjustment in working procedures for data analysts, adding more rigour, and teachers might find that incorporating these protocols into the curriculum improves their students’ statistical thinking.

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.

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