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.

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 Longitudinal Magnetic Resonance Imaging as a Potential Correlate in the Diagnosis of Alzheimer Disease: Exploratory Data Analysis (Preprint)

2019 ◽  
Author(s):  
Afreen Khan ◽  
Swaleha Zubair
Keyword(s):  
Magnetic Resonance Imaging ◽  
Data Analysis ◽  
Magnetic Resonance ◽  
Alzheimer Disease ◽  
Exploratory Data Analysis ◽  
Resonance Imaging ◽  
Gender Education ◽  
Longitudinal Mri ◽  
Exploratory Data

BACKGROUND Alzheimer disease (AD) is a degenerative progressive brain disorder where symptoms of dementia and cognitive impairment intensify over time. Numerous factors exist that may or may not be related to the lifestyle of a patient that result in a higher risk for AD. Diagnosing the disorder in its beginning period is important, and several techniques are used to diagnose AD. A number of studies have been conducted on the detection and diagnosis of AD. This paper reports the empirical study performed on the longitudinal-based magnetic resonance imaging (MRI) Open Access Series of Brain Imaging dataset. Furthermore, the study highlights several factors that influence the prediction of AD. OBJECTIVE This study aimed to correlate the effect of various factors such as age, gender, education, and socioeconomic background of patients with the development of AD. The effect of patient-related factors on the severity of AD was assessed on the basis of MRI features, Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), estimated total intracranial volume (eTIV), normalized whole brain volume (nWBV), and Atlas Scaling Factor (ASF). METHODS In this study, we attempted to establish the role of longitudinal MRI in an exploratory data analysis (EDA) of AD patients. EDA was performed on the dataset of 150 patients for 343 MRI sessions (mean age 77.01 [SD 7.64] years). The T1-weighted MRI of each subject on a 1.5-Tesla Vision (Siemens) scanner was used for image acquisition. Scores of three features, MMSE, CDR, and ASF, were used to characterize the AD patients included in this study. We assessed the role of various features (ie, age, gender, education, socioeconomic status, MMSE, CDR, eTIV, nWBV, and ASF) on the prognosis of AD. RESULTS The analysis further establishes the role of gender in the prevalence and development of AD in older people. Moreover, a considerable relationship has been observed between education and socioeconomic position on the progression of AD. Also, outliers and linearity of each feature were determined to rule out the extreme values in measuring the skewness. The differences in nWBV between CDR=0 (nondemented), CDR=0.5 (very mild dementia), and CDR=1 (mild dementia) are significant (ie, <i>P</i>&lt;.01). CONCLUSIONS A substantial correlation has been observed between the pattern and other related features of longitudinal MRI data that can significantly assist in the diagnosis and determination of AD in older patients.

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

Chemometric exploratory data analysis for patients with diabetes type 2 and diabetic complications

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Miroslava Nedyalkova ◽  
Ralitsa Robeva ◽  
Atanaska Elenkova ◽  
Vasil Simeonov
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Diabetes Type 2 ◽  
Latent Factors ◽  
Multivariate Statistical ◽  
Patients With Diabetes ◽  
Exploratory Data ◽  
Components Analysis

Abstract The present study deals with the interpretation and modeling of clinical data for patients with diabetes mellitus type 2 (DMT2) additionally diagnosed with complications of the disease by the use of multivariate statistical methods. The major goal is to determine some specific clinical descriptors characterizing each health problem by applying the options of the exploratory data analysis. The results from the statistical analysis are commented in details by medical reasons for each of the complications. It was found that each of the complications is characterized by specific medical descriptors linked into each one of the five latent factors identified by factor and principal components analysis. Such an approach to interpret concomitant to DMT2 complications is original and allows a better understanding of the role of clinical parameters for diagnostic and prevention goals.

scholarly journals Longitudinal Magnetic Resonance Imaging as a Potential Correlate in the Diagnosis of Alzheimer Disease: Exploratory Data Analysis

10.2196/14389 ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. e14389 ◽  
Author(s):  
Afreen Khan ◽  
Swaleha Zubair
Keyword(s):  
Magnetic Resonance Imaging ◽  
Data Analysis ◽  
Magnetic Resonance ◽  
Alzheimer Disease ◽  
Exploratory Data Analysis ◽  
Resonance Imaging ◽  
Gender Education ◽  
Longitudinal Mri ◽  
Exploratory Data

Background Alzheimer disease (AD) is a degenerative progressive brain disorder where symptoms of dementia and cognitive impairment intensify over time. Numerous factors exist that may or may not be related to the lifestyle of a patient that result in a higher risk for AD. Diagnosing the disorder in its beginning period is important, and several techniques are used to diagnose AD. A number of studies have been conducted on the detection and diagnosis of AD. This paper reports the empirical study performed on the longitudinal-based magnetic resonance imaging (MRI) Open Access Series of Brain Imaging dataset. Furthermore, the study highlights several factors that influence the prediction of AD. Objective This study aimed to correlate the effect of various factors such as age, gender, education, and socioeconomic background of patients with the development of AD. The effect of patient-related factors on the severity of AD was assessed on the basis of MRI features, Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), estimated total intracranial volume (eTIV), normalized whole brain volume (nWBV), and Atlas Scaling Factor (ASF). Methods In this study, we attempted to establish the role of longitudinal MRI in an exploratory data analysis (EDA) of AD patients. EDA was performed on the dataset of 150 patients for 343 MRI sessions (mean age 77.01 [SD 7.64] years). The T1-weighted MRI of each subject on a 1.5-Tesla Vision (Siemens) scanner was used for image acquisition. Scores of three features, MMSE, CDR, and ASF, were used to characterize the AD patients included in this study. We assessed the role of various features (ie, age, gender, education, socioeconomic status, MMSE, CDR, eTIV, nWBV, and ASF) on the prognosis of AD. Results The analysis further establishes the role of gender in the prevalence and development of AD in older people. Moreover, a considerable relationship has been observed between education and socioeconomic position on the progression of AD. Also, outliers and linearity of each feature were determined to rule out the extreme values in measuring the skewness. The differences in nWBV between CDR=0 (nondemented), CDR=0.5 (very mild dementia), and CDR=1 (mild dementia) are significant (ie, P<.01). Conclusions A substantial correlation has been observed between the pattern and other related features of longitudinal MRI data that can significantly assist in the diagnosis and determination of AD in older patients.

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