Why Data Analysis?

1990 ◽  
Vol 83 (2) ◽  
pp. 90-93
Author(s):  
Richard L. Scheaffer
Keyword(s):  
Data Analysis ◽  
Empirical Data ◽  
Exploratory Data Analysis ◽  
Applied Statistics ◽  
Statistical Education ◽  
The Past ◽  
Classical Statistics ◽  
Exploratory Data ◽  
To Come ◽  
Oriented Approach

Recent years have witnessed a strong movement away from what might be termed classical statistics to a more empirical, data-oriented approach to statistics, sometimes termed exploratory data analysis, or EDA. This movement has been active among professional statisticians for twenty or twenty-five years but has begun permeating the area of statistical education for nonstatisticians only in the past five to ten years. At this point, there seems to be little doubt that EDA approaches to applied statistics will gain support over classical approaches in the years to come. That is not to say that classical statistics will disappear. The two approaches begin with different assumptions and have different objectives, but both are important. These differences will be outlined in this article.

TRENDS IN THE EVOLUTION OF SCHOLARLY ACCOUNTING THOUGHT: A QUANTITATIVE EXAMINATION

1988 ◽  
Vol 15 (1) ◽  
pp. 45-64 ◽  
Author(s):  
Miklos A. Vasarhelyi ◽  
Da Hsien Bao ◽  
Joel Berk
Keyword(s):  
Data Analysis ◽  
Research Method ◽  
Exploratory Data Analysis ◽  
Accounting Research ◽  
The Past ◽  
Exploratory Data ◽  
Quantitative Examination

Contemporary Accounting Research (CAR) has expanded substantially in scope over the past two decades. This paper provides an overview of these trends using both quantitative techniques from statistics and exploratory data analysis (EDA). Articles in CAR are classified into taxonomies and the literature tracked over 22 years. Analysis focuses on four taxonomies: foundation discipline, school of thought, research method and mode of reasoning. The paper first examines journals vis-a-vis article publication frequency and dominant taxonomies. Secondly, three assertions concerning the relative posture of the Journal of Accounting Research and the literature are examined. Next the context of the literature is examined through major taxonomies and a crosstabulation of research method vs school of thought. The last part of the analysis focuses on trends within the taxonomies in the 1963–1984 period.

3. The —omics revolution

2020 ◽  
pp. 27-34
Author(s):  
Eberhard O. Voit
Keyword(s):  
Data Analysis ◽  
Scientific Method ◽  
Exploratory Data Analysis ◽  
Computing Methods ◽  
Central Position ◽  
The Past ◽  
Fields Of Study ◽  
Genomics And Proteomics ◽  
Strong Hypothesis ◽  
Exploratory Data

In the past, experiments were time consuming and expensive, and data were therefore often scarce. The so-called —omics revolution has changed this situation to a point where we often have so many data that we cannot make sense of them and need to resort to sophisticated computing methods. ‘The —omics revolution’ explains how this shift has changed how we perform experiments and think about science. The —omics revolution—with fields of study such as genomics and proteomics—has not only generated huge datasets, it has turned the tried-and-true scientific method on its head. The central position of a strong hypothesis has all but vanished, and the new mindset is exploratory data analysis.

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.

scholarly journals Multivariate Statistical Approach for Nephrines in Women with Obesity

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1393
Author(s):  
Ralitsa Robeva ◽  
Miroslava Nedyalkova ◽  
Georgi Kirilov ◽  
Atanaska Elenkova ◽  
Sabina Zacharieva ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Metabolic Syndrome ◽  
Data Analysis ◽  
Stress Response ◽  
Exploratory Data Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Obese Women ◽  
Exploratory Data

Catecholamines are physiological regulators of carbohydrate and lipid metabolism during stress, but their chronic influence on metabolic changes in obese patients is still not clarified. The present study aimed to establish the associations between the catecholamine metabolites and metabolic syndrome (MS) components in obese women as well as to reveal the possible hidden subgroups of patients through hierarchical cluster analysis and principal component analysis. The 24-h urine excretion of metanephrine and normetanephrine was investigated in 150 obese women (54 non diabetic without MS, 70 non-diabetic with MS and 26 with type 2 diabetes). The interrelations between carbohydrate disturbances, metabolic syndrome components and stress response hormones were studied. Exploratory data analysis was used to determine different patterns of similarities among the patients. Normetanephrine concentrations were significantly increased in postmenopausal patients and in women with morbid obesity, type 2 diabetes, and hypertension but not with prediabetes. Both metanephrine and normetanephrine levels were positively associated with glucose concentrations one hour after glucose load irrespectively of the insulin levels. The exploratory data analysis showed different risk subgroups among the investigated obese women. The development of predictive tools that include not only traditional metabolic risk factors, but also markers of stress response systems might help for specific risk estimation in obesity patients.

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