Relevant bibliographies by topics / Panel data analysis and Exploratory Data analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Panel data analysis and Exploratory Data analysis'

Author: Grafiati
Published: 6 June 2025
Last updated: 25 June 2025

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Journal articles on the topic "Panel data analysis and Exploratory Data analysis"

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Akash, M. A., and Ajay Massand Dr. "Analyzing the Impact of Covid-19 And Solvency Ratios on the Profitability of Indian Pharma Companies." Journal of Research and Review in Accounting, Business & Finance ManagementJournal of Research and Review in Accounting, Business & Finance Management 1, no. 2 (2024): 45–56. https://doi.org/10.5281/zenodo.13576618.

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<em>The present research work studies about the impact of Covid-19 and solvency ratios impact on the profitability of Indian pharma companies. This particular study has been conducted for the period of 11 years from 2013 to 2023 with the data set of 110 observations for 10 pharma companies which are registered under BSE (Bombay stock Exchange) and NSE (National Stock Exchange) of India. The four independent variables are Covid, Current ratio, Debt to equity ratio and Interest coverage ratio and the dependent variable as Return on Net worth have been tested using the Panel Data Analysis and Exploratory Data Analysis as statistical tools. The results suggested that covid doesn&rsquo;t have any impact on the profit of the selected firms where as other three independent variables have impact on profitability of Indian pharma companies. </em><em>The present research work studies about the impact of Covid-19 and solvency ratios impact on the profitability of Indian pharma companies. This particular study has been conducted for the period of 11 years from 2013 to 2023 with the data set of 110 observations for 10 pharma companies which are registered under BSE (Bombay stock Exchange) and NSE (National Stock Exchange) of India. The four independent variables are Covid, Current ratio, Debt to equity ratio and Interest coverage ratio and the dependent variable as Return on Net worth have been tested using the Panel Data Analysis and Exploratory Data Analysis as statistical tools. The results suggested that covid doesn&rsquo;t have any impact on the profit of the selected firms where as other three independent variables have impact on profitability of Indian pharma companies. </em>
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Malik, Amir Yousuf, Baljit Kaur, and Zahid Mushtaq. "Exploratory Data Analysis and Forecasting the Output Power Generated by Solar Photovoltaic." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 1061–71. http://dx.doi.org/10.22214/ijraset.2022.47537.

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Abstract: Due to solar PV panels' growing feasibility as a renewable energy source, PV panel installations have surged recently. Machine learning algorithms can now generate better predictions due to the increasing availability of data and computing capacity. For many stakeholders in the energy business, predicting solar PV energy output is crucial, therefore machine learning and time series models may be used to do this. In this study, time series models and several machine learning techniques are compared across five different sites in India. Since the energy time series are non-stationary, we find that applying time series models is a challenging process. On the other hand, putting machine learning methods into practice was simpler.
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ANDRESS, HANS-JÜRGEN. "Recurrent unemployment—The West German experience: An exploratory analysis using count data models with panel data." European Sociological Review 5, no. 3 (1989): 275–97. http://dx.doi.org/10.1093/oxfordjournals.esr.a036526.

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Khan, Muhammad Anus Hayat, and Ijaz Hussain. "Analysis of Road Traffic Accidents in the Punjab by Using Panel Count Data Models." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 3, no. 1 (2021): 1–13. http://dx.doi.org/10.52700/scir.v3i1.23.

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Each year more than three thousand people die and get serious injuries in traffic accidents. Count data model provide more precise tools for planners and decision makers to conduct proactive road safety planning.We analyzed the exploratory research of Road Traffic Accidents (RTAs) and furthermore explores the factors affecting the RTAs frequency in 36 districts of the Punjab over a time period of three years (July 1, 2013 June 30, 2016) with monthly data using panel count data models. Among the models considered, the random parameters Poisson panel count data model is found to fit the data best. The exploratory analysis shows that highly dense populated districts with large number of registered vehicles causes more accidents as compared to low density populated districts. It is found that, most of the variables used to control the variation in the frequency of RTAs counts play vital role with higher significance levels. The application of regression analysis and modeling of RTAs at district level in Punjab will help to identification of districts with high RTAs rates and this could help more efficient road safety management in the Punjab.
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Friesner, Daniel, and Robert Rosenman. "The relationship between service intensity and the quality of health care: an exploratory data analysis." Health Services Management Research 18, no. 1 (2005): 41–52. http://dx.doi.org/10.1258/0951484053051915.

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This paper provides an empirical check of some assumptions used to define the quality of care in the health services literature. Specifically, we test (i) whether service intensity is the only important determinant of a provider's quality and (ii) whether higher service intensity always causes higher quality. Using a panel of hospitals from Washington State, we find evidence that rejects both of these assumptions. As a result, further work is needed to postulate a more general definition that does not rely on these assumptions.
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Kim, Jeonghyun, Lingzi Hong, Sarah Evans, Erin Oyler‐Rice, and Irhamni Ali. "Development and Validation of a Data Literacy Assessment Scale." Proceedings of the Association for Information Science and Technology 60, no. 1 (2023): 620–24. http://dx.doi.org/10.1002/pra2.827.

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ABSTRACTThe recognition of data literacy as an important learning outcome in higher education has led to a call for assessment tools to measure students’ data literacy. Although there has been a growing interest in the conceptualization of data literacy, the literature lacks a measuring instrument to operationalize data literacy. This study developed and validated a three‐factor, 24‐item data literacy assessment tool using a sample of 573 students from four community colleges in the United States. The data literacy scale developed in this study has respectable reliability and construct validity, supported by a concept analysis of data literacy, a comparative analysis of data literacy competency frameworks, an expert panel review, exploratory factor analysis, and confirmatory factor analysis.
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Tchuta, Leonard, and Fuji Xie. "AN EXPLORATORY FACTOR ANALYSIS OF FIRMS ENDOGENOUS GROWTH MEASURES." Humanities & Social Sciences Reviews 7, no. 5 (2019): 201–8. http://dx.doi.org/10.18510/hssr.2019.7525.

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Purpose: This study attempts to standardize firm endogenous growth measures by performing exploratory factor analysis on nine firm endogenous growth measures (equity book value, equity market value, working capital, stock R&amp;D investments, stock advertisement investment, stock capital asset investment, operating expenses, sales revenue, and the number of employees).&#x0D; Methodology: Data was generated by pooling a panel dataset of 116 firms and13 years timespan data.&#x0D; Main Findings: The result of the analysis reveals three underlying firm growth factors (namely firm financials, operations, and capabilities) representing the initial nine growth measures.&#x0D; Implications/Applications: The results of this research can be used as the bases for further research in firm endogenous growth model analysis.
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Postalovskiy, A. V. "Exploratory research as a method for making a people-metric panel in the media study of the television audience." RUDN Journal of Sociology 22, no. 3 (2022): 707–19. http://dx.doi.org/10.22363/2313-2272-2022-22-3-707-719.

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The article considers methodological approaches and practical use of the exploratory research for making a people-metric panel for the measurement of television ranking indicators. The main task of the exploratory research is to design a social-demographic portrait of the audience, to assess the contribution to the television viewing of different age groups, and to identify control parameters for the selection of permanent participants in such studies. In 2020, in the Republic of Belarus, the practice for establishing a single national television and radio ranking measuring company was tested. The MediaIzmeritel company entered the media advertising market as using international standards (GGTAM) and research technologies (Kantar, UK). To make a panel of studies’ participants equipped with technical devices for the passive measurement of television ranking (people meters), an exploratory research and a subsequent multivariate analysis of the data obtained are necessary. The article describes the innovative for Belarus practice of creating a single national media measurement company under the few works on exploratory research of the television audience. The author explains the design of such an exploratory study conducted in July-December 2020 with the face-to-face interview and CATI telephone survey; methodological aspects of making a television panel with a multivariate analysis of empirical data; theoretical approaches to the interpretation of the concept ‘audience’, and the design of the panel matrix - the empirical structure of the peoplemetric panel.
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Alldredge, Jill, Vinay Kumar, Brook Sanders, and Farahnaz Rahmatpanah. "Abstract 6219: Endogenous human retroviruses in epithelial ovarian cancers: An exploratory analysis." Cancer Research 83, no. 7_Supplement (2023): 6219. http://dx.doi.org/10.1158/1538-7445.am2023-6219.

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Abstract Objectives: Endogenous human retroviruses (ERVs) are remnants of exogenous retroviruses that gained access to germline cells allowing integration into the human genome. Emerging data suggest that some ERVs may become activated allowing epigenetic alterations through DNA methylation or histone modification, which can further alter gene regulation. This may serve as a targeted therapeutic opportunity in modulating oncogenesis, through aberrant oncogene activation or tumor suppressor gene inactivation. This is an emerging area of exploration in ovarian cancer. Methods: We applied our ERV mapping tools (1) to RNA-seq data from 24 female patients with ovarian, fallopian tube, or primary peritoneal cancers to investigate expression of ~550,000 ERV elements from the Human Endogenous Retrovirus database (HERVd) (2, 3). Univariate Cox regression models were constructed with normalized ERV expression data and the most significantly differentially expressed ERVs were then filtered using a penalized Lasso-Cox proportional-hazards model. ERV expression, alongside the available clinical data (including age, histotypes, and tumor stage) were provided as inputs and linear predictors generated by the model separated samples into either high or low risk categories. We identified a panel of 15 predictive ERVs and these risk evaluations allowed for Kaplan-Meier analyses of survival by clinical parameters alone versus combined with ERV signature. We also performed a secondary analysis of platinum resistant ovarian cancers using RNA-seq data (GSE102118) to investigate the effect of demethylating agent guadecitabine on ERV expression (n=9) (4). Results: Exploratory analyses of ERVs demonstrated significantly different expression between histologies (clear cell, mucinous, endometrioid, high grade serous) and FIGO disease stage. In our prognostic model, Kaplan-Meier survival analysis using only clinical parameters resulted in a significance level of 0.0013, however the supplemented model combining the 15-ERV panel and the clinical data discriminated the two risk groups for time to recurrence at a much higher significance level of p = 7.076 × 10−8. Included in the 15 ERV panel are HERVK, HERV3, and several putative ERV promoters including LTR12, LTR7, LTR3 and LTR4 (5). In our secondary analysis of platinum-resistant patients, the ERV transcripts were compared for each patient pre- and post-guadecitabine treatment and there were several ERVs with risk-predictive values induced in patients receiving guadecitabine including LTR7, LTR12 and HERVK. Conclusions: In summary, ERV RNA expression in ovarian cancer is significantly different between histologies and disease stages. The ability to successfully classify patients as either high-risk or low-risk for disease progression is of considerable value for patient management and ERVs may improve prognostication and guide future therapeutic targeting. Citation Format: Jill Alldredge, Vinay Kumar, Brook Sanders, Farahnaz Rahmatpanah. Endogenous human retroviruses in epithelial ovarian cancers: An exploratory analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6219.
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Sagge, Jr, Roberto G. "Exploring Students’ Perceptions of Academic Integrity in the Digital Classroom through Exploratory Factor Analysis (EFA)." Indian Journal Of Science And Technology 17, no. 46 (2024): 4907–20. https://doi.org/10.17485/ijst/v17i46.2384.

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Objectives: The research aims to develop a reliable tool for thoroughly understanding the multifaceted concept of academic integrity and establish a foundational model that can serve as a cornerstone for subsequent investigations into academic integrity within digital classrooms. Methods: A cross-sectional survey method was employed, involving 196 students from the College of Education of a state university in the Visayas region of the Philippines. The participants were students across year levels from the following programs: Bachelor of Secondary Education and Bachelor of Elementary Education. The data underwent thematic analysis to identify initial factors, which were then developed into a questionnaire. The questionnaire was validated through face and content validation by a panel of experts. Data were analyzed using SPSS. Findings: The analysis revealed nine factors influencing academic integrity: learning environment, ethical concerns, cheating, academic anxiety, educational impact, mental health, external pressures, technology challenges, and motivation. These factors highlight the multifaceted nature of academic integrity in online learning. This research informs interventions and policies to promote integrity and student success, advocating for continued exploration in this evolving field. Novelty: This study used Exploratory Factor Analysis (EFA) to create a research-based framework of students’ perceptions of academic integrity in the digital classroom. By presenting a framework, future studies can build upon this model to further explore and refine our understanding of academic integrity dynamics in the digital age. Ultimately, the culmination of these efforts aims to foster a culture of honesty, trust, and intellectual integrity within the academic community. Keywords: Academic integrity, Digital classroom, Exploratory Factor Analysis (EFA), Students' perception, Cross­sectional survey
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Dissertations / Theses on the topic "Panel data analysis and Exploratory Data analysis"

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Park, Jeong Il. "Foreign direct investment and sustainable local economic development: spatial patterns of manufacturing foreign direct investment and its impacts on middle class earnings." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51851.

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Foreign Direct Investment (FDI) in the United States, which predominately occurs in the manufacturing sector, remains critically important for a strong regional and local economy, due to the resulting increase in employment, wages, and tax revenue. Traditionally, local economic development strategies have focused on attracting external manufacturing plants or facilities as the primary route to economic growth, through the expansion of the tax base and/or an increase in employment. In comparison, Sustainable Local Economic Development (SLED) emphasizes the establishment of a minimum standard of living for all and an increase in this standard over time; a reduction in the steady growth in inequality among people; a reduction in spatial inequality; and the promotion and encouragement of sustainable resource use and production (Blakely & Leigh, 2010). These essential SLED principles motivate this study, which will seek to develop a better understanding of whether and how FDI contributes to SLED in terms of its spatial patterns and its impact on middle class earnings. By selecting Georgia as a case study area, this research specifically examines whether and how the location of manufacturing FDI has reduced (or increased) spatial inequality at the intra-state and intra-metropolitan levels. It also identifies whether and how manufacturing FDI has reduced (or increased) inequality among people, focusing on its impact on middle class earnings. This study finds a strong spatial concentration of manufacturing FDI employment in metropolitan areas, particularly in a large metropolitan area, at the intra-state spatial pattern analysis. The results of panel regression analysis suggest that presence of agglomeration economies in metropolitan areas has positively influenced the location of manufacturing FDI jobs. The study also finds a suburbanization pattern of manufacturing FDI employment in the intra-metropolitan spatial pattern analysis. This intra-metropolitan suburbanization of FDI in manufacturing jobs is associated with loss of urban industrial land in the central areas within a large metropolitan area. These uneven distribution patterns of manufacturing FDI jobs indicate increased spatial inequality at both intra-state and intra-metropolitan levels, but the implications of this finding are mixed. Using individual earnings data from the American Community Survey Public Use Microdata Sample files, this study also conducts a quantile regression to estimate the earnings distribution effects that a concentration of manufacturing FDI may have on different earnings groups. The findings both from place-of-work and place-of-residence earnings analysis suggest that manufacturing FDI generally has reduced inequality among people. The concentration of manufacturing FDI in a certain area show the largest distribution effects on area workers in the lower earnings group and residents in the middle earnings group.
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Wu, Shaowen. "Nonstationary panel data analysis." Connect to resource, 1998. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1261321005.

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He, Xin. "Semiparametric analysis of panel count data." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4774.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2007.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on November 27, 2007) Vita. Includes bibliographical references.
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Romaniuk, Helena. "Analysis of product usage panel data." Thesis, University of Southampton, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326798.

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Laxminarayan, Parameshvyas. "Exploratory analysis of human sleep data." Worcester, Mass. : Worcester Polytechnic Institute, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0119104-120134/.

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Thesis (M.S.)--Worcester Polytechnic Institute.<br>Keywords: association rule mining; logistic regression; statistical significance of rules; window-based association rule mining; data mining; sleep data. Includes bibliographical references (leaves 166-167).
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Walls, L. A. "The exploratory analysis of reliability data." Thesis, Nottingham Trent University, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.377574.

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The thesis outlines the usual parametric analysis of field failure time data for repairable equipments. Due to shortcomings of this black-box approach, exploratory reliability analysis has been adopted to exploit the available data and so learn more about the physical failure process. Elements of exploratory analysis have appeared in recent statistical applications of point process, time series and multivariate methods in the area. These approaches are reviewed and investigated. Exploratory analysis of much field time between failure and limited repair time data for hardware equipments has been undertaken. Despite being from different physical mechanisms, software failure interval data has the same underlying statistical point process as such hardware data and has been similarly investigated. Simple graphs, often with simulation bounds, inference procedures for nonhomogeneous Poisson processes and Box-Jenkins analysis have been used to search for and model aspects of structure expected in reliability data. The appropriateness of the methods is discussed. As well as revealing that (constant) failure rates are often unsuitable summaries, exploratory analysis has highlighted features previously unknown or ignored. The identified time structures, data irregularities and other complexities are described. Exploratory analysis indicated potential dependent failures. A simulation-based graphical tool for highlighting these important events is described. Applications to real data have shown this is a promising approach. Principal coordinates and cluster analyses have been used to explore multivariate field data for automatic fire detection systems in an attempt to identify circumstances leading to false alarms. Data problems limited this analysis. Exploratory analysis has revealed it is common in reliability to assume a too simplistic model formulation compared with the true complex data structures. The implications of this for reliability data collection. storage and analysis are discussed. While an exploratory approach is generally successful, some specialisation of standard statistical methods for reliability is desirable.
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Mamo, Fikirte. "Economic growth and Inflation : A panel data analysis." Thesis, Södertörns högskola, Institutionen för samhällsvetenskaper, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-17463.

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One of the most important objectives for any countries is to sustain high economic growth. Even though there are main factors that affect economic growth, the concern of this paper is only about inflation. The relationship between economic growth and inflation is debatable. The first objective of this study is to investigate the relationship between inflation and economic growth. This study uses panel data which includes 13 SSA countries from 1969 to 2009. To analyze the data the model is formed by taking economic growth as dependent variable and four variables (i.e. inflation, investment, population and initial GDP) as independent variables. The result indicates that there is a negative relationship between economic growth and inflation. This study is also examined the causality relationship between economic growth and inflation by using panel Granger causality test. Panel granger causality test shows that inflation can be used in order to predict growth for all countries in the sample, while the opposite it is only true for Congo, Dep. Rep and Zimbabwe.
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Bun, Maurice Josephus Gerardus. "Accurate statistical analysis in dynamic panel data models." [Amsterdam : Amsterdam : Thela Thesis] ; Universiteit van Amsterdam [Host], 2001. http://dare.uva.nl/document/57690.

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Müller, Werner, and Michaela Nettekoven. "A Panel Data Analysis: Research & Development Spillover." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1998. http://epub.wu.ac.at/620/1/document.pdf.

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Panel data analysis has become an important tool in applied econometrics and the respective statistical techniques are well described in several recent textbooks. However, for an analyst using these methods there remains the task of choosing a reasonable model for the behavior of the panel data. Of special importance is the choice between so-called fixed and random coefficient models. This choice can have a crucial effect on the interpretation of the analyzed phenomenon, which is demonstrated by an application on research and development spillover. (author's abstract)<br>Series: Forschungsberichte / Institut für Statistik
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Karamancı, Kaan. "Exploratory data analysis for preemptive quality control." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53126.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.<br>Includes bibliographical references (p. 113).<br>In this thesis, I proposed and implemented a methodology to perform preemptive quality control on low-tech industrial processes with abundant process data. This involves a 4 stage process which includes understanding the process, interpreting and linking the available process parameter and quality control data, developing an exploratory data toolset and presenting the findings in a visual and easily implementable fashion. In particular, the exploratory data techniques used rely on visual human pattern recognition through data projection and machine learning techniques for clustering. The presentation of finding is achieved via software that visualizes high dimensional data with Chernoff faces. Performance is tested on both simulated and real industry data. The data obtained from a company was not suitable, but suggestions on how to collect suitable data was given.<br>by Kaan Karamancı.<br>M.Eng.
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Books on the topic "Panel data analysis and Exploratory Data analysis"

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Raj, Baldev, and Badi H. Baltagi, eds. Panel Data Analysis. Physica-Verlag HD, 1992. http://dx.doi.org/10.1007/978-3-642-50127-2.

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du Toit, S. H. C., A. G. W. Steyn, and R. H. Stumpf. Graphical Exploratory Data Analysis. Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4612-4950-4.

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S. H. C. Du Toit. Graphical exploratory data analysis. Springer-Verlag, 1986.

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Wu, Shaowen. Nonstationary panel data analysis. UMI, 1999.

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Jambu, M. Exploratory and multivariate data analysis. Academic Press, 1991.

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R, Martinez Angel, and Solka Jeffrey L. 1955-, eds. Exploratory data analysis with MATLAB. 2nd ed. CRC Press, 2011.

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R, Martinez Angel, ed. Exploratory data analysis with MATLAB. Chapman & Hall/CRC, 2005.

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Baltagi, Badi H. Econometric Analysis of Panel Data. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-53953-5.

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Agung, I. Gusti Ngurah, ed. Panel Data Analysis Using EViews. John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118715543.

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Finkel, Steven. Causal Analysis with Panel Data. SAGE Publications, Inc., 1995. http://dx.doi.org/10.4135/9781412983594.

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Book chapters on the topic "Panel data analysis and Exploratory Data analysis"

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Striligkas, Panagiotis. "Factors Affecting Pay TV Consumption: An Exploratory Study in Greece." In Strategic Innovative Marketing and Tourism. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51038-0_39.

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AbstractThe purpose of this study is to investigate the role of socio-demographic factors that influence the use of Pay TV usage in Greece. We try to understand the impact of the continuous innovations in the media, information, and communication technology (ICT) industries, such as Over the Top services (OTT) which have allowed consumers to access video content anytime and anywhere through various devices. The research aims to highlight the unique challenges that Greek television companies face, including socio-economic characteristics and technological factors. Through quantitative research, the influence of various socio-demographic factors on the likelihood of being an OTT TV user are examined. Panel data analysis and cluster analysis are conducted to identify which factors affect pay TV usage. Data from the Hellenic Statistical Authority analyzed for the period 2016 up to 2022, that refer to the usage of information and communication technologies by households and individuals. Results suggest that age, income, education level and urbanization degree are positive predictors of OTT pay tv consumption. New directions for television businesses’ models concerning new trends in TV consumption and the rise of broadband services are suggested.
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Monsen, Karen A. "Exploratory Data Analysis." In Intervention Effectiveness Research: Quality Improvement and Program Evaluation. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61246-1_7.

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Abzalov, Marat. "Exploratory Data Analysis." In Modern Approaches in Solid Earth Sciences. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39264-6_15.

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Vierheller, Janine. "Exploratory Data Analysis." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45006-2_9.

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Gorunescu, Florin. "Exploratory Data Analysis." In Intelligent Systems Reference Library. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19721-5_3.

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Willekens, Frans. "Exploratory Data Analysis." In Multistate Analysis of Life Histories with R. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08383-4_4.

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Hauer, Ezra. "Exploratory Data Analysis." In The Art of Regression Modeling in Road Safety. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12529-9_3.

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Hinterberger, Hans. "Exploratory Data Analysis." In Encyclopedia of Database Systems. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_1384-2.

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Mukherjee, Sudipta. "Exploratory Data Analysis." In Thinking in LINQ. Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6844-4_8.

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Cox, Victoria. "Exploratory Data Analysis." In Translating Statistics to Make Decisions. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2256-0_3.

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Conference papers on the topic "Panel data analysis and Exploratory Data analysis"

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Vongsuvat, Sakol, and Chetneti Srisaan. "Cybersecurity Threat Detection Analysis via Exploratory Data Analysis." In 2024 8th International Conference on Information Technology (InCIT). IEEE, 2024. https://doi.org/10.1109/incit63192.2024.10810601.

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Tripathi, Sudhanshu, Leena Singh, and Jaisurya Sermanraja. "Complete Data Using Exploratory Data Analysis and ML Algorithms." In 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS). IEEE, 2024. https://doi.org/10.1109/ictacs62700.2024.10840870.

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Vinora, A., S. Afrin, Lavina Roshni Manoj, and A. Alagu Gomathi. "Exploratory Data Analysis of Geolocation Data using Machine Learning." In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2025. https://doi.org/10.1109/idciot64235.2025.10915093.

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Yao, Yuheng. "Exploratory Data Analysis & Data Mining on NBA Match Prediction." In 2023 6th International Conference on Computing and Big Data (ICCBD). IEEE, 2023. http://dx.doi.org/10.1109/iccbd59843.2023.10607267.

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Ogwo-Ude, Ezinne, Raza Hasan, Shakeel Ahmad, and Salman Mahmood. "Exploratory Data Analysis and Data Visualization on Accidental Drug Related Deaths." In 2024 2nd International Conference on Computing and Data Analytics (ICCDA). IEEE, 2024. https://doi.org/10.1109/iccda64887.2024.10867318.

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Sai, C. C. Dhanush, Chaluvadi Naga Amaresh, and S. Jancy. "Online Payment Fraud Detection Using Exploratory Data Analysis." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10968090.

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Pinto, Ana Sofia, Matilde Pato, and Nuno Datia. "Enhancing Drug Reviews Insights through Exploratory Data Analysis and Sentiment Analysis." In 2024 28th International Conference Information Visualisation (IV). IEEE, 2024. http://dx.doi.org/10.1109/iv64223.2024.00042.

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Usman, Sahnius, Norulhusna Ahmad, Muhd Faiq Nurhakim Nor Iskandar, and Siti Zura A. Jalil. "Exploratory Data Analysis for Malaysian Cases of Covid-19." In 2024 5th International Conference on Smart Sensors and Application (ICSSA). IEEE, 2024. https://doi.org/10.1109/icssa62312.2024.10788551.

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Zhang, Hanbing, Yinan Jing, Wei Zhang, et al. "InsightCube: Accelerating Interactive Insight Discovery in Exploratory Data Analysis." In 2025 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2025. https://doi.org/10.1109/bigcomp64353.2025.00062.

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Jansen, F. E., and M. G. Kelkar. "Exploratory Data Analysis of Production Data." In Permian Basin Oil and Gas Recovery Conference. Society of Petroleum Engineers, 1996. http://dx.doi.org/10.2118/35184-ms.

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Reports on the topic "Panel data analysis and Exploratory Data analysis"

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Thompson, James R. Topics in Exploratory and Speculative Data Analysis. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada290505.

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Plotts, Dylan. Computational Notebooks: Designing for Exploratory Data Analysis. Iowa State University, 2020. http://dx.doi.org/10.31274/cc-20240624-409.

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Ismay, Chester. Exploratory Data Analysis in R with Tidyverse. Instats Inc., 2025. https://doi.org/10.61700/9ig8c167iq5b91556.

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Abstract:
This workshop provides a comprehensive introduction to Exploratory Data Analysis (EDA) using R and the tidyverse suite, focusing on practical data manipulation and visualization techniques essential for robust research. Participants will gain expertise in handling complex datasets and creating publication-ready visualizations, enabling them to transform raw data into actionable insights. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers ECTS Equivalent points.
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Meglen, R. R. Exploratory data analysis on data generated in the DOE subsurface microbiology program. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/5965901.

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Duncan, James P. C. Determining Patterns from Radiation Portal Monitor Data: Enabling Data Insight with Visual and Interactive Exploratory Data Analysis. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1467231.

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Lipsey, Robert, and Fredrik Sjoholm. Foreign Firms and Indonesian Manufacturing Wages: An Analysis With Panel Data. National Bureau of Economic Research, 2003. http://dx.doi.org/10.3386/w9417.

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Fitzgerald, John, Peter Gottschalk, and Robert Moffitt. An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of Income Dynamics. National Bureau of Economic Research, 1998. http://dx.doi.org/10.3386/t0220.

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Bandyopadhyay, Subhayu, Arabinda Basistha, and Jonathan Munemo. Foreign Aid and Export Performance: A Panel Data Analysis of Developing Countries. Federal Reserve Bank of St. Louis, 2007. http://dx.doi.org/10.20955/wp.2007.023.

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Adamopoulos, Tasso, Loren Brandt, Jessica Leight, and Diego Restuccia. Misallocation, Selection and Productivity: A Quantitative Analysis with Panel Data from China. National Bureau of Economic Research, 2017. http://dx.doi.org/10.3386/w23039.

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Czaplewski, Raymond L., and Mike T. Thompson. Model-based time-series analysis of FIA panel data absent re-measurements. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, 2013. http://dx.doi.org/10.2737/rmrs-rp-102.

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