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About 10,000 papers for “Exploratory analysis”

exploratory analysis

Michael Butkiewicz A, Thomas Butkiewicz B, William Ribarsky B et al. · 2012

Cited by 0

Exploratory analysis.

Renata D. Alquezar (13194675), Fabio J. V. Costa (13194678), João Paulo Sena-Souza (6513065) et al. · Figshare · 2022

<p>R code and results for Analysis of Variance followed by a Tukey’s HSD (variable year of collection, feather type, biome, and sub-family), Linear Models (variable latitude and longitude), and Model Selection with all variables.</p> <p>(PDF)</p>

Cited by 0Open access

Exploratory analysis.

Larissa Vilany (4382092), Thiago J. R. de Rezende (4382089), Luiza G. Piovesana (4382080) et al. · Figshare · 2017

<p>Right and left hemispheres, lateral, medial, frontal, posterior, superior and inferior views. Atrophic regions are marked in blue, regions with increased cortical thickness in red, colorbar indicates p-value. In light grey shade, sulci, in dark grey shade, gyri. Significant different regions from the control group: CCD: right precuneus and lateral occipital.</p>

Cited by 0Open access

Exploratory analysis.

Per Johansson (491233), Paulina Jonéus (17290723), Sophie Langenskiöld (10304138) · Figshare · 2023

<div><p>Regulatory authorities are recognizing the need for real-world evidence (RWE) as a complement to randomized controlled trials in the approval of drugs. However, RWE needs to be fit for regulatory purposes. There is an ongoing discussion regarding whether pre-publication of a protocol on appropriate repositories, e.g. ClinicalTrials.gov, would increase the quality of RWE or not. This paper illustrates that an observational study based on a pre-published protocol can entail the same level of detail as a protocol for a randomized experiment. The strategy is exemplified by designing a comparative effectiveness evaluation of abiraterone acetate against enzalutamide in clinical practice. These two cancer drugs are prescribed to patients with advanced prostate cancer. Two complementary designs, including pre-analysis plans, were published before data on outcomes and proxy-outcomes were obtained. The underlying assumptions are assessed and both analyses show an increased mortality risk from being prescribed abiraterone acetate compared to enzalutamide.</p></div>

Cited by 0Open access

Exploratory analysis.

Olatunji Johnson (8534166), Claudio Fronterre (7385999), Peter J. Diggle (7507748) et al. · Figshare · 2021

<p>Map showing the empirical prevalence and the locations of the villages surveyed (upper panel) and the scatter plot of the relationship between the prevalence and elevation and NDVI.</p>

Cited by 0Open access

Exploratory Analysis

Richard E. Chandler, E. Marian Scott · 2011

This chapter describes some simple procedures that may be used to gain a preliminary understanding of a data set, often as a prelude to more sophisticated analyses. Specifically, techniques for visualising time series data are discussed, as well as some of the “classical” tests for trend. The key concepts of autocorrelation and smoothing are introduced-the former because of the need to account for it in subsequent analyses and the latter because, in one form or another, smoothing techniques underlic many of the other methods discussed. The chapter discusses some procedures that are commonly used to test for the existence of trends in environmental time series. Many techniques are well established in one or more areas of environmental science, and it is perhaps natural to ask why they have all been included under the heading “exploratory analysis”-surely, after dealing with visualisation, serial dependence, smoothing, trend and seasonality removal and testing. Controlled Vocabulary Terms autocorrelation function; exploratory factor analysis; smoothing

Cited by 0

Exploratory Analysis

Patricia Mumby, Esha Rani Kumar, Kristin Schneider · OSF Preprints (OSF Preprints) · 2021

Cited by 0

Exploratory Analysis

Lacoste J · 2015

New York City hopes to reduce Green House Gas emissions from buildings by 3.4 million metric tons by 2025. Since the vast majority of buildings in New York City already exist, a crucial objective is improving the energy efficiency of the existing building stock. The slow implementation of energy efficient technologies in buildings can be explained by a lack of funding. This study explores methods for targeting deeper energy efficiency retrofits, as well as financing these improvements through reforming the City’s zoning ordinance, specifically the governance of Transfer of Development Rights (TDR) transactions. Developments with transferred development rights create increased density that impose costs that need to be accounted for; with more building density there will be more energy usage at one point in space. Based on research and reasonable assumptions, this study of Manhattan alone demonstrates that placing a value capture tax on TDR transactions at a hypothetical 6 % tax rate could raise between $46 million and $72.5 million. Similarly placing a flat $18 per square foot (PSF) charge could raise from $47 million to $67 million. The revenues generated by this

Cited by 0

Exploratory Analysis

Mumby, Patricia, Kumar, Esha Rani, Schneider, Kristin · Open Science Framework · 2021

Cited by 0Open access

Exploratory Analysis

Esha Rani Kumar, Kristin Schneider, Patricia Mumby · OSF Preprints (OSF Preprints) · 2021

Cited by 0

Exploratory analysis.

Martin Kayitale Mbonye (507318), Sarah M. Burnett (507319), Aldomoro Burua (507320) et al. · Figshare · 2015

Cited by 0Open access

Exploratory Analysis

Anke Gericke, Mario Klesse, Robert Winter et al. · 2011

The increased deployment of commercial off-the-shelf (COTS) applications creates application integration challenges. However, very few articles focus on application integration success. That is why we first analyze these contributions and derive success factor candidates (SFCs). In addition, contributions dealing with problems and risks of application integration and related research fields are examined—as they allow for the derivation of further SFCs. In total twenty-six SFCs are collected. A factor analysis is conducted to examine interrelations between SFCs. It yields seven success factor groups, e.g., architecture management, IT/business alignment, or use of methods. In a second step, an exploratory analysis is conducted to examine the impact of these SFCs on various success indicators of application integration. These success indicators were defined beforehand by analyzing theories for

Cited by 0

Exploratory analysis.

Christoph Aurnhammer (11495354), Francesca Delogu (11495357), Miriam Schulz (710353) et al. · Figshare · 2021

<p>Coefficients (left; added to their intercept) and estimated ERPs (right) for exploratory LMER models fitted only on Condition A. Error bars indicate the standard error on the coefficients from the statistical model (right) and standard error computed from the per-subject per-value averages (right).</p>

Cited by 0Open access

From Exploratory Data Analysis to Exploratory Spatial Data Analysis

Patricia Abelairas-Etxebarria, Inma Astorkiza · Mathematics and Statistics · 2020

The Exploratory Data Analysis raised by Tuckey [19] has been used in multiple research in many areas but, especially, in the area of the social sciences. This technique searches behavioral patterns of the variables of the study, establishing a hypothesis with the least possible structure. However, in recent times, the inclusion of the spatial perspective in this type of analysis has been revealed as essential because, in many analyses, the observations are spatially autocorrelated and/or they present spatial heterogeneity. The presence of these spatial effects makes necessary to include spatial statistics and spatial tools in the Exploratory Data Analysis. Exploratory Spatial Data Analysis includes a set of techniques that describe and visualize those spatial effects: spatial dependence and spatial heterogeneity. It describes and visualizes spatial distributions, identifies outliers, finds distribution patterns, clusters and hot spots and suggests spatial regimes or other forms of spatial heterogeneity and, it is being increasingly used. With the objective of reviewing the last applications of this technique, this paper, firstly, shows the tools used in Exploratory Spatial Data Analysis and, secondly, reviews the latest Exploratory Spatial Data Analysis applications focused on different areas in the social sciences particularly. As conclusion, it should be noted the growing interest in the use of this spatial technique to analyze different aspects of the social sciences including the spatial dimension.

Cited by 7Open access