Binary logistic regression—Instrument for assessing museum indoor air impact on exhibits

Abstract

The paper presents a new way to assess the environmental impact on historical artifacts using binary logistic regression. The mathematical model developed on the environmental parameters analyzed by the binary logistic regression method could be useful in a decision-making process establishing the best measures for pollution reduction and preventive preservation of exhibits.

Lung Function and Quality of Life in Workers with Chemical and Dust Exposure

“…Correlation analysis of health-related quality of life scores and different lung function parameters among all cases (P and OLD) revealed negative relationships (table 3, figs. [1][2][3][4]. Correlations between VC, FEF 1 , FEF 1 /VC and MEF 50 and SGRQ total score were, also, assessed, by occupational exposure, among patients of subgroups:…”

“…Both particulate and chemical air pollution remain high topics on the public agenda [1]. Despite successful measures to reduce respiratory exposure implemented in Europe, industrial air pollution remains an important matter of concern for both environmental scientists and medical professionals.…”

Risk induced farmers’ participation in agricultural innovations: evidence from a field experiment in eastern Ethiopia

“…If used, it might produce biased and inconsistent estimates (Winship and Mare 1984). On the other hand, qualitative response models that involve two possible outcomes are binary and hence are studied using either binary logit or binary probit (Cameron and Trivedi 2005;Bucur et al 2017). Qualitative choice models are suitable when respondents can choose only an outcome from the two mutually exclusive and collectively exhaustive choices (Bucur et al 2017;Alemayehu, Beuving, and Ruben 2018).…”

Environmental Impact Assessment Regarding Indoor Air Quality Using Statistical Methods

“…We are trying to provide a possible answer to this question in this paper, analyzing how the regression can bring more information in the air quality assessment process. Statistical regression is increasingly used in recent years in various fields of activity: Morrison et al [10] established the causality relationship between the incidence of respiratory diseases and the air quality, using the correlations found between the soil metal concentration and the incidence of respiratory diseases, Danciulescu et al [11] identified the correlation between the concentration of gaseous compounds in ambient air and the noise level in urban areas, and Yoo et al [12] analyzed the influence of precipitation intensity on the quality of air at the soil level in South Korea; linear correlation and regression were used by Kliuncininkas et al [13] to identify the major sources of air pollution inside Kaunas, Lithuania with particulate matter and PAHs, while Bucur et al [14] applied the binary logistic regression to identify and hierarchize the pollutants present in a museum depending on the potential impact on the exhibits.…”