Environmental Statistics Methods

2018 ◽  
pp. 267-287
Author(s):  
Ning-Wu Chang
Keyword(s):  
Environmental Statistics

Environmental Statistics, Assessment, and Forecasting.

1995 ◽  
Vol 90 (430) ◽  
pp. 810
Author(s):  
NL ◽  
C. Richard Cothern ◽  
N. Phillip Ross
Keyword(s):  
Environmental Statistics

scholarly journals Influence of environmental statistics on inhibition of saccadic return

2009 ◽  
Vol 107 (2) ◽  
pp. 929-934 ◽  
Author(s):  
S. Farrell ◽  
C. J. H. Ludwig ◽  
L. A. Ellis ◽  
I. D. Gilchrist
Keyword(s):  
Environmental Statistics

scholarly journals Relations between demographic, geographic, and environmental statistics, and the spread of novel coronavirus disease (COVID-19) in Italy

2020 ◽  
Author(s):  
Alessandro Rovetta ◽  
Lucia Castaldo
Keyword(s):  
Normal Distribution ◽  
Strong Correlation ◽  
Demographic Data ◽  
Population Number ◽  
Environmental Statistics ◽  
Particulate Pollution ◽  
Pollution Levels ◽  
Awareness Campaigns ◽  
Novel Coronavirus ◽  
Pm2.5 And Pm10

BACKGROUND: Since January 2020, the COVID-19 pandemic has raged around the world, causing nearly a million deaths and hundreds of severe economic crises. In this scenario, Italy has been one of the most affected countries. OBJECTIVE: This study investigated significant correlations between COVID-19 cases and demographic, geographical, and environmental statistics of each Italian region from February 26 to August 12, 2020. We further investigated the link between the spread of SARS-CoV-2 and particulate matter (PM) 2.5 and 10 concentrations before the lockdown in Lombardy. METHODS: All demographic data were obtained from the AdminStat Italia website, and geographic data were from the Il Meteo website. The collection frequency was one week. Data on PM2.5 and PM10 average daily concentrations were collected from previously published articles. We used Pearson's coefficients to correlate the quantities that followed a normal distribution, and Spearman's coefficient to correlate quantities that did not follow a normal distribution. RESULTS: We found significant strong correlations between COVID-19 cases and population number in 60.0% of the regions. We also found a significant strong correlation between the spread of SARS-CoV-2 in the various regions and their latitude, and with the historical averages (last 30 years) of their minimum temperatures. We identified a significant strong correlation between the number of COVID-19 cases until August 12 and the average daily concentrations of PM2.5 in Lombardy until February 29, 2020. No significant correlation with PM10 was found in the same periods. However, we found that 40 μg/m^3 for PM2.5 and 50 μg/m^3 PM10 are plausible thresholds beyond which particulate pollution clearly favors the spread of SARS-CoV-2. CONCLUSION: Since SARS-CoV-2 is correlated with historical minimum temperatures and PM10 and 2.5, health authorities are urged to monitor pollution levels and to invest in precautions for the arrival of autumn. Furthermore, we suggest creating awareness campaigns for the recirculation of air in enclosed places and to avoid exposure to the cold. KEYWORDS: COVID-19, Italy, Pandemic, Epidemiology, Coronavirus-2019

scholarly journals Environmental statistics influence integration of visual cues to depth

2010 ◽  
Vol 9 (8) ◽  
pp. 48-48
Author(s):  
A. Seydell ◽  
D. Knill ◽  
J. Trommershauser
Keyword(s):  
Visual Cues ◽  
Environmental Statistics

scholarly journals Biased evaluations emerge from inferring hidden causes

2020 ◽  
Author(s):  
Yeon Soon Shin ◽  
Yael Niv
Keyword(s):  
Decision Making ◽  
Social Groups ◽  
Positive Influence ◽  
Negativity Bias ◽  
Inference Process ◽  
Inference Model ◽  
Negative Experiences ◽  
Environmental Statistics ◽  
Model Average ◽  
Positive Experiences

How do we evaluate a group of people after having positive experiences with some members and negative experiences with others? In particular, how do rare experiences with members who stand out (e.g., negative experiences when most are positive) influence the overall impression we have of the group? Here, we show that such rare events may be overweighted due to normative inference of the hidden, or latent, causes that are believed to generate the observed events. We propose a Bayesian latent-cause inference model that learns environmental statistics by combining highly similar events together and separating rare or highly variable observations. The model predicts that group evaluations that rely on averaging inferred latent causes will overweight variable events. We empirically tested these model-derived predictions in four decision-making experiments, where subjects observed a sequence of social (Exp 1 to 3) or non-social (Exp 4) behaviors and were subsequently asked to estimate the average of observed values. As predicted by our latent-cause model, average estimation was biased toward rare and highly variable events when observing social behaviors. We then showed that tracking of a single summary value, instead of parsing events into distinct latent causes, eliminates the bias. These results suggest that biases in evaluations of social groups, such as negativity bias, may arise from the causal inference process of the group.

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