Testing Linear Regressions by StatsModel Library of Python for Oceanological Data Interpretation

Aquatic Sciences and Engineering, 34(2), 51-60, 2019. doi: 10.26650/ASE2019547010

Posted: 5 Jul 2019

Date Written: June 26, 2019

Abstract

The study area is focused on the Mariana Trench, west Pacific Ocean. The research aim is to investigate correlation between various factors, such as bathymetric depths, geomorphic shape, geographic location on four tectonic plates of the sampling points along the trench, and their influence on the geologic sediment thickness. Technically, the advantages of applying Python programming language for oceanographic data sets were tested. The methodological approaches include GIS data collecting, data analysis, statistical modelling, plotting and visualizing. Statistical methods include several algorithms that were tested: 1) weighted least square linear regression between geological variables, 2) autocorrelation; 3) design matrix, 4) ordinary least square regression, 5) quantile regression. The spatial and statistical analysis of the correlation of these factors aimed at the understanding, which geological and geodetic factors affect the distribution of the steepness and shape of the trench. Following factors were analysed: geology (sediment thickness), geographic location of the trench on four tectonics plates: Philippines, Pacific, Mariana and Caroline and bathymetry along the profiles: maximal and mean, minimal values, as well as the statistical calculations of the 1st and 3rd quantiles. The study revealed correlations between the sediment thickness and distinct variations of the trench geomorphology and sampling locations across various segments along the crescent of the trench.

Keywords: Programming language, Python, Statistical analysis, Pacific Ocean, Hadal trenches, Mariana Trench, oceanology, marine geology

JEL Classification: Q25, Q22, Q00, Q01, Q55

Suggested Citation

Lemenkova, Polina, Testing Linear Regressions by StatsModel Library of Python for Oceanological Data Interpretation (June 26, 2019). Aquatic Sciences and Engineering, 34(2), 51-60, 2019. doi: 10.26650/ASE2019547010, Available at SSRN: https://ssrn.com/abstract=3411935

Polina Lemenkova (Contact Author)

Ocean University of China ( email )

5 Yushan Road
Qingdao, 266003
China

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