Aquatic Sciences and Engineering

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DOI :10.26650/ASE2019547010   IUP :10.26650/ASE2019547010    Full Text (PDF)

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

Polina Lemenkova

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

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APA

Lemenkova, P. (2019). Testing Linear Regressions by StatsModel Library of Python for Oceanological Data Interpretation. Aquatic Sciences and Engineering, 34(2), 51-60. https://doi.org/10.26650/ASE2019547010


AMA

Lemenkova P. Testing Linear Regressions by StatsModel Library of Python for Oceanological Data Interpretation. Aquatic Sciences and Engineering. 2019;34(2):51-60. https://doi.org/10.26650/ASE2019547010


ABNT

Lemenkova, P. Testing Linear Regressions by StatsModel Library of Python for Oceanological Data Interpretation. Aquatic Sciences and Engineering, [Publisher Location], v. 34, n. 2, p. 51-60, 2019.


Chicago: Author-Date Style

Lemenkova, Polina,. 2019. “Testing Linear Regressions by StatsModel Library of Python for Oceanological Data Interpretation.” Aquatic Sciences and Engineering 34, no. 2: 51-60. https://doi.org/10.26650/ASE2019547010


Chicago: Humanities Style

Lemenkova, Polina,. “Testing Linear Regressions by StatsModel Library of Python for Oceanological Data Interpretation.” Aquatic Sciences and Engineering 34, no. 2 (May. 2024): 51-60. https://doi.org/10.26650/ASE2019547010


Harvard: Australian Style

Lemenkova, P 2019, 'Testing Linear Regressions by StatsModel Library of Python for Oceanological Data Interpretation', Aquatic Sciences and Engineering, vol. 34, no. 2, pp. 51-60, viewed 10 May. 2024, https://doi.org/10.26650/ASE2019547010


Harvard: Author-Date Style

Lemenkova, P. (2019) ‘Testing Linear Regressions by StatsModel Library of Python for Oceanological Data Interpretation’, Aquatic Sciences and Engineering, 34(2), pp. 51-60. https://doi.org/10.26650/ASE2019547010 (10 May. 2024).


MLA

Lemenkova, Polina,. “Testing Linear Regressions by StatsModel Library of Python for Oceanological Data Interpretation.” Aquatic Sciences and Engineering, vol. 34, no. 2, 2019, pp. 51-60. [Database Container], https://doi.org/10.26650/ASE2019547010


Vancouver

Lemenkova P. Testing Linear Regressions by StatsModel Library of Python for Oceanological Data Interpretation. Aquatic Sciences and Engineering [Internet]. 10 May. 2024 [cited 10 May. 2024];34(2):51-60. Available from: https://doi.org/10.26650/ASE2019547010 doi: 10.26650/ASE2019547010


ISNAD

Lemenkova, Polina. “Testing Linear Regressions by StatsModel Library of Python for Oceanological Data Interpretation”. Aquatic Sciences and Engineering 34/2 (May. 2024): 51-60. https://doi.org/10.26650/ASE2019547010



TIMELINE


Submitted30.03.2019
First Revision22.05.2019
Last Revision13.06.2019
Accepted13.06.2019
Published Online26.06.2019

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