REGRESSION MODELS BY GRETL AND R STATISTICAL PACKAGES FOR DATA ANALYSIS IN MARINE GEOLOGY

Year 2019, Volume: 3 Issue: 1, 39 - 59, 30.06.2019

Polina Lemenkova

Abstract

Gretl
and R statistical libraries enables to perform data analysis using
various algorithms, modules and functions. In this study, the
geospatial analysis of example case study of Mariana Trench, a
deep-sea hadal trench located in west Pacific Ocean, was performed
using multi-functional combined approach of both Gretl and R
libraries. The study aim was to model and visualize trends in
variations of the trench’s properties: bathymetry (depths),
geomorphology (steepness gradient), geology, volcanism (igneous
rocks). The workflow included following statistical methods computed
and visualized in Gretl and R libraries: 1) descriptive statistics;
2) box plots, normality analysis by
quantile-quantile (QQ)
plots; 3) local weighted polynomial regression model
(loess),
4) linear regression by several methods:
weighted
least squares
(WLS) regression,
ordinary least squares
(OLS) regression,
maximal likelihood linear regression and heteroskedasticity
regression model; 5) confidence ellipses and marginal intervals for
data distribution; 6) robust estimation by Nadaraya–Watson kernel
regression fit; 7) correlation analysis and matrix. The results
include following ones. First, the geology of the trench has a
correlation with a slope angle gradient and igneous rocks (volcanism
effect). Second, the sedimentation is distributed unequally by
tectonic plates. Third, there is a correlation between the slope
gradient and aspect degree. Forth, geospatial analysis of the
bathymetry shows that the deepest part of the trench is located in
the south-west.

Keywords

Statistical Analysis, Gretl, R, Regression Model, Spatial Analysis, Geology

Supporting Institution

China Scholarship Council (CSC)

Project Number

2016SOA002

Thanks

This research was funded by the China Scholarship Council (CSC), State Oceanic Administration (SOA), Marine Scholarship of China, Grant Nr. 2016SOA002, Beijing, People’s Republic of China.

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