Step-up multiple regression model to compute Chlorophyll a in the coastal waters off Cochin, southwest coast of India

2007 ◽  
Vol 139 (1-3) ◽  
pp. 217-226 ◽  
Author(s):  
K. K. Balachandran ◽  
K. V. Jayalakshmy ◽  
C. M. Laluraj ◽  
Maheswari Nair ◽  
Thresiamma Joseph ◽  
...  
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Chlorophyll A ◽  
Coastal Waters ◽  
Multiple Regression Model ◽  
Southwest Coast Of India ◽  
Southwest Coast

Occurrence of a multi-species diatom bloom dominated by Proboscia alata (Brightwell) Sandstorm along the southwest coast of India

2013 ◽  
Vol 42 (1) ◽  
Author(s):  
Lathika Cicily ◽  
K. Padmakumar ◽  
C. Asha Devi ◽  
V. Sanjeevan
Keyword(s):  
Cell Density ◽  
Coastal Waters ◽  
Monsoon Season ◽  
Late Summer ◽  
Nutrient Concentrations ◽  
Diatom Bloom ◽  
Southwest Coast Of India ◽  
Southwest Coast ◽  
Toxic Species ◽  
A Cell

AbstractThe present communication reports on the occurrence of a multi-species diatom bloom in the upwelled waters along the southwest coast of India. During the late summer monsoon season (September 2009) a multi-species diatom bloom with a pale green discoloration of the sea surface was observed in the coastal waters of southwest coast of India. The bloom spread over an area of approximately 15 km2 along the coastal waters off Kannur (Lat. 11°59.471 N, Long. 75°03.446 E). Total diatom cell density of the bloom area was 16 × 104 cells l−1. Proboscia (=Rhizosolenia) alata (Brightwell) Sandstrom constituted 90% of the total phytoplankton population. Other phytoplankton groups that contributed to the bloom population included Chaetoceros spp., Pseudo-nitzschia spp., Rhizosolenia spp., Coscinodiscus sp., Leptocylindrus danicus, Thalassiosira sp., and Bacteriosira sp. Among these Pseudo-nitzschia multiseries, a toxic species with the ability to produce potent neurotoxin domoic acid, was observed with a cell density of 4 × 103 cells l−1. Surface chlorophyll a concentration of the bloom region was 14.1 μg l−1. Nutrient concentrations of the bloom area were 0.01 μmol l−1 for NO2-N, 0.1 μmol l−1 for NO3-N, 0.83 μmol l−1 for PO4-P and 11.44 μmol l−1 for SiO4.

Factors Affecting NPAs in Indian Banking Sector

Paradigm ◽  
2021 ◽  
Vol 25 (2) ◽  
pp. 181-193
Author(s):  
Nitya Garg
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Banking Sector ◽  
Relative Weight ◽  
Multiple Regression Model ◽  
Factors Affecting ◽  
Weight Analysis ◽  
Specific Factors ◽  
Relative Weight Analysis ◽  
Bank Specific Factors

Banking sector is the backbone of any economy, so it is necessary to focus on its performance which is largely affected by its non-performing assets (NPAs). In the year 2018–2019, NPA of scheduled banks was Rs 355,076 Crore which is 3.7% of net advances. The purpose of this study is to identify the determinants based on analysis from previous literatures, and majorly macroeconomic and bank specific factors which are affecting NPAs using the relative weight analysis and to frame a model to predict future NPAs using multiple regression model using SPSS. The study also attempts to focus on actions and remedies that banks should make to control future NPAs. Findings of the study will act as a scaffolding for financial analysts and policymakers to prevent the conversion of its performing assets into NPAs and also help in proper management of banks and also in the recovery of economy.

scholarly journals Use of a Multiple Regression Model in the Estimation of Water Borehole Flows in the Middle of Cracked Basement in Côte d’Ivoire

2020 ◽  
Vol 12 (07) ◽  
pp. 527-544
Author(s):  
Assoué Kouakou Sylvestre Kouadio ◽  
Ouedraogo Moussa ◽  
Ismaïla Ouattara ◽  
Issiaka Savane
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Multiple Regression Model

Research on Related Problems of PM2.5 in Air

2014 ◽  
Vol 644-650 ◽  
pp. 5319-5324
Author(s):  
Tian Jiu Leng
Keyword(s):  
Regression Analysis ◽  
Boundary Conditions ◽  
Regression Model ◽  
Multiple Regression ◽  
Stepwise Regression ◽  
Multiple Regression Model ◽  
Stepwise Regression Analysis ◽  
Spatial Evolution ◽  
Analysis Method ◽  
Relevant Factors

In this paper, the relevant factors of PM2.5 and the degree of correlation between them were analyzed.The multiple regression model was established using stepwise regression analysis method and the temporal spatial evolution of PM2.5 was obtained by setting the initial and boundary conditions.

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