Quality Prediction of Rice Flour by Multiple Regression Model with Instrumental Texture Parameters of Single Cooked Milled Rice Grains

2005 ◽  
Vol 82 (4) ◽  
pp. 414-419 ◽  
Author(s):  
Hiroshi Okadome ◽  
Hidechika Toyoshima ◽  
Naoto Shimizu ◽  
Keitaro Suzuki ◽  
Ken'ichi Ohtsubo
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Rice Flour ◽  
Multiple Regression Model ◽  
Quality Prediction ◽  
Milled Rice ◽  
Rice Grains ◽  
Texture Parameters ◽  
Instrumental Texture

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.

ANALYSING THE SATISFACTION OF PEOPLE TO PUBLIC SERVICES PROVIDED BY BRANCH OF LAND REGISTRATION AUTHORITY IN BO TRACH DISTRICT, QUANG BINH PROVINCE

2021 ◽  
Vol 130 (3C) ◽  
Author(s):  
Nguyen Van Binh ◽  
Le Dinh Huy ◽  
Ho Nhat Linh
Keyword(s):  
Service Quality ◽  
Regression Model ◽  
Multiple Regression ◽  
Public Service ◽  
Public Services ◽  
Multiple Regression Model ◽  
Land Registration ◽  
Research Site

The research was conducted to analyse the satisfaction of people to public service provided by branch of Land registration authority in Bo Trach district, Quang Binh province through 155 questionnaires applied on people who used the these services. The multiple regression model indicated there are 5 factors that affect to the satisfaction of people in the research site. The importance of the factors are different. The result showed that the most important factor is the Facility (0.335) followed by the Service quality (0.331) and the Staff capacity (0.177), the Confidence (0.170) and the Procedural process (0.155). There is no difference among genders, ages, education and qualifications, careers and living spaces on satisfaction.

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