scholarly journals A Study of Multicollinearity Detection and Rectification under Missing Values

2021 ◽  
Vol 12 (1S) ◽  
pp. 399-418
Author(s):  
Alhassan Umar Ahmad, Et. al.
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Missing Values ◽  
Multiple Regression Model ◽  
Clear Relationship ◽  
Missing Observations ◽  
Variance Inflation Factor ◽  
Tolerance Level ◽  
Drastic Decrease ◽  
Inflation Factor

In this paper, the consequences of missing observations on data-based multicollinearity were analyzed. Different missing values has a different effect on multicollinearity in the system of multiple regression model. Therefore, to ascertain the clear relationship between both multicollinearity and skipping values on monotone and arbitrary missing values, the collinear effects were potentially studied on two types of missing values. Similarly, the comparison was done to investigate each response of multicollinearity on each pattern of the missing values with the same informatics data. It was found that tolerance and variance inflation factor fluctuates due to the missing of information from the sample analyzed at a different percentages of the missing values.It was observed that the more missing values available in the sample obtain from either population statistics or survey than multicollinearity will be found in the system of multiple regression, this is because as the number of Missingness increase it shows a drastic decrease from the tolerance level on both monotone and arbitrary types as observed from the analysis.

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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