Results of the multiple regression model of working memory, United States

2020 ◽  
Keyword(s):  
United States ◽  
Working Memory ◽  
Regression Model ◽  
Multiple Regression ◽  
Multiple Regression Model

scholarly journals Local Outcomes of Federal and State Urban & Community Forestry Programs

2011 ◽  
Vol 37 (4) ◽  
pp. 152-159
Author(s):  
Richard Hauer ◽  
Gary Johnson ◽  
Michael Kilgore
Keyword(s):  
United States ◽  
Regression Model ◽  
Multiple Regression ◽  
Community Forestry ◽  
Urban Community ◽  
The United States ◽  
Multiple Regression Model ◽  
Cost Share ◽  
Rate Of Increase ◽  
Forestry Programs

Increasing local urban and community forestry (U&CF) programs and activities in the United States is a goal of state and federal U&CF programs. This study found local U&CF programs within the 50 United States increased in activity between 1997 and 2002 at a 2.1% annual rate of increase. Several attributes of state U&CF forestry programs from a multiple regression model and correla-tion analysis partially explain the increase in local U&CF program activity. The number of technical assists in a state were a strong pre-dictor for increased local activity. Less certainty was found with state money used to fund the state U&CF program or the use of cost-share assistance (Federal Cooperative Forestry Assistance Challenge Cost-share Grants) and this increase. Study findings provide evidence that state and federal U&CF programs within the United States are furthering the building of capacity and development of local U&CF programs.

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.

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