Selection of a soil extraction and a multiple regression model to predict plant available manganese

1982 ◽  
Vol 13 (12) ◽  
pp. 1095-1113 ◽  
Author(s):  
S.C. Sheppard ◽  
T.E. Bates
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Multiple Regression Model ◽  
Soil Extraction ◽  
Selection Of

scholarly journals THE MULTIPLE REGRESSION MODEL WITH DICHOTOMOUS VARIABLES IN ANALYSIS OF MULTI-SECTIONAL DEMAND FOR CONNECTIVITY SERVICES – APPROACH BASED ON PER SECOND BILLING

2021 ◽  
Vol 20 (2) ◽  
pp. 47-57
Author(s):  
Paweł Kaczmarczyk
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Multiple Regression Model ◽  
Explanatory Variables ◽  
First Order ◽  
Forecasting Method ◽  
Telephone Calls ◽  
Dichotomous Variables ◽  
Short Term Forecasting ◽  
Selection Of

The article presents the results of comparative research of the effectiveness of two types of models in terms of approximation and short-term forecasting of the multi-sectional demand for connectivity services. The presented results of the analyses are related to the selection of an appropriate forecasting method as an element of the Prediction System dedicated to telecommunications operators. The first tested model was a multiple regression model with dichotomous explanatory variables. The second model was a multiple regression model with dichotomous explanatory variables and autoregression. In both models, the dependent variable was the hourly counted seconds of outgoing calls within the network of the selected operator. Telephone calls were analysed in terms of such classification factors as: type of day, category of call, group of subscribers. Taking into account all levels of classification factors of the explanatory variable, 35 dichotomous explanatory variables were specified. The defined set of dichotomous explanatory variables was used in the estimation process of both compared regression models. However, in the second model, first-order autoregression was additionally applied. The second model (multiple regression model with dichotomous explanatory variables with first-order autoregression) was found to have higher approximation and predictive capabilities than the first model (multiple regression model with dichotomous explanatory variables without autoregression).

scholarly journals Adapting Hellwig’s method for selecting concomitant variables under a certain growth curve model

2018 ◽  
Vol 55 (2) ◽  
pp. 139-146
Author(s):  
Mirosława Wesołowska-Janczarek ◽  
Monika Różańska-Boczula
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Growth Curve ◽  
Multiple Regression Model ◽  
Growth Curve Model ◽  
Concomitant Variables ◽  
Curve Model ◽  
Theoretical Considerations ◽  
Over Time ◽  
Selection Of

SummaryThis paper presents an application of Hellwig’s method for selecting concomitant variables under a growth curve model, where the values of the concomitant variables change over time and are the same for all experimental units. The authors present a simple adaptation of the growth curve model to the multiple regression model for which Hellwig’s method applies. The theoretical considerations are applied to the selection of significant concomitant variables for raspberry fruiting.

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
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