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 Property Mass Valuation on Small Markets

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 388
Author(s):  
Sebastian Gnat
Keyword(s):  
Machine Learning ◽  
Regression Model ◽  
Multiple Regression ◽  
Nearest Neighbor ◽  
Multiple Regression Model ◽  
K Nearest Neighbor ◽  
Real Property ◽  
Explanatory Variables ◽  
Land Taxation ◽  
Land Valuation

The main bases for land taxation are its area or value. In many countries, especially in Eastern Europe, reforms of property taxation, including land taxation, are being carried out or planned, introducing property value as a tax base. Practice and research in this area indicate that such a change in the tax system leads to large changes in land use and reallocation. The taxation of land value requires construction of mass valuation system. Different methodological solutions can serve this purpose. However, mass land valuation requires a large amount of information on property transactions. Such data are not available in every case. The main objective of the paper is to evaluate the possibility of applying selected algorithms of machine learning and a multiple regression model in property mass valuation on small, underdeveloped markets, where a scarce number of transactions takes place or those transactions demonstrate little volatility in terms of real property attributes. A hypothesis is verified according to which machine learning methods result in more accurate appraisals than multiple regression models do, considering the size of training datasets. Three types of models were employed in the study: a multiple regression model, k nearest neighbor regression algorithm and XGBoost regression algorithm. Training sets were drawn from a larger dataset 1000 times in order to draw conclusions for averaged results. Thanks to the application of KNN and XGBoost algorithms, it was possible to obtain models much more resistant to a low number of observations, a substantial number of explanatory variables in relation to the number of observations, a low property attributes variability in the training datasets as well as collinearity of explanatory variables. This study showed that algorithms designed for large datasets can provide accurate results in the presence of a limited amount of data. This is a significant observation given that small or underdeveloped real estate markets are not uncommon.

scholarly journals Statistical Model For Short-Term Forecasting Sparkling Beverage Sales In Southern Thailand

2011 ◽  
Vol 8 (9) ◽  
Author(s):  
Wassana Suwanvijit ◽  
Chamnein Choonpradub ◽  
Nittaya McNeil
Keyword(s):  
Statistical Model ◽  
Regression Model ◽  
Multiple Regression ◽  
Multiple Regression Model ◽  
Short Term ◽  
Sales Revenue ◽  
Southern Thailand ◽  
Short Term Forecasting

This study developed a simple statistical model for forecasting a companys sparkling beverage sales in the 14 provinces of Southern Thailand. Data comprised sales revenue from January 2000 to December 2006 obtained from the company. We fitted an observation-driven multiple regression model to log-transformed monthly revenue containing season of year (month), location and beverage flavour as factors, as well as lagged observations for the preceding four months. The model gave a r-squared of 0.95 and was effective for forecasting revenues for up to 12 future months. Using such models for forecasting sales revenue can assist company managers with planning more effectively.

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.

scholarly journals Comparison of a Multiple Regression Model and a two-Regime Model of a Vertical Refraction/ Porovnanie Viacnásobného Regresného Modelu A Dvojrežimového Modelu Vertikálnej Refrakcie

2015 ◽  
Vol 61 (1) ◽  
pp. 32-41
Author(s):  
Štefan Sokol ◽  
Mroslav Lipták ◽  
Marek Bajtala
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Meteorological Parameters ◽  
Multiple Regression Model ◽  
Physical Principle ◽  
Threshold Function ◽  
Explanatory Variables ◽  
Different Types ◽  
The Impact

Abstract Accuracy of the trigonometric measurement of elevations is affected by the systematic influence of a vertical refraction, which is caused by changes of meteorological parameters. Submitted paper deals with a modelling of the impact of the vertical refraction using selected meteorological parameters. At first, a concise derivation of a physical principle of the vertical refraction is given. Then, a multiple regression model and its extension into a form of two-regime model are given. Division into two regimes provides a threshold function, which expresses the dependence of the original explanatory variables. Different types of the threshold function are considered and finally a comparison of the quality of the proposed models and application of a chosen model on the results of repeated trigonometric measurements is given.

scholarly journals Determinants of Differentiation of Cost of Risk (CoR) among Polish Banks during COVID-19 Pandemic

2021 ◽  
Vol 14 (3) ◽  
pp. 110
Author(s):  
Zbigniew Korzeb ◽  
Paweł Niedziółka
Keyword(s):  
Credit Risk ◽  
Regression Model ◽  
Multiple Regression ◽  
Commercial Banks ◽  
Explanatory Variable ◽  
Structural Diversity ◽  
Multiple Regression Model ◽  
Explanatory Variables ◽  
Return On Capital ◽  
The Cost

The aim of the paper is to assess the evolution of the cost of credit risk (CoR) of Polish banks as a result of the COVID-19 pandemic in the first three quarters of 2020 as well as its microeconomic determinants. We analysed the structural diversity of the sample of the 13 largest Polish commercial banks in terms of the evolution of their CoR. For this purpose, a diagraphic method of Jan Czekanowski was used. It allowed us to distinguish two groups of banks displaying features characteristic of multi-object structures and three groups consisting of individual banks characterized by atypical CoR developments, significantly different from the structures of objects classified to the first and second groups. In the second part of the research, in order to identify the determinants of the observed trends, a multiple regression model was used in which the explanatory variable was the dynamics of CoR in the first three quarters of 2020. The parameters of return on capital (ROE) at the end of 2019, Non-Performing Loans (NPLs) at the end of 2019 and the dynamics of write-offs in the period 2017–2019 proved to be important explanatory variables.

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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