Estimating the Efforts of Mobile Application Development in the Planning Phase Using Nonlinear Regression Analysis

Estimating the Efforts of Mobile Application Development in the Planning Phase Using Nonlinear Regression Analysis

The authors consider the construction of a nonlinear multiple regression model, its confidence and prediction intervals to evaluate the efforts of mobile application development in the planning phase based on the multivariate normalizing transformation and outlier detection. The constructed model is...

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Main Authors: Prykhodko Sergiy, Prykhodko Natalia, Knyrik Kateryna
Format: Article
Language:English
Published: Sciendo 2020-12-01
Series:Applied Computer Systems
Subjects:
estimation
maximum likelihood estimation
mobile applications
parameter estimation
planning
regression analysis
transform
Online Access:https://doi.org/10.2478/acss-2020-0019
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spelling doaj-b2bd9c62a4164acb8356bdec7044167e2021-09-06T19:41:01ZengSciendoApplied Computer Systems2255-86912020-12-0125217217910.2478/acss-2020-0019acss-2020-0019Estimating the Efforts of Mobile Application Development in the Planning Phase Using Nonlinear Regression AnalysisPrykhodko Sergiy0Prykhodko Natalia1Knyrik Kateryna2Admiral Makarov National University of Shipbuilding, Mykolaiv, UkraineAdmiral Makarov National University of Shipbuilding, Mykolaiv, UkraineAdmiral Makarov National University of Shipbuilding, Mykolaiv, UkraineThe authors consider the construction of a nonlinear multiple regression model, its confidence and prediction intervals to evaluate the efforts of mobile application development in the planning phase based on the multivariate normalizing transformation and outlier detection. The constructed model is compared to the linear regression model and nonlinear regression models based on the univariate transformations, such as the decimal logarithm, Box–Cox, and Johnson transformation. This model, in comparison with other regression models, has better prediction accuracy.https://doi.org/10.2478/acss-2020-0019estimationmaximum likelihood estimationmobile applicationsparameter estimationplanningregression analysistransform
collection DOAJ
language English
format Article
sources DOAJ
author Prykhodko Sergiy
Prykhodko Natalia
Knyrik Kateryna
spellingShingle Prykhodko Sergiy
Prykhodko Natalia
Knyrik Kateryna
Estimating the Efforts of Mobile Application Development in the Planning Phase Using Nonlinear Regression Analysis
Applied Computer Systems
estimation
maximum likelihood estimation
mobile applications
parameter estimation
planning
regression analysis
transform
author_facet Prykhodko Sergiy
Prykhodko Natalia
Knyrik Kateryna
author_sort Prykhodko Sergiy
title Estimating the Efforts of Mobile Application Development in the Planning Phase Using Nonlinear Regression Analysis
title_short Estimating the Efforts of Mobile Application Development in the Planning Phase Using Nonlinear Regression Analysis
title_full Estimating the Efforts of Mobile Application Development in the Planning Phase Using Nonlinear Regression Analysis
title_fullStr Estimating the Efforts of Mobile Application Development in the Planning Phase Using Nonlinear Regression Analysis
title_full_unstemmed Estimating the Efforts of Mobile Application Development in the Planning Phase Using Nonlinear Regression Analysis
title_sort estimating the efforts of mobile application development in the planning phase using nonlinear regression analysis
publisher Sciendo
series Applied Computer Systems
issn 2255-8691
publishDate 2020-12-01
description The authors consider the construction of a nonlinear multiple regression model, its confidence and prediction intervals to evaluate the efforts of mobile application development in the planning phase based on the multivariate normalizing transformation and outlier detection. The constructed model is compared to the linear regression model and nonlinear regression models based on the univariate transformations, such as the decimal logarithm, Box–Cox, and Johnson transformation. This model, in comparison with other regression models, has better prediction accuracy.
topic estimation
maximum likelihood estimation
mobile applications
parameter estimation
planning
regression analysis
transform
url https://doi.org/10.2478/acss-2020-0019
work_keys_str_mv AT prykhodkosergiy estimatingtheeffortsofmobileapplicationdevelopmentintheplanningphaseusingnonlinearregressionanalysis
AT prykhodkonatalia estimatingtheeffortsofmobileapplicationdevelopmentintheplanningphaseusingnonlinearregressionanalysis
AT knyrikkateryna estimatingtheeffortsofmobileapplicationdevelopmentintheplanningphaseusingnonlinearregressionanalysis
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