Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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This work on univariate and Vector Autocorrelation (VAR) time series model for the sectors in Nigeria, aims at providing an in-depth quantitative analysis of the variables (Agriculture, Industry, Building & Construction, Wholesale & Retail trade and Services). The study made use of secondary data, of all the variables investigated in the model, collected from the National Bureau of Statistics ‘ Statistical Bulletin (2018). The sample covers quarterly data from 1981 to 2018. Univariate and Multivariate time series estimation techniques -Autoregressive Integrated Moving Average (ARIMA) and Vector Autoregressive (VAR) were employed. Plots of the five sectors indicate that they all have Quadratic trend with appreciation and depreciation. Correlation analysis of the data set show that there exists a strong relationship among each variable. Each of the economic variables ARIMA model was built using Minitab 18 statistical software.
Amadi, Godpower Dike. 2020. \u201cUnivariate and Vector Autocorrelation Time Series Models for Some Sectors in Nigeria\u201d. Global Journal of Science Frontier Research - F: Mathematics & Decision GJSFR-F Volume 20 (GJSFR Volume 20 Issue F6): .
Crossref Journal DOI 10.17406/GJSFR
Print ISSN 0975-5896
e-ISSN 2249-4626
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Total Score: 104
Country: Nigeria
Subject: Global Journal of Science Frontier Research - F: Mathematics & Decision
Authors: Amadi, Biu, Arimie, Christopher Onyema (PhD/Dr. count: 0)
View Count (all-time): 122
Total Views (Real + Logic): 2069
Total Downloads (simulated): 1108
Publish Date: 2020 09, Wed
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Neural Networks and Rules-based Systems used to Find Rational and
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This work on univariate and Vector Autocorrelation (VAR) time series model for the sectors in Nigeria, aims at providing an in-depth quantitative analysis of the variables (Agriculture, Industry, Building & Construction, Wholesale & Retail trade and Services). The study made use of secondary data, of all the variables investigated in the model, collected from the National Bureau of Statistics ‘ Statistical Bulletin (2018). The sample covers quarterly data from 1981 to 2018. Univariate and Multivariate time series estimation techniques -Autoregressive Integrated Moving Average (ARIMA) and Vector Autoregressive (VAR) were employed. Plots of the five sectors indicate that they all have Quadratic trend with appreciation and depreciation. Correlation analysis of the data set show that there exists a strong relationship among each variable. Each of the economic variables ARIMA model was built using Minitab 18 statistical software.
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