This paper analyses the effects of Gross Domestic Product growth (GDP) and Inflation rate (INF) on Unemployment rate (UMP) in Ghana’s economy using covariance matrix and multiple regression models. The two models were examined separately on the same data of three variables and the different outputs analysed to determine the effectiveness among the two models. The analyses of the outputs highlight the significance of both predictor variables on unemployment rate in Ghana. Scatterplot and normal probability distribution (pnorm) graphs were used to analyse the normality of the predictor variables. Data on inflation rate and GDP growth spanning from 1991 to 2017 was used. The data was transformed to n X m matrix form for covariance –variance matrix analysis. The rows in the n by m data matrix were the multivariate observations on n units. Multiple regression analysis was performed on the data. Both the two methods provided the long-run effects of the two predictor variables on the unemployment rate. However, while multiple regression model could quantify the effect of each predictor variable on the predicted variable, the covariance matrix model only quantifies the relation existing between predictor variables and the predicted variable.
| Published in | American Journal of Applied Mathematics (Volume 7, Issue 1) |
| DOI | 10.11648/j.ajam.20190701.12 |
| Page(s) | 5-12 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2019. Published by Science Publishing Group |
Gross Domestic Product Growth Rate, Inflation Rate, Unemployment Rate, Covariance Matrix Model, Multiple Regression Model, Scatterplot Graphs and Normal Probability Distribution (Pnorm)
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APA Style
Brew Lewis, Crankson Monica Veronica, Nyarko Francis, Ampofi Isaac. (2019). Effects of Gross Domestic Product and Inflation Rate on Unemployment Rate in Ghana: Comparative Analysis of Multiple Regression and Covariance Matrix Models. American Journal of Applied Mathematics, 7(1), 5-12. https://doi.org/10.11648/j.ajam.20190701.12
ACS Style
Brew Lewis; Crankson Monica Veronica; Nyarko Francis; Ampofi Isaac. Effects of Gross Domestic Product and Inflation Rate on Unemployment Rate in Ghana: Comparative Analysis of Multiple Regression and Covariance Matrix Models. Am. J. Appl. Math. 2019, 7(1), 5-12. doi: 10.11648/j.ajam.20190701.12
AMA Style
Brew Lewis, Crankson Monica Veronica, Nyarko Francis, Ampofi Isaac. Effects of Gross Domestic Product and Inflation Rate on Unemployment Rate in Ghana: Comparative Analysis of Multiple Regression and Covariance Matrix Models. Am J Appl Math. 2019;7(1):5-12. doi: 10.11648/j.ajam.20190701.12
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Download@article{10.11648/j.ajam.20190701.12,
author = {Brew Lewis and Crankson Monica Veronica and Nyarko Francis and Ampofi Isaac},
title = {Effects of Gross Domestic Product and Inflation Rate on Unemployment Rate in Ghana: Comparative Analysis of Multiple Regression and Covariance Matrix Models},
journal = {American Journal of Applied Mathematics},
volume = {7},
number = {1},
pages = {5-12},
doi = {10.11648/j.ajam.20190701.12},
url = {https://doi.org/10.11648/j.ajam.20190701.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajam.20190701.12},
abstract = {This paper analyses the effects of Gross Domestic Product growth (GDP) and Inflation rate (INF) on Unemployment rate (UMP) in Ghana’s economy using covariance matrix and multiple regression models. The two models were examined separately on the same data of three variables and the different outputs analysed to determine the effectiveness among the two models. The analyses of the outputs highlight the significance of both predictor variables on unemployment rate in Ghana. Scatterplot and normal probability distribution (pnorm) graphs were used to analyse the normality of the predictor variables. Data on inflation rate and GDP growth spanning from 1991 to 2017 was used. The data was transformed to n X m matrix form for covariance –variance matrix analysis. The rows in the n by m data matrix were the multivariate observations on n units. Multiple regression analysis was performed on the data. Both the two methods provided the long-run effects of the two predictor variables on the unemployment rate. However, while multiple regression model could quantify the effect of each predictor variable on the predicted variable, the covariance matrix model only quantifies the relation existing between predictor variables and the predicted variable.},
year = {2019}
}
TY - JOUR T1 - Effects of Gross Domestic Product and Inflation Rate on Unemployment Rate in Ghana: Comparative Analysis of Multiple Regression and Covariance Matrix Models AU - Brew Lewis AU - Crankson Monica Veronica AU - Nyarko Francis AU - Ampofi Isaac Y1 - 2019/04/22 PY - 2019 N1 - https://doi.org/10.11648/j.ajam.20190701.12 DO - 10.11648/j.ajam.20190701.12 T2 - American Journal of Applied Mathematics JF - American Journal of Applied Mathematics JO - American Journal of Applied Mathematics SP - 5 EP - 12 PB - Science Publishing Group SN - 2330-006X UR - https://doi.org/10.11648/j.ajam.20190701.12 AB - This paper analyses the effects of Gross Domestic Product growth (GDP) and Inflation rate (INF) on Unemployment rate (UMP) in Ghana’s economy using covariance matrix and multiple regression models. The two models were examined separately on the same data of three variables and the different outputs analysed to determine the effectiveness among the two models. The analyses of the outputs highlight the significance of both predictor variables on unemployment rate in Ghana. Scatterplot and normal probability distribution (pnorm) graphs were used to analyse the normality of the predictor variables. Data on inflation rate and GDP growth spanning from 1991 to 2017 was used. The data was transformed to n X m matrix form for covariance –variance matrix analysis. The rows in the n by m data matrix were the multivariate observations on n units. Multiple regression analysis was performed on the data. Both the two methods provided the long-run effects of the two predictor variables on the unemployment rate. However, while multiple regression model could quantify the effect of each predictor variable on the predicted variable, the covariance matrix model only quantifies the relation existing between predictor variables and the predicted variable. VL - 7 IS - 1 ER -
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