Analysis of Migration Trends in Zimbabwe Using Mann-Kendall and Sen’s Slope Tests
Romeo Mawonike,
Musara Chipumuro,
Tendai Makoni,
Talent David Murwendo
Issue:
Volume 9, Issue 6, December 2021
Pages:
133-140
Received:
20 November 2020
Accepted:
22 June 2021
Published:
2 December 2021
Abstract: The movement of people into and out of the Zimbabwean borders has become a topical issue in every sector of the country. The main devastating movement is when a lot of skilled professionals leave the country to other countries in search of greener pastures without ploughing back home and left the country of origin with few or no skilled personnel to drive the economy forward. Zimbabwe is one of the developing countries that have suffered the “brain drain” for the past decades and currently there seem no solution to stop this emigration. There is no equal balance between immigrants and emigrants. In this paper, we investigate the trend analysis of Zimbabwe migration using Mann-Kendall and Sen’s Slope test. We find Mann-Kendall Test imperative in the analysis of trends of migration since it most requires data which is not normally distributed and data which contains extreme values which are difficult to handle using parametric test. Firstly, we investigate the direction of the trend and then its magnitude in terms of slope. Results show that there is a significant increase in the number of migrants going out of Zimbabwe to other countries over past two decades. This is indicated by high positive magnitudes in trend or the Sen’s estimator. On the other hand, there is insignificant trend of inflows from other countries into Zimbabwe over a decade from February to December, except in every January.
Abstract: The movement of people into and out of the Zimbabwean borders has become a topical issue in every sector of the country. The main devastating movement is when a lot of skilled professionals leave the country to other countries in search of greener pastures without ploughing back home and left the country of origin with few or no skilled personnel t...
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Effect of Multicollinearity on Variable Selection in Multiple Regression
Harrison Oghenekevwe Etaga,
Roseline Chibotu Ndubisi,
Ngonadi Lilian Oluebube
Issue:
Volume 9, Issue 6, December 2021
Pages:
141-153
Received:
25 October 2021
Accepted:
17 November 2021
Published:
9 December 2021
Abstract: When Multicollinearity exists in a data set, the data is considered deficient. Multicollinearity is frequently encountered in observational studies. It creates difficulties when building regression models. It is a phenomenon whereby two or more explanatory variable in a multiple regression model are highly correlated. Variable selection is an important aspect of model building as such the choice of the best subset among many variables to be included in a model is the most difficult part of model building in regression analysis. Data was obtained from Nigerian Stock Exchange Fact Book, Nigerian Stock Exchange Annual Report and Account, CBN Statistical Bulletin and FOS Statistical bulletin from 1987 to 2018. Variance Inflation Factor (VIF) and correlation matrices were used to detect the presence of multicollinearity. Ridge regression and Least Square Regression were applied using R-package, Minitab and SPSS Packages. Ridge Models with constant range of 0.01 ≤ K ≤ 1.5 and Least Square Regression models were considered for each value of P = 2, 3, …,7. The optimal Ridge and Least Square model from the Ridge and Least Square Regression models were obtained by taking the average rank of the Coefficient of Determination and Mean Square Error. The result showed that the choices of variable selection were affected by the presence of multicollinearity as different variables were selected under Ridge and Least Square Regression for same level of P.
Abstract: When Multicollinearity exists in a data set, the data is considered deficient. Multicollinearity is frequently encountered in observational studies. It creates difficulties when building regression models. It is a phenomenon whereby two or more explanatory variable in a multiple regression model are highly correlated. Variable selection is an impor...
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