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2011 (Vol. 3, Issue: 3)
Article Information:

Applying Multiple Linear Regression to Predict Women Representation

Najeebullah, Adnan Hussein and Bakhtiar Khan
Corresponding Author:  Najeebullah Khan 

Key words:  Multiple linear regression analyses, Pakistan, women representation, , , ,
Vol. 3 , (3): 111-116
Submitted Accepted Published
2011 July, 17 2011 September, 08 2011 September, 25

Many researches have been conducted in various part of the world on women representation in the national legislatures of different countries. They indicate that democracy, type of electoral system, quotas and socio-economic factors influence the systems of legislative bodies. In this study we tested the impacts of seven explanatory variables (political rights, civil liberty, election system type, quota, literacy rate, per-capita income and women participation in labor force) on women representation using multiple linear regression analyses. Data collected from the freedom house and economic surveys were divided into two sub-samples such as training and test data set to develop and test the regression models. Using SPSS version 12 we first constructed the multiple linear regression model and then tested its validity. The results confirm that the model is perfectly fit for explaining variation in women representation. Per-capita income is the only significant predictor which explains 99.5% variation in women representation.
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  Cite this Reference:
Najeebullah, Adnan Hussein and Bakhtiar Khan, 2011. Applying Multiple Linear Regression to Predict Women Representation.  Research Journal of Mathematics and Statistics, 3(3): 111-116.
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ISSN (Online):  2040-7505
ISSN (Print):   2042-2024
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