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Abstract
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Article Information:
Applying Multiple Linear Regression to Predict Women Representation
Najeebullah, Adnan Hussein and Bakhtiar Khan
Corresponding Author: Najeebullah Khan
Submitted: 2011 July, 17
Accepted: 2011 September, 08
Published: 2011 September, 25 |
Abstract:
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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.
Key words: Multiple linear regression analyses, Pakistan, women representation, , , ,
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Cite this Reference:
Najeebullah, Adnan Hussein and Bakhtiar Khan, . Applying Multiple Linear Regression to Predict Women Representation. Research Journal of Mathematics and Statistics, (3): 111-116.
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ISSN (Online): 2040-7505
ISSN (Print): 2042-2024 |
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