Regression technique: model to predict causal relationship between variables

Authors

  • Gladius Jennifer Hirudayaraj Department of Community Medicine, Karpaga Vinayaga Institute of Medical Sciences and Research Centre, Chinna Kolambakkam, Palayanoor, Kancheepuram District, Tamilnadu, India
  • Bagavan Das School of Public Health, SRM University, Chennai, Tamilnadu, India

DOI:

https://doi.org/10.18203/2394-6040.ijcmph20162539

Keywords:

Regression, MLR, Logistic Regression, Meta regression, Spatial regression

Abstract

Medical research is aim to quantify the disease magnitude and establish association between the study variables. Regression is the technique, which will not only find the correlation but also predict how much are the strength of relationship between variables.  This article aims, to discuss various types of regression techniques such as Linear Regression, Multiple Regression, Logistic Regression, Meta regression and spatial regression and Regression Imputation with assumptions and models. This article is to sensitize doctors and post-graduate medical students about this useful analytical technique.

References

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Borens MT, Hedges LV, Higgins JPT, Rothstein HR. Introduction to Meta-Analysis. 2009 John Wiley and Sons, Ltd; ISBN:978-0-470-05724-7.

Modeling Spatial Ordinal Logistic Regression and The Principal Component to Predict Poverty Status of Districts. In: Java Island Muhammad NA, Tuti Purwaningsih SS. International Journal of Statistics and Applications. 2013;3(1):1-8

Lance AW, Carol AG. Applied Spatial Statistics for Public Health Data. John Wiley and Sons inc.

Gaetan C, Guyon X. Spatial Statistics and Modeling. Springer publication.

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Published

2017-01-05

How to Cite

Hirudayaraj, G. J., & Das, B. (2017). Regression technique: model to predict causal relationship between variables. International Journal Of Community Medicine And Public Health, 3(8), 1981–1985. https://doi.org/10.18203/2394-6040.ijcmph20162539

Issue

Section

Review Articles