Classifying three Communities of Assam Based on Anthropometric Characteristics using R Programming

The study of anthropometric characteristics of different communities plays an important role in design, ergonomics and architecture. As the change of life style, nutrition and ethnic composition of different communities leads to obesity epidemic etc. The authors performed two experiments. In the first experiment, the authors tried to classify three communities of Assam, India based on anthropometric characteristics using R Programming. The authors mined out the statistically significant anthropometric characteristics among the Chutia, Mising and Deori communities of Assam. In the second experiment, the authors performed the Cochran Mantel Haenszel test to find out the association between the communities and BMI based nutritional status stratified by the age of the people studied.

[1]  S. Martin,et al.  A comparison of discriminant analysis and logistic regression for the prediction of coliform mastitis in dairy cows. , 1987, Canadian journal of veterinary research = Revue canadienne de recherche veterinaire.

[2]  Daniel W. Jones,et al.  The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. , 2003, JAMA.

[3]  Stephen T. C. Wong,et al.  Cancer classification and prediction using logistic regression with Bayesian gene selection , 2004, J. Biomed. Informatics.

[4]  Y. Kamio,et al.  Association between delayed bedtime and sleep-related problems among community-dwelling 2-year-old children in Japan , 2015, Journal of Physiological Anthropology.

[5]  Robert A. Muenchen,et al.  The Popularity of Data Analysis Software , 2013 .

[6]  Maja Pohar Perme,et al.  Comparison of logistic regression and linear discriminant analysis , 2004, Advances in Methodology and Statistics.

[7]  E. Gait A history of Assam , 1927 .

[8]  S. Lobo,et al.  Cephalic index of Gurung community of Nepal--an anthropometric study. , 2005, Kathmandu University medical journal.

[9]  David Madigan,et al.  Large-Scale Bayesian Logistic Regression for Text Categorization , 2007, Technometrics.

[10]  J. G. Liao,et al.  Logistic regression for disease classification using microarray data: model selection in a large p and small n case , 2007, Bioinform..

[11]  Anthropometric predictors of gestational hypertensive disorders in a remote aboriginal community: a nested case–control study , 2014, BMC Research Notes.