Volume 4, Issue 3, May 2015, Page: 89-98
Analysis of Tobacco Smoking Patterns in Kenya Using the Multinomial Logit Model
Samwel N. Mwenda, Department of data processing, ICT Directorate, Kenya National Bureau of Statistics, Nairobi, Kenya
Anthony Kibira Wanjoya, Department statistics and actuarial science, Jomo Kenyatta university of Agriculture and technology, Nairobi, Kenya
Anthony Gichuhi Waititu, Department statistics and actuarial science, Jomo Kenyatta university of Agriculture and technology, Nairobi, Kenya
Received: Feb. 17, 2015;       Accepted: Mar. 27, 2015;       Published: Apr. 3, 2015
DOI: 10.11648/j.ajtas.20150403.14      View  4898      Downloads  323
Objectives: The study aimed to determine the tobacco smoking patterns in Kenya. Methods: This research project used the Kenya GATS 2014 data, in which a sample of 5436 total people was interviewed. However since the research focussed on modelling tobacco smoking pattern in Kenya, data from only 4418 people was used for the analysis. Data from 1018 people in the sample was dropped because information about the individuals smoking pattern, age or work status could not be found. Data Analysis: The data was analysed using R-software version 3.0.2, and report presented in form of tables and graphs. Results: This project found out that there is likelihood of a person being a heavy smoker, light smoker or Non-smoker, if the person works in the Government and Non-government /private organization, self-employed or Unemployed. The overall effect of work status was statistically significant with a chi-square value of 129.722 (p-value<0.0001). Conclusion: The results show that a person’s working status and their age are good predictors of a specific smoking pattern. From the results we have more people smoking as they grow old.
GATS, Kenya, Tobacco Smoking
To cite this article
Samwel N. Mwenda, Anthony Kibira Wanjoya, Anthony Gichuhi Waititu, Analysis of Tobacco Smoking Patterns in Kenya Using the Multinomial Logit Model, American Journal of Theoretical and Applied Statistics. Vol. 4, No. 3, 2015, pp. 89-98. doi: 10.11648/j.ajtas.20150403.14
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