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.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 4, Issue 3) |
| DOI | 10.11648/j.ajtas.20150403.14 |
| Page(s) | 89-98 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2015. Published by Science Publishing Group |
GATS, Kenya, Tobacco Smoking
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APA Style
Samwel N. Mwenda, Anthony Kibira Wanjoya, Anthony Gichuhi Waititu. (2015). Analysis of Tobacco Smoking Patterns in Kenya Using the Multinomial Logit Model. American Journal of Theoretical and Applied Statistics, 4(3), 89-98. https://doi.org/10.11648/j.ajtas.20150403.14
ACS Style
Samwel N. Mwenda; Anthony Kibira Wanjoya; Anthony Gichuhi Waititu. Analysis of Tobacco Smoking Patterns in Kenya Using the Multinomial Logit Model. Am. J. Theor. Appl. Stat. 2015, 4(3), 89-98. doi: 10.11648/j.ajtas.20150403.14
AMA Style
Samwel N. Mwenda, Anthony Kibira Wanjoya, Anthony Gichuhi Waititu. Analysis of Tobacco Smoking Patterns in Kenya Using the Multinomial Logit Model. Am J Theor Appl Stat. 2015;4(3):89-98. doi: 10.11648/j.ajtas.20150403.14
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Download@article{10.11648/j.ajtas.20150403.14,
author = {Samwel N. Mwenda and Anthony Kibira Wanjoya and Anthony Gichuhi Waititu},
title = {Analysis of Tobacco Smoking Patterns in Kenya Using the Multinomial Logit Model},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {4},
number = {3},
pages = {89-98},
doi = {10.11648/j.ajtas.20150403.14},
url = {https://doi.org/10.11648/j.ajtas.20150403.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150403.14},
abstract = {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.},
year = {2015}
}
TY - JOUR T1 - Analysis of Tobacco Smoking Patterns in Kenya Using the Multinomial Logit Model AU - Samwel N. Mwenda AU - Anthony Kibira Wanjoya AU - Anthony Gichuhi Waititu Y1 - 2015/04/03 PY - 2015 N1 - https://doi.org/10.11648/j.ajtas.20150403.14 DO - 10.11648/j.ajtas.20150403.14 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 89 EP - 98 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20150403.14 AB - 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. VL - 4 IS - 3 ER -
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