American Journal of Theoretical and Applied Statistics

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Analysis of Tobacco Smoking Patterns in Kenya Using the Multinomial Logit Model

Received: 17 February 2015    Accepted: 27 March 2015    Published: 03 April 2015
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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.

DOI 10.11648/j.ajtas.20150403.14
Published in American Journal of Theoretical and Applied Statistics (Volume 4, Issue 3, May 2015)
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), 2024. Published by Science Publishing Group

Keywords

GATS, Kenya, Tobacco Smoking

References
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Author Information
  • Department of data processing, ICT Directorate, Kenya National Bureau of Statistics, Nairobi, Kenya

  • Department statistics and actuarial science, Jomo Kenyatta university of Agriculture and technology, Nairobi, Kenya

  • Department statistics and actuarial science, Jomo Kenyatta university of Agriculture and technology, Nairobi, Kenya

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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

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    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

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    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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  • @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://download.sciencepg.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}
    }
    

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  • 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
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    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
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    EP  - 98
    PB  - Science Publishing Group
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    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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