Mapping Cigarettes Similarities using Cluster Analysis Methods
AbstractThe aim of the research was to investigate the relationship and/or occurrences in and between chemical composition information (tar, nicotine, carbon monoxide), market information (brand, manufacturer, price), and public health information (class, health warning) as well as clustering of a sample of cigarette data. A number of thirty cigarette brands have been analyzed. Six categorical (cigarette brand, manufacturer, health warnings, class) and four continuous (tar, nicotine, carbon monoxide concentrations and package price) variables were collected for investigation of chemical composition, market information and public health information. Multiple linear regression and two clusterization techniques have been applied. The study revealed interesting remarks. The carbon monoxide concentration proved to be linked with tar and nicotine concentration. The applied clusterization methods identified groups of cigarette brands that shown similar characteristics. The tar and carbon monoxide concentrations were the main criteria used in clusterization. An analysis of a largest sample could reveal more relevant and useful information regarding the similarities between cigarette brands.
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Bolboacă, S.D.; Jäntschi, L. Mapping Cigarettes Similarities using Cluster Analysis Methods. Int. J. Environ. Res. Public Health 2007, 4, 233-242.
Bolboacă SD, Jäntschi L. Mapping Cigarettes Similarities using Cluster Analysis Methods. International Journal of Environmental Research and Public Health. 2007; 4(3):233-242.Chicago/Turabian Style
Bolboacă, Sorana D.; Jäntschi, Lorentz. 2007. "Mapping Cigarettes Similarities using Cluster Analysis Methods." Int. J. Environ. Res. Public Health 4, no. 3: 233-242.