Reply #1
Thank you for your response! Indeed, I identified several aspects that may affect the results of the linear regression study. There exist several effective control measures on these variables in a study, which, as Fox (2015) explains, entails reducing the confounding variables effect. Multiple regression is principal among how I intend to control all extraneous variables in the research. The method entails using both variables affecting the dependent variable and any potential confounding variables as an independent. Secondly, I will employ experiments to establish any significant effect possible when manipulating several independent variables. In this case, some variables should be held constant to eliminate any chance of having the measured effects influenced by external factors.
Reply #2
Thank you for your response! You are right that I intend to use existing data to determine any correlation between specific locations and one’s proneness to skin cancer. It is also true that while the location is of great importance to the study, it is also within reason to account for ethnicity. The primary cause of skin cancer is the exposure to the sun’s ultraviolet (UV) radiation, which results in severe skin cells impairment (Savoye et al., 2017). Thus, geography is a significant variable to consider, particularly one’s proximity to the equator.
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Nonetheless, research indicates that one’s ethnicity, especially the type of skin is a vital aspect insofar as the skin cancer debate goes. For instance, people residing in Africa show signs of pigmented skins which developed as a mechanism to replace body hair, as opposed to people living in countries such as Australia. Therefore, ethnicity is indeed an essential factor for which to account in the study.
References
Fox, J. (2015). Applied regression analysis and generalized linear models . Sage Publications.
Savoye, I., Olsen, C. M., Whiteman, D. C., Bijon, A., Wald, L., Dartois, L., ... & Kvaskoff, M. (2017). Patterns of ultraviolet radiation exposure and skin cancer risk: the E3N- SunExp study. Journal of epidemiology , JE20160166.
