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Calculated Life Expectancy of the Average Scottish Person in Relation to Smoking Rates

Undergraduate #251
Discipline: Mathematics and Statistics
Subcategory: Mathematics and Statistics

Chelsea L. Mitchell - Virginia State University


The purpose of the study is to determine if measures taken against smoking in the Scotland region have effected long term life expectancy over a ten year period. Life expectancy is a calculated estimate of the life longevity of a person taking into account multiple factors that may affect one’s life. This study uses smoking rates and life expectancy taken from the past decade in the Scotland region as a predictor for the average life expectancy of males, females, and the generalized persons from birth for the year 2020. Before a prediction can be made a strong relation, known as a correlation, must be established between both factors for both genders. After a strong correlation was found between life expectancy and smoking rates for males and females respectively, it was established that smoking rates can be used as a valid predictor. Once the correlation was established the model could be used to predict the life expectancy of the average Scottish male, female, and general person for the year 2020. A t-test confidence interval was then used to determine margin of error of the prediction. Since the population standard deviation is unknown and the data is approximately normally distributed on the basis that life expectancy is a natural phenomenon, a t-test will be used in analyzing the data. A java based program scripted in Ecilpse Luna interface was constructed to utilize the calculations done in the study to predict life expectancy for the users inputted region and data points. In the future the prediction model can be extended to study the life expectancy in different regions of the world based on characteristics other than smoking rates and life expectancy.

Funder Acknowledgement(s): National Science Foundation (NSF), Y. Lu, E. Poarch-Wall, A. Ansari, M. Sample, and R. Jarratt

Faculty Advisor: Yungjin Lu,

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This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

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