This study used regression analysis to examine the relationship between various socioeconomic factors and obesity rates in the United States. The regression results showed that lack of physical activity, increased availability of fast food restaurants, and lack of physician availability were the strongest influences on higher obesity rates. Fast food consumption had the strongest correlation to obesity based on the p-values and t-statistics. Lack of sleep, alcohol consumption, and antidepressant use did not significantly correlate to obesity trends. The final regression prediction estimated that the percentage of the American population that is overweight or obese will continue to increase by 0.3-1% per year until reaching 67.93% in 2018.

1. Janica Jain, Cindy Villamil, Sylvain Jaume, Data Science Graduate Program, Department of Computer Science, Saint Peter’s University, Jersey City, NJ
ABSTRACT
This study looks at various socio-economic factors that have been identified by other researchers as important influences on weight imbalance in the American population: physical activity rates, sugar consumption, availability of physician care,
growth of fast food restaurants, lack of sleep, prescription drugs, and alcohol consumption. Regression analysis is used to determine which among the independent variables are related to the dependent variable (overweight and obesity rates)
and to explore the forms of these relationships. Currently, the obesity epidemic is known to be one of America’s most severe and fastest growing health complications. The growing obesity rates in the country lead to numerous other significant
health problems and contribute to intensified rates of more than 50 severe diseases. These circumstances furthermore create major pressure on America’s health care system. However, it is not exactly clear as to why the prevalence of obesity
has increased so dramatically over the last 30 years.
INTRODUCTION
Obesity is defined as having a body mass index (BMI) of 30 or more, which equals weight in kilograms
divided by height in meters squared (Centers for Disease Control and Prevention, 2008). The sudden
increase in overweight individuals and the epidemic of obesity in America is currently a major public
health fear. Obesity furthermore causes abundant health problems which include but are not limited to:
diabetes, hypertension, high cholesterol, cardiovascular diseases, asthma, arthritis, and even forms of
cancer. In this study we wish to understand why obesity is higher in the United States than in any other
developed country.
METHODOLOGY
Regression analysis was used for the investigation of relationship between the variables and the dependent variable, obesity. We
sought to ascertain the casual effect of the variables upon obesity. We also assessed the “statistical significance” of 95% to the
estimated relationships, which was the degree of confidence that the true relationship is close to the estimated relationship. We might
resolve that the null hypothesis is implausible if the t-statistic associated with our regression estimate lies so far out in one tail of its t-
distribution that such a value, or one even larger in absolute value, would arise less than, say, 5 percent of the time if the null
hypothesis is correct. We will also take into account the P-value, which will tell us how confident we can be that each individual
variable has some correlation with the dependent variable.
RESULTS
CONCLUSION
The only factor for which we did not see an influence on overweight and obesity rates was the delivery of sugar for consumption in different industries in the US. However,
when focusing on the delivery of sugar for consumption through sugary beverages, we saw a strong influence. We also discovered that lack of sleep, lack of physical
activity and low physician availability to citizens were the strongest strong influences on heightened overweight and obesity rates, while also confirming that alcohol
consumption and fast food restaurant growth would be strong influences as well. The factor that seemed to play a minor role in the increase of overweight and obesity
rates was the consumption of prescribed antidepressants. However, this was compared to the population as a whole, and we think it would be interesting to see the
change in overweight and obesity rates in people who consume them only. Obesity is becoming an epidemic in the United States and any knowledge that can be gathered
to prevent it from affecting more people is beneficial.
REFERENCES
• The Organization for Economic Cooperation and Development
• Baum, C., & Yi-Chou, S. (2011). The Socio-Economic Causes of Obesity (Paper
No.17423). New York, NY: The National Bureau of Economic Research.
• Cutler, D., Glaesar, E., & Shapiro, J. (2003). Why Have Americans Become More Obese?
Journal of Economic Perspectives, LXXI(3), 93-118
• Knutson, K., & Cauter, E. (2008). Associations between Sleep Loss and Increased Risk of
Obesity and Diabetes. Chicago, Illinois, USA. University of Chicago, Department of Health
Studies.
Regression Analysis of Socio-Economic Factors Leading to Increased
Body Weight and Obesity in America
Forecasting returns the predicted value of obesity for the
specific value, x, of the independent variable (represented in the
data by factors) by using a best fit linear regression. Final
results show a continuous trend in obesity of about <0.3-1 % per
year until 2018. The final prediction for 2018 shows a
percentage of 67.93% of the American population as overweight
or obese.
Regression Statistics
Lack of
sleep
Fast Food
Restaurants
Lack of
Physical
Activity
Antidepressant
Consumption
Sugar
Availability
Sugar-
Sweetened
Beverages
Alcohol
Consumption
Physician
Availability
Multiple R 0.9414 0.97907 0.97846 0.61714 0.61714 0.89140 0.956410 0.97854
R2 0.8861 0.95858 0.95737 0.38086 0.38086 0.79460 0.91472 0.95753
Adjusted R2 0.8767 0.95540 0.95382 0.32926 0.32926 0.77880 0.90816 0.95427
Standard Error 0.8210 0.55765 0.50231 1.91440 1.91440 1.24183 0.80017 0.56466
Observations
(years) 14 14 14 14 14 14 14 14
P-Value
5.1740E
-07
2.27E-10 1.38E-09 0.018714 0.170848 8.15E-06 2.53E-08 2.67E-10
T-Statistic 9.6641 17.34555 16.41706 2.7169 1.44966 7.09161 11.8085 17.12091
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.9897196
R Square 0.9795449
Adjusted R
Square 0.9590898
Standard Error 0.5340544
Observations 15
ANOVA
df SS MS F Significance F
Regression 7 95.60750098 13.6582 47.8876 2.19E-05
Residual 7 1.996499017 0.28521
Total 14 97.604
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Obesity 33.807146 48.63021776 0.69519 0.50935 -81.185046 148.79934
Alcohol -2.649E-05 0.00012236 -0.21647 0.83479 -0.0003158 0.0002628
Sleep -0.0394214 0.277870747 -0.14187 0.89118 -0.6964813 0.6176385
Physicians 0.1301715 0.201527667 0.64592 0.53892 -0.3463658 0.6067087
Fast Food 0.3645159 0.297074354 1.22702 0.25948 -0.3379533 1.0669851
No Exercise 2.8406219 4.037426425 0.70357 0.50442 -6.7063745 12.387618
Anti-Depress -4.468E-06 4.84873E-05 -0.09216 0.92915 -0.0001191 0.0001102
Beverages 4.688E-06 8.27112E-06 0.56685 0.58852 -1.487E-05 2.425E-05
After conducting a multi-regression analysis of all the factors comparatively to
the obesity trends, the results matched extremely similarly to the individual line
regression tests. As a result of the linear regression tests, three of the factors
proved to be the strongest factors of obesity trends in the United States were:
fast food chain availability, low exercise rate, and physician availability. Also in
a similar sense once again the T-Stat and P-value for these three factors were
closest to our cut-off numbers when the multi-regression analysis was done.
Fast food consumption stood out as the strongest factor in both p and t values.
Sleep, alcohol, and the anti-depressant medication were not significant factors
in obesity trends, which is similar to our linear regression tests.