Popular articles: digging deeper research paper

Summary of the Academic Article

The topic of the article is about adult obesity and discusses various behavioral factors that contribute to its prevalence. Obesity is considered the normal excess weight caused by excessive deposition of fat. Normal or optimal (ideal) body weight that is the weight of which, according to medical, statistically validated data, taking into account the features of the skeleton and muscles, as well as the nature of work, the most favorable for a person of a certain age and gender.
Adult’s ideal weight roughly corresponds to the number of growth of minus 100 centimeters, according to the latest ideas to take away from the result of 5-10%. This should take into account some factors, such as body composition and age. Recently, the conventional wisdom is that with 30 years of active life there is a decrease in body weight (i. e., the mass of muscles, bones and internal organs), and the weight does not correspond to the correct ratio of fat and active body weight. Thus, seventy-year-woman with 170 cm height, weighing 70 kg, certainly does not have optimal body weight, its age as muscle, bone, and other organs atrophy, their weight is reduced (Chou et. al, pp. 1-57). Therefore, the weight determines the number of excess fat, and therefore, it fat. On the other hand, an obese adult weight of the similar age will be different.
Measures and calculations of the fat content of the active mass of the body can be more accurate method than the simple weighting. Absolutely guaranteed set amount of subcutaneous fat using calipers – the device with the type grippers pull sliding key, which skin fold thickness was measured at any location. There are other methods, e. g. using ultrasound, preferably some trapping gases in grease or some minerals in the active body weight.
These conclusions were reached by researchers from Imperial College London and Harvard University in Boston, who analyzed the trends in changes in body mass index (BMI), blood cholesterol, and systolic blood pressure in the adult population (aged 20 years and older) from 199 countries and areas of the world. The analysis included data from the years 1980-2008, which involved more than 9 million people in the case of BMI, 5. 4 million people in the case of blood pressure, and 3 million in the case of cholesterol. Too high values ​​of all these factors greatly increase the risk of cardiovascular diseases and hypertension is a major risk factor for premature death.
It turned out that in 2008, more than 10 percent. World’s adult population was obese, or has a BMI over 30 A larger proportions suffering from obesity were found among women – nearly 14 percent. In total, there was over 0. 5 billion people in the world. Number of people who are overweight, or BMI above 25, reached nearly 1. 5 billion. In 1980, the percentage of obese men and women was almost twice less respectively 8 percent and 5 percent.

News Article

The news article that is being taken for this task is the one reported in Businessweek titling The Global Obesity Bomb. It was an article based on the actions taken by New York City Mayor Michael Bloomberg (Kenny). The action or proposed action was to ban soft drinks that were serving over 16 ounces. Furthermore, the article discussed the fatalities caused by obesity in adults and as per the article only in New York around 5000 people die due to obesity. The article overall reported the issue of obesity on the global scale. Kenny (2012) has pointed out to a number of reasons that cause obesity which include usage of soft drinks, poor quality meals, poverty in third world countries etc. According to the author, worldwide obesity has been doubled in the recent past due to all these reasons.

Regression Model

As far as the study conducted by Chou et. al is concerned, they have used linear probability model rather than selecting any other due to the reason that they were targeting too big of a population and had taken a vast sample size to derive the outcome. You can analyze both linear and nonlinear effects for any quantity and type of predictors with a discrete or continuous dependent variable. Plans may include the effects of many degrees of freedom for the categorical predictors, the effects of a single degree of freedom for continuous predictors, as well as any combination of effects for continuous and categorical predictors. In their precluding regression test, the researchers found that most of the applied models had non-linear effects. However further tests confirmed the results and patterns to be linear in nature.

Probable Violation of a Classical Assumption

In this, case the classical assumptions that might be violated by the researcher is the occurrence of standard errors in the independent variables as the population size taken is too big which can cause errors in the findings.

Possibility of Endogenity, Hetroscedasticity, Autocorrelation

According to the paper, endogenity may arise in the case of caloric intake, energy expenditure, and cigarette smoking. Hetroscedasticity is evident in the research as F statistics is used in Table 3 and Table 4 of the conducted research. Furthermore, there is a possibility of the existence of autocorrelation due to a large population sample.

Conclusion

The news article precisely discusses the problems that obesity is causing and the reasons due to that it is prevailed in adults. Some facts are written in the article which suggest minor figures for the death rates, but there are only a few issues identified which leads to obesity. Whereas the research article provides adequate information about various factor that contribute to obesity in adults. The news article can become more informative if it provides information about the fast food restaurants and their part in enhancing obesity in adults.

Work Cited

Kenny, C. “ The Global Obesity Bomb” Bloomberg Businessweek, 2012, retrieved from http://www. businessweek. com/articles/2012-06-04/the-global-obesity-bomb
Chou, S. Y., Grossman, M, and Saffer, H, “ An Economic Analysis of Adult Obesity: Results from the Behavioral Risk Factor Surveillance System” National Bureau of Economic Research (2002), pp. 1-57,