The article â€œMcDonald’s sales fall with tough year aheadâ€ from U.S. News and World Report magazine deals with a drop of global sales of McDonald’s fast food restaurants chain. The author of the article, Candice Choi, reports the key sales figures of the last year and January 2013 focusing on the Middle East, Asia, Europe and U.S. marketing regions. This piece correlates the sales decrease with various economic, cultural and media events and facts. The author uses correlation to highlight the close relationship of events and as the basis to assume their probable underlying causality connection.
First of all, it states that McDonald’s sales sank in Japan correlates with the growing number of people who eat at home after 2011 earthquake and tsunami. Both statistical measurements show changes that accompany each other, that is why they can be called correlated. Correlation is a relationship between variables that may be negative (inverse), positive, or curvilinear. Here, we observe the example of negative correlation, when the increase in the second variable leads to the decrease of the first one. Accordingly, the independent variable is the number of diners eating at home while the dependent measured variable is the percent of sales fall. The latter is the dependent variable since it is the one that was studied in the article and expected to change if the independent variable is going to be altered.
The second example of correlation is the relationship between McDonald’s sales drop in China and the growing wariness among Chinese diners after TV reports about chicken producers â€œignoring regulators and giving birds unapproved levels of antibioticsâ€. In this case, McDonald’s sales drop statistics is also a dependent variable and the level of the Chinese wariness after TV reports is an independent variable.
The article does not comment on the strength of correlation, but it may be inferred as a medium strength one. The reason to consider this process of a medium level of strength is that there is no exact figure of the independent variable â€“ only an assumed correlation of two variables is being mentioned by the author. As it is assumed, it may be rejected. Though the relationship of variables is not strong, it is still significant for presenting the argument of the article.
Correlation presented in the article can be called meaningful, as the related data is quantifiable and meaningful. Although the number of Japanese diners who eat at home or the level of the Chinese wariness about chicken products can not be precisely measured, still these notions are more quantifiable than categorical. They can be roughly estimated and displayed in a form of data. Moreover, correlation is meaningful, when the two presented variables are true random variables like in this article. In addition, the author does not confuse correlation and causality or incorrectly links them â€“ she simply describes the variables of meaningful correlation giving the readers an opportunity to make conclusions about cause-and-effect relationships themselves.