Recently, the issue of GMO products has been a controversial topic which is broadly discussed, especially in China.

GMOs, or “genetically modified organisms,” are plants or animals created through the gene splicing techniques of biotechnology (also called genetic engineering, or GE).

As many people know, Cui Yongyuan, a famous former host of China Central Television(CCTV), has traveled to US for the purpose of investigating the situation of GMO products. He has been visiting authority of the Genetic Engineering in the universities, former professors, doctors, and even owners of farms, citizens, trying to find out if GMO products are harmful to human-beings scientifically, and what sort of opinions the US citizens are possessing about GMO products.

In the 68 minutes video, a former navy professor, named Dr.Nancy Swanson, who took a few important chronic diseases showing either the incidence or deaths over time. Her research had given us the evidence of the relation between either glyphosate application or the amount of GE products, and the usual chronic diseases such as stroke, obesity, diabetes, etc, in US.

In Dr.Nancy Swanson’s research, she mentioned “Correlation” to describe it. I would like to talk a little bit about “Correlation”.

Generally, correlation tells us how well two sets of data are linked together. **If the change in one variable is accompanied by a change in the other, then the variables are said to be correlated**. In this article, it is not so difficult to find that the “two sets of data” would be the amount of GE products, and the incidence of diseases.

The theoretical covariance is the limiting value of the observed covariance of a frequency distribution defined as:

In here, let be the variable for the amount of GE products, and be the incidence of diseases.

But depending on the measurement unit, the numbers of the variable could be fluctuating(bigger or smaller), and that makes covariance a futility to understand the relationship between two variables. In statistic, correlation is measured by what is called coefficient of correlation, which is defined as the covariance divided by the product of the standard deviations of the two variables:

Here is just a sample from Dr.Nancy Swanson, showing annual data of “GE soy & corn crops” and “Annual Incidence of Diabetes(per 1000)” of the latest decade.

So you can calculate the value for a try and find out if it is approaching to 1.

As the number of observations increases the observed correlation coefficient, , tends towards its theoretical counterpart, , defined as:

And Dr.Nancy Swanson mentioned that, all the numbers of these correlations(the relations between the glyphosate application or the amount of GE products, and the cancer incidence, these years) are greater than 0.9. And a correlation of 1 is perfect.

Actually, correlation coefficient is bounded between -1 and 1. Here is the proof of it:

Let , ,

,

By the Cauchy–Schwarz inequality,

,

So we have

Therefore,

Closer the coefficients are to +1.0 and -1.0, greater is the strength of the relationship between the variables.

Believe it or not. According to the data, it is said that the correlations between GE products and terrible diseases are greater than 0.9, which is nearly perfect.

Do you want to buy GE foods any more?