Does this scatter chart really show a cause and effect?

[OmCheeto]

This thread doesn't make much sense. Try plotting fatness vs. number of cars per person

Sense, schmense... This is fun!

Cars make you fatter than eating chickens makes you fatter!

, or just vs. GDP per capita. I bet you'll get a much better correlation.

I wish you hadn't asked that...

AFAIK chicken grow fast because they have ideal conditions. I know for a fact that temperature is regulated to $0.1^oC$ within an ideal temperature profile, probably also lighting does not resemble natural daylight.
Anyway, meat is meat, there are no homeopathic imprints or toxic substances in it.

It is probably true that developed countries eat too much of *any* meat, though. But I doubt statistics can shed some light on this, because there are too many other variables. You need a controlled experiment where the only difference is the amount of chicken eaten, over at least a decade, in a group of at least several tens of people.

Not sure where you're going to get a "control group".
But you are right, there are many variables.

For instance, working on the farm will make you skinny.

Anyone know how to add statistical data together, to get a really smooth graph?

I'm up to 15 variables now.

 

[Mark44]

Cars make you fatter than eating chickens makes you fatter!

What if you have two cars, and eat chicken in both of them?

 

[mfb]

For instance, working on the farm will make you skinny.

Or being skinny makes you sell your car and work on farms.

Your income/obesity outlier is Japan, by the way (same for the cars and agriculture), and you should consider GPD purchase-power adjusted.

 

[Mark44]

"correlation does not prove causality"

Perhaps it's just me and the world thinks differently but I have great difficulty in trying to see any positive results from that saying.

First off, that's not just a "saying." And second, yes, the world pretty much thinks differently from you about whether factors that are correlated mean that one of them causes the other. This is a result from many, many years of the disciplines of statistics and probability.

We can "agree to disagree" but you're not going to find anyone in your corner who is knowledgeable about statistics.