Is there a causal relationship between electricity consumption and obesity, or is it just an illusory correlation❓
Is there a causal relationship between electricity consumption and obesity, or is it just an illusory correlation❓
Is there a causal relationship between electricity consumption and obesity, or is it just an illusory correlation❓
@science @dataisbeautiful @health
The plot and curve fitting made in @LabPlot, a FREE, open source Data Visualization and Analysis software. It works on #Windows, #Linux and #macOS.
➡️ https://labplot.kde.org/download
#Data compiled for 184 countries.
#FOSS #FLOSS #OpenSource #FreeSoftware #DataViz #Visualization #Obesity #Health #ClimateChange #ClimateCrisis #Climate #Food
I don't even know what the graph results are supposed to mean. The Lower consumption side is too jumpy and goes in the two extremes.
No
One can fit a curve to anything. One should not.
https://blog.revolutionanalytics.com/2017/05/the-datasaurus-dozen.html
We agree. But still, a question is just a question, and you can always refine your questions.
Matejka, J., & Fitzmaurice, G. (2017). Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing.
BTW, the Datasaurus Dozen example is already available in @LabPlot via File > Open Example.
If you are interested, please see also this thread on the importance of visualizing data (the Anscombe's quartet, Simpson's paradox are also included in @LabPlot):
https://mstdn.social/@onemoment/109692198312380103
#Anscombe #SimpsonsParadox #DatasaurusDozen #Visualization #DataViz
Statistics teaches how to measure the strength of a correlation. Also, science can help us understand cause VS correlation. You can't just do a curve fit and assume it means something.
@LabPlot @science @dataisbeautiful @health seems like a classic graphic for illustrating the wrong variables being compared. To stick a trend line in the there should result in a loss of coffee room privileges for a week.
What correlation?
The answer is... kind of, but only really at the lower end.
Countries with very low (around 0) electricity usage are going to be places where food refrigeration is hard to come by, if even possible, and so stockpiling and transporting food becomes more difficult. These places, then, have to grow or hunt their own food, and it's often just enough to get by, especially considering how much hard work goes into it.
Once electricity becomes more prevalent and food refrigeration becomes common, people tend to be a bit freer with their food consumption. This doesnt mean that they all turn into fat slobs, but it does mean that they have the the option to do so that didn't exist before.
Once you hit that threshold, you start to notice things spreading out on the chart, whereas there are basically no obese countries at 0 kWh, outside of a few outliers. I'm kind of curious about which countries are up there at 45% obesity rate and no electricity.
For example: Tonga, Samoa, Kiribati, Nauru with electricity consumption per capita (the median) 548 kWh.
Ah, that explains a lot, then.
These are all island nations in Oceania that receive large amounts of their food supply from outside the country. This offloads much of the energy cost of refrigeration onto whatever nation owns the ship. I don't know if there's a good way of figuring out how much energy is spent shipping supplies to those countries, though.
@LabPlot @science @dataisbeautiful @health I mean... A bit? But the comparison does come across as "computers make you fat and lazy" which feel a bit aimless.