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Too much of a tangled mess for me.

I like this chart much more than a race bar chat. you can see all information in one go.

After Covid most people don't want to live in CA anymore at least not in the expensive areas... trends are reversing for CA at least.

If this was a species of a animal, Scientists would be figuring out the cause. Thank online porn and the fact that 1 out of 10 young men dont go to college or work. Women also say they wont date a unemployed man.

https://youtu.be/kSQyDEZKJYs

Data Source: www.census.gov

Created with: https://plotset.com/

Numbers spike with the prevalence, prominence of smart phones and social media.

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The problem is that OP is comparing two different things.

One is the sum of all deaths year round, the other is a single event that lasts for a few hours to a few days at most.

The other problem is exposure. Most people drive every day, but most people aren't in a natural disaster every day.

It's just a bad way to compare data.

Why showing as bar chart? Should visualize another way to describe distribution

The measurements seem sporadic.

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Show the married vs. unmarried splits. 😭😭

Nobody would say they were outright sexless if they weren't though. Total number of partners, ok that could lead to some changes between reality and what you say.

What's the lower limit - 16, 18? A rate of 90% of people having sex some years seems really high to me - or is that a reflection on me?! I think surely more than 10% of people at any one time are single, recently divorced, windowed, voluntarily celibate, unhappily married, religious etc

you can get more reliable answers from a politician than asking people if they have had sex

There are a couple of exceptions: for some reason a few competitions were held much later in the year in 1990 and 2005 and I didn't want to exclude them. Plus, I liked that by keeping the other months the seasons are automatically easily visually distinguishable.

It's comparing data gathered through WHO research (traffic fatalities) with sampling obtained through Wikipedia's list articles. These include mostly incidents significant enough to have their own article.

A less manipulative comparison would be with Wikipedia's list of road accidents.

Also, flood contains one huge outlier in the 1931 China floods in which deaths from subsequent famine and epidemics are included, increasing the number of fatalities from ~150,000 to 4 million, and that is just one example.

>The lion’s share of the increase in sexlessness among unmarried Americans has been among the relatively religiously devout.

And at the same time and over the same period fewer Americans are identifying with a religion.

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If the athletic season is only from January to March, you don't need to include April to December at all. Though that would mean a different type of chart...

As I said below another post you made, don't do a regression if you don't have a model in mind. It may just be hypothetical, but you must have an explanation as to why you chose this regression in particular, other than "it fits pretty well". A 100th degree polynomial function will fit better, a Ngh degree polynomial, with N the number of points, will fit perfectly.

Also, the problem you have here is that you have "positive" outliers but you don't have negative outliers for the lowest values, because energy production can't go below 0. So you have a regression which is higher than the truth. You should find a way to identfy and eliminate these outliers.

And if you can't that's not a problem! We don't need a regression all the time. We see the relationship pretty well, the red line is not needed. It just shows a model which is obviously wrong for many reasons.