How do you think a case of “this explains some of the differences in the population, but not a lot” should look?
And that looks potentially fine for an error bar. For a mean estimate, SE=SD/√N , so depending on what error band they used this looks quite plausible.
I recommend finding a different statistics teacher, preferably one who isn’t a comic and one who knows what the difference between a standard deviation, a standard error, and a 95% interval is. Those should not be too hard to find, it’s relatively basic stuff, but many people actually kinda struggle with the concepts (made harder by various factors, don’t get me started on the misuse of bar charts).
I post the picture because it gets the point across, not because that is “my teacher”. The point is that you can choose smart any random regression function and they all fit just as “good”.
How do you think a case of “this explains some of the differences in the population, but not a lot” should look?
And that looks potentially fine for an error bar. For a mean estimate, SE=SD/√N , so depending on what error band they used this looks quite plausible.
Also, the R^2 is even in the picture: .11
Take a look at these examples of regression. See how any one of the conclusions is absurd? Mind you the data in that example is far less random!
I recommend finding a different statistics teacher, preferably one who isn’t a comic and one who knows what the difference between a standard deviation, a standard error, and a 95% interval is. Those should not be too hard to find, it’s relatively basic stuff, but many people actually kinda struggle with the concepts (made harder by various factors, don’t get me started on the misuse of bar charts).
I post the picture because it gets the point across, not because that is “my teacher”. The point is that you can choose smart any random regression function and they all fit just as “good”.