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Hi,
I have a 2 sets (f and y) of 1000 data points each. Also, each data point corresponds to one in the other set. Essentially, I wanted to compute the standard deviation between the two sets, and I did this:
## \sigma_1 = ## IMAGE 1 (check attachment)
## \sigma_2 = ## IMAGE 2 (check attachment)
##\Delta H## is simply ##f(x_i)  y(x_i)##. This gives me a new set of the differences, and ##\bar{H} ## is the average of this new set. As you can see, one computation is the standard deviation between the two sets, while the other computation is the standard deviation of the differences between the two sets.
Now, I am simply wondering if it's possible to know what the error between ## \sigma_1## and ## \sigma_2## is without having to check manually. My apologies if this sounds like an odd inquiry or a very vague request, but these two variables seem like two very different quantities, and I am just a little uncertain on how different the two really are. I've done the two computations for a set of data (with a nearly Gaussian distribution) of my own using this method and my answers are nearly identical (off by 0.003) but I'm fairly sure this is dependent on the data itself.
Any advice is welcome!
I have a 2 sets (f and y) of 1000 data points each. Also, each data point corresponds to one in the other set. Essentially, I wanted to compute the standard deviation between the two sets, and I did this:
## \sigma_1 = ## IMAGE 1 (check attachment)
## \sigma_2 = ## IMAGE 2 (check attachment)
##\Delta H## is simply ##f(x_i)  y(x_i)##. This gives me a new set of the differences, and ##\bar{H} ## is the average of this new set. As you can see, one computation is the standard deviation between the two sets, while the other computation is the standard deviation of the differences between the two sets.
Now, I am simply wondering if it's possible to know what the error between ## \sigma_1## and ## \sigma_2## is without having to check manually. My apologies if this sounds like an odd inquiry or a very vague request, but these two variables seem like two very different quantities, and I am just a little uncertain on how different the two really are. I've done the two computations for a set of data (with a nearly Gaussian distribution) of my own using this method and my answers are nearly identical (off by 0.003) but I'm fairly sure this is dependent on the data itself.
Any advice is welcome!
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