Mathcatchup
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- Joined
- Jul 29, 2018
- Messages
- 9
I am struggling to get the intuition behind degrees of freedom. I watched several videos on youtube but non managed to get the point across.
So let's take the case of the sample variance as an example.
We must divide by n-1 instead of n as we have 1 degree of freedom less.
The videos explain it as the fact that we need the mean, and if we have the mean then we don't need the full list of n values as the last value can be inferred. But isn't that the same case for the population variance equation?
Why do we have 1 df less compared to the equation for the population variance?
Why do we need to subtract df from n to get an unbiased estimate?
So let's take the case of the sample variance as an example.
We must divide by n-1 instead of n as we have 1 degree of freedom less.
The videos explain it as the fact that we need the mean, and if we have the mean then we don't need the full list of n values as the last value can be inferred. But isn't that the same case for the population variance equation?
Why do we have 1 df less compared to the equation for the population variance?
Why do we need to subtract df from n to get an unbiased estimate?
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