Deriving a Confidence Interval for the Ratio of Two Variances
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- Опубликовано: 1 окт 2024
- I derive the appropriate formula for a confidence interval for the ratio of two population variances (when we are sampling from normally distributed populations). I do not do any calculations or look at any examples in this video, I simply derive the appropriate confidence interval formula.
I feel like the intervalls are wrong mate,
P(F(alpha/2) < U (statistic) < F(1-alpha/2)) seems more appropriate.
In the notation I use, F_{alpha/2} represents the value from the F distribution that has an area *to the right* of alpha/2. Many sources use this notation. Others define F_{alpha/2} to be the value with an area to the *left* of alpha/2. There are merits and disadvantages to both, and there isn't a universal notation. What I state in the video is correct, given my choice of notation.
yeah I would definitely get rid of those fractions that are in his conclusion. Since F_(alpha/2) = 1/[F_(1-alpha/2) you can swap their places and make a much (IMO) cleaner expression as a result
well explained video. 😃👍👍
Professor --in calculating the p value of the F statistic, please amplify on why you take the smaller of the areas and multiply by two (2). I have an innate understanding but I would like to hear what your rationale is. Thank you.
You have just saved my life, thank you!
1-alpha/2 and alpha/2 should be switched.
No, that's just different notation than what I use. Here, F_alpha/2 is the F value with an area of alpha/2 to the right. Some define it that way, some the other way.
Tq to the core... The best class I have ever seen....
Good explanation
is there a way to do this if the two population variances aren't equal?
We don't assume anything about the values of the population variances when constructing a confidence interval for their ratio.
@@jbstatistics sorry, I meant to ask if this is possible when the two samples don't come from normal distributions?
the context of my question is that I am trying to run a test for whether one group's variance is greater than another's (2 sample test) I was trying to use Levene's test, but that looks like a test that simply concludes whether variances of the two groups are equal. I saw that the F-test has a one tail version, but that requires normality....so I'm stuck. Or better yet, can I run Levene's test and conclude that if the variances aren't equal, then the group with the larger variance has statistically greater variance than the other group? Thank you so much!
Why the confidence Interval is finite?
Please comment
it is?