Timeline for Calculating Odds of Getting a Sample w/ a Specific Standard Deviation
Current License: CC BY-SA 3.0
12 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| May 28, 2017 at 18:02 | history | bounty ended | EthanT | ||
| May 25, 2017 at 22:26 | vote | accept | EthanT | ||
| May 25, 2017 at 21:56 | comment | added | soakley | I've added in some more detail to address how the beta parameter is found. | |
| May 25, 2017 at 21:56 | history | edited | soakley | CC BY-SA 3.0 |
added 416 characters in body
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| May 25, 2017 at 21:36 | comment | added | EthanT | This was all very helpful and I've almost got all the pieces filled in that were missing for me. I've replicated the above answer in MATLAB, but I'm still a bit stuck on understanding the parameters in the gamma dist. above. alpha = (15-1)/2 = 7, where the 15 is my sample size, correct? But, why the 100/7 for beta? Looks like population mean divided by alpha, but why would that be? I guess I was expecting beta = 2, based on Analysts1's comment. Also, where does my population standard deviation of 10 come in? This seems like it should be crucial to calculating the probability I'm after. | |
| May 25, 2017 at 21:20 | comment | added | StatsStudent | Got it. I was looking in the reference for the Gamma link bud didn't see it. I see now that you were addressing a different question! Thanks! | |
| May 25, 2017 at 20:37 | comment | added | Max S. | It doesn't. OP asked "Also, can you provide a reference/link where I can see the justification (or even a derivation) for the first equation?" @Analyst1 | |
| May 25, 2017 at 20:32 | comment | added | StatsStudent | Where does it help the OP understand how to get to a Gamma from a chi-squared distribution on that page, @MaxS.? | |
| May 25, 2017 at 20:17 | comment | added | Max S. | onlinecourses.science.psu.edu/stat414/node/174 has a derivation of statement 1 | |
| May 25, 2017 at 20:12 | comment | added | StatsStudent | That is a chi-squared distribution and a $\chi^2_p$ distribution is a special case of the Gamma Distribution, where $\alpha = p/2$ and $\beta=2$, using the pdf characterization described in Casella and Berger, 2nd ed., 2002. | |
| May 25, 2017 at 20:04 | comment | added | EthanT | Thanks for the reply soakley. I'm a bit confused going from eq 1 to 2. Is that a Chi^2 on the RHS of your first equation? How does that jive with a gamma distribution in the second equation. Also, can you provide a reference/link where I can see the justification (or even a derivation) for the first equation? (Or, if you could provide any details, that would be great too). | |
| May 25, 2017 at 19:53 | history | answered | soakley | CC BY-SA 3.0 |