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Thread: MATH2901 Higher Theory of Statistics

  1. #26
    Rambling Spirit
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    Re: MATH2901 Higher Theory of Statistics

    Quote Originally Posted by leehuan View Post
    Oh I double checked that. I put the n back in there, got a determinant of (2alpha beta^2)/n^2 * (a matrix with n's appearing everywhere). That's fine I reckon



    When we were taught it, we were honestly taught "educated guess". Normally I try to exploit the CLT.
    Oh yeah, I forgot to include the n in front of the matrix when getting the 1,1 entry of the inverse. It should be n in the denominator indeed (for final answer).

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    Supreme Member Flop21's Avatar
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    Re: MATH2901 Higher Theory of Statistics

    Can you guys help me with going through this question:



    So to start off, is the null and alternative hypothesis.... H0 = 95 versus H1 < 95. Or is this wrong??

    OR is it H0 < 95 versus H1 >= 95
    Last edited by Flop21; 18 Jun 2017 at 4:44 PM.
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    Re: MATH2901 Higher Theory of Statistics

    nvm
    Last edited by Flop21; 18 Jun 2017 at 5:09 PM.
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    Re: MATH2901 Higher Theory of Statistics

    Quote Originally Posted by Flop21 View Post
    Also I read that if standard deviation / sigma is know, we can use z-distribution. If not, use t-distribution. We're given the sample SD right?? Does that mean we know it or not?
    We don't know it ("it" refers to the population standard deviation (or really the standard deviation of the assumed underlying distribution)), since that's only the sample standard deviation.
    Last edited by InteGrand; 18 Jun 2017 at 4:45 PM.
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    Re: MATH2901 Higher Theory of Statistics

    Help pls someone,

    So we have to use R commands instead of distribution tables (wtf it's so much easier with the tables)?

    How would you then find the region of rejection (when doing a hypothesis test) for both z and t distributions?

    E.g. for T test, I would find the degrees of freedom (n-1), and look up n-1 on the T table with the respective value of alpha, say 0.05. How would you do this in the test????
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    Ancient Orator leehuan's Avatar
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    Re: MATH2901 Higher Theory of Statistics

    Quote Originally Posted by Flop21 View Post
    Help pls someone,

    So we have to use R commands instead of distribution tables (wtf it's so much easier with the tables)?

    How would you then find the region of rejection (when doing a hypothesis test) for both z and t distributions?

    E.g. for T test, I would find the degrees of freedom (n-1), and look up n-1 on the T table with the respective value of alpha, say 0.05. How would you do this in the test????
    You leave it there. They will supply you the R code and all you have to do is read it.

    Since you do computer science you shouldn't have any trouble understanding just one line of code tbh
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    Supreme Member Flop21's Avatar
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    Re: MATH2901 Higher Theory of Statistics

    Quote Originally Posted by leehuan View Post
    You leave it there. They will supply you the R code and all you have to do is read it.

    Since you do computer science you shouldn't have any trouble understanding just one line of code tbh
    yeah I am a mind reader that knows what vaguely named functions do


    But anyway I know it's just going to be a shit show. Hopefully not, but I am 80% sure it will be.
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    Ancient Orator leehuan's Avatar
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    Re: MATH2901 Higher Theory of Statistics

    Quote Originally Posted by Flop21 View Post
    yeah I am a mind reader that knows what vaguely named functions do


    But anyway I know it's just going to be a shit show. Hopefully not, but I am 80% sure it will be.
    R's free to download. You can always just investigate briefly :P

    Here's an example of something that might appear

    Tbh all I've seen is p___, q___, r___ where ___ is a distribution.
    p - probability
    q - quantile
    r - just a bunch of random simulations

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    Supreme Member Flop21's Avatar
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    Re: MATH2901 Higher Theory of Statistics

    Quote Originally Posted by leehuan View Post
    R's free to download. You can always just investigate briefly :P

    Here's an example of something that might appear

    Tbh all I've seen is p___, q___, r___ where ___ is a distribution.
    p - probability
    q - quantile
    r - just a bunch of random simulations
    Thanks

    tbh I think I will fail this course unfortunately
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    Re: MATH2901 Higher Theory of Statistics

    Quote Originally Posted by Flop21 View Post
    Thanks

    tbh I think I will fail this course unfortunately
    You could try the 2901 exam past papers just in case, as they seem to be similar difficulties (I think some questions overlap? or the old ones used to anyway.)
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    Re: MATH2901 Higher Theory of Statistics

    All this stat stuff looks so much more complicated than what I've done. Only done stat171 where had z, various t tests, and chi squared test for independence and GOF test... rip
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    Re: MATH2901 Higher Theory of Statistics

    Quote Originally Posted by BenHowe View Post
    All this stat stuff looks so much more complicated than what I've done. Only done stat171 where had z, various t tests, and chi squared test for independence and GOF test... rip
    We learn this in second year at UNSW, so it seems a bit later than at Macquarie.
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    Re: MATH2901 Higher Theory of Statistics

    Quote Originally Posted by Flop21 View Post
    Can you guys help me with going through this question:



    So to start off, is the null and alternative hypothesis.... H0 = 95 versus H1 < 95. Or is this wrong??

    OR is it H0 < 95 versus H1 >= 95
    I think youre supposed to make the null hypothesis equal to something but it doestn really matter so I guess

    This is how I would do it
    Last edited by BenHowe; 29 Jun 2017 at 7:07 PM.
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    Re: MATH2901 Higher Theory of Statistics

    Quote Originally Posted by BenHowe View Post
    I think youre supposed to make the null hypothesis equal to something but it doestn really matter so I guess

    This is how I would do it
    I think it should be Ho: U = 95, Ha: U < 95

    R output is mainly qnorm / pnorm / qt IIRC. Qnorm is % shaded eg qnorm(0.5) = 0 i.e. 50% shaded is at 0 on the normal distribution, etc. Pnorm is opposite so Pnorm(0.5) = the % shaded at z = 0.5 (or the pnorm/qnorm may have been other way around)

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