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    (Original post by ComputerMaths97)
    If I'm honest I can't remember how much I used, but as long as you picked the biggest possible scale that you saw as sensible you'll be fine.

    Worst case scenario (I think, please remember I'm not an examiner xD) is you'd lose 1 for lack of specificity if your graph is too small.
    Ah okay, thanks for replying. Hopefully we've both done well good luck for the rest!
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    (Original post by ComputerMaths97)
    You can't of expected to get all the marks mate head up

    Conclude is a very assertive word, can you remember the whole sentence? If you can I can give you a better opinion there.
    I know ahaha, but I really need an A for University, especially how my Chem 4 went...and now I am finding out about these little mistakes that might put me out of an A.

    I literally used suggest and conclude together lol so "At the 5% significance level there is sufficient evidence to suggest and conclude that there is an increase in the mean radioactivity of limpets after the power incident."

    I used more or less that sentence for all 3 hypothesis tests, so I might lose 3 more marks
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    (Original post by Crozzer24)
    Got pretty much all the same but got 6513 for sigma, 58350 for mean and 40094 for h
    i got exactly same as you
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    Will i get penalized for using jumps of 15 and 30... if all my points are plotted on the right place? I really hate awkward scales!
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    I got a value of 0.555 for my spearmans rank but my method was correct and my method for hypothesis test was correct. how many marks would i loose between them both?
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    Anyone? Would I lose any marks because of ignore subsequent working and the question wasn't asking which distribution to approx with.. Just the conditions as to why Poisson can be used which is n is large p is small (2)

    (Original post by Lollolololololol)
    For why is Poisson a good approximation distribution? I put:

    number of possible genes mutating is large and probability of a gene mutating is small.
    Hence normal is good approximation to binomial (I meant to say Poisson will I lose both marks)?
    (Original post by Lollolololololol)
    For why is Poisson a good approximation distribution? I put:

    number of possible genes mutating is large and probability of a gene mutating is small.
    Hence normal is good approximation to binomial (I meant to say Poisson will I lose both marks)?
    (Original post by Lollolololololol)
    For why is Poisson a good approximation distribution? I put:

    number of possible genes mutating is large and probability of a gene mutating is small.
    Hence normal is good approximation to binomial (I meant to say Poisson will I lose both marks)?
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    Phew, all this talk of S.D. = 1.74 had me worried until I just remembered using sqrt(3.03/60) as the standard error (the denominator of the Z equation), and sqrt(3.03) is 1.74. Noice.
    Damn variance trying to rustle my jimmies.
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    (Original post by WhiteBison)
    Phew, all this talk of S.D. = 1.74 had me worried until I just remembered using sqrt(3.03/60) as the standard error (the denominator of the Z equation), and sqrt(3.03) is 1.74. Noice.
    Damn variance trying to rustle my jimmies.
    same, there wasnt any reason to work out the SD was there?

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    (Original post by HFancy1997)
    same, there wasnt any reason to work out the SD was there?

    Posted from TSR Mobile
    Nope, but it's an equally valid line of working - just a little more involved. The only difference is that you'd end up with a standard error of 1.74/sqrt(60) as opposed to our sqrt(3.03/60). They're equivalent expressions, so s'all good.
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    I'm still unaware as to any marks I've lost?! Let's hope it stays this way.
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    Btw if anyone can get me the paper I'll mock up solutions I just cannot remember the questions
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    for all the hypothesis throughout the paper i got reject H1. for the very last question i got 0.3692<1.645

    for 4(ii) i got 5.324<5.991
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    Did anyone elese put the relationship must be monotonic and use a scale of 4 and then 2 for the scatter graph
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    Was any taught the modelling assumptions for spearmans rank???? I was never told it had to be monotonic
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    (Original post by gonecrazy)
    for all the hypothesis throughout the paper i got reject H1. for the very last question i got 0.3692<1.645

    for 4(ii) i got 5.324<5.991
    The last one you forgot to divide the standard deviation by root(60). A very common mistake

    4(ii) seems good from my memory but I can't comfirm anything. They were all accept H0 other than the last one from what i can remember. Look at the June 2015 paper Q4 it will remind you of the question today, pretty much the exact same question (the second part)
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    (Original post by ♥Samantha♥)
    Was any taught the modelling assumptions for spearmans rank???? I was never told it had to be monotonic
    All I can see is that a "monotonic" set of data is any data with a correlation.

    And assuming a correlation so that you can test for a correlation seems a bit weird to me. Plus on page 5 of this I saw there being no assumptions:
    http://www.mei.org.uk/files/pdf/spearmanrcc.pdf
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    Nope we lost. It's monotonic.

    Yeah I do not remember learning that lol
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    (Original post by ComputerMaths97)
    All I can see is that a "monotonic" set of data is any data with a correlation.

    And assuming a correlation so that you can test for a correlation seems a bit weird to me. Plus on page 5 of this I saw there being no assumptions:
    http://www.mei.org.uk/files/pdf/spearmanrcc.pdf
    Monotonic does not mean correlated. It means that the function is either always increasing or always decreasing. So an exponential curve for example is monotonic, but a quadratic curve is not. So if the plotted points exactly fell perfectly on a quadratic curve (I.e a perfect association) SR would give an association of or near zero.
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    On page 5 in the section 'Codnitions underlying the Spearman's test' it says "For the Spearman test to work, the underlying relationship must be monotonic"
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    (Original post by ♥Samantha♥)
    Monotonic does not mean correlated. It means that the function is either always increasing or always decreasing. So an exponential curve for example is monotonic, but a quadratic curve is not. So if the plotted points exactly fell perfectly on a quadratic curve (I.e a perfect association) SR would give an association of or near zero.
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    On page 5 in the section 'Codnitions underlying the Spearman's test' it says "For the Spearman test to work, the underlying relationship must be monotonic"
    That's exactly what correlated means lol. If they're correlated, one increasing means the other increasing, or visa versa.

    And exactly. It says both.
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    (Original post by ComputerMaths97)
    That's exactly what correlated means lol. If they're correlated, one increasing means the other increasing, or visa versa.

    And exactly. It says both.
    Well correlation is monotonic, but monotonic does not mean correlated. Correlation is only linear association as said at the bottom of page 3 "There is correlation between the variables if the association is linear".

    So an exponential or logarithmic association is monotonic but is not correlation.
 
 
 
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