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# OCR (non mei) S2 Wednesday 15th June 2016

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1. Hi!This is for discussion pre and post the up coming S2 exam
Lets start the discussion!
For past papers and mark schemes:
http://www.ocr.org.uk/qualifications/as-a-level-gce-mathematics-3890-3892-7890-7892/
For some older past papers:
http://www.physicsandmathstutor.com/...papers/s2-ocr/
2. this is quiet
3. I am struggling to figure out when you divide the variance by the sample size and when you don't. I thought that you didn't divide by sample size when the original distribution is normal but I did a past paper question where you did.
4. (Original post by hallo.C)
I am struggling to figure out when you divide the variance by the sample size and when you don't. I thought that you didn't divide by sample size when the original distribution is normal but I did a past paper question where you did.
generally, you should divide by n when there is a question involving a sample

You do divide by n when the original distribution X is normal , however you are not using the central limit theorem, because the distribution of the sample mean is exactly normal.

hope this helps? ??? ? ?
5. (Original post by duncanjgraham)
generally, you should divide by n when there is a question involving a sample

You do divide by n when the original distribution X is normal , however you are not using the central limit theorem, because the distribution of the sample mean is exactly normal.

hope this helps? ??? ? ?
So is it only if I have worked out an unbiased estimate of a distribution that is originally normal, that I don't divide the variance by sample size?
6. Sorry to keep pushing this but I don't understand. June 2008 question 3, you divide by sample size when it's normal and then on question 2 January 13, you don't divide by sample size because it's normal?
7. (Original post by hallo.C)
Sorry to keep pushing this but I don't understand. June 2008 question 3, you divide by sample size when it's normal and then on question 2 January 13, you don't divide by sample size because it's normal?
jan 13 is an absolute b i t c h of a paper, i just did it today.

q 2 is a bit of a special case, it's not asking for the distribution of the sample mean of C - (C with a line on top of it) - and is just interested in the distribution of C, and using the data (sum of x/ n etc) helps estimate the distribution of C.

if you were asked to find the distribution of the sample mean of C for this question, you would divide the variance by n.

Your query is quite hard to help you with, you may need a teacher.. . . .
8. Hi,

You know for some of the normal distribution questions, in the mark scheme they always give the z value to 4 decimal places. But, the tables only give you an answer from 3 decimal places. So, I get a different answer sometimes from what they accept. Does that matter or will they still accept my answer?Thanks!
9. (Original post by Nl1998)
Hi,

You know for some of the normal distribution questions, in the mark scheme they always give the z value to 4 decimal places. But, the tables only give you an answer from 3 decimal places. So, I get a different answer sometimes from what they accept. Does that matter or will they still accept my answer?Thanks!
Hey, in the mark scheme is normally says if it rounds to ... So say it says 0.6547 it will also say in the side bit or r.t 0.655,
Hope this helps
10. (Original post by TheNicholas)
Hey, in the mark scheme is normally says if it rounds to ... So say it says 0.6547 it will also say in the side bit or r.t 0.655,
Hope this helps
Yeah but sometimes because of calculating to a lower degree of accuracy, the answer is slightly different and still doesn't round to the the answer in the markscheme even to 3s.f.
11. (Original post by Nl1998)
Yeah but sometimes because of calculating to a lower degree of accuracy, the answer is slightly different and still doesn't round to the the answer in the markscheme even to 3s.f.
Umm personally that's never happened to me and I've done all the papers, are you sure you're using continuity corrections or something like that???
12. Just had a look at the previous few years' grade boundaries and jeez are they high! I was feeling a bit confident about this but now I'm not so sure.
13. (Original post by PotAuFeu)
Just had a look at the previous few years' grade boundaries and jeez are they high! I was feeling a bit confident about this but now I'm not so sure.
very high indeed,
14. Not looking forward to this at all
Can anyone help me with when to use the 1/2n continuity correction please? i have no idea!
Also in some of the mark schemes it says it will give you the full marks even if your continuity correction is incorrect/used wrong/ not even used so im relying on that to do well in stats lol
15. (Original post by duncanjgraham)
Fck it's hard to give examples but basically if you were correcting P(X<5) you would make it P(X<5+0.5) but if it's a case where 1/2n is required you actually make P(X<5) into P(X<5minus1/2n)

Likewise you'd make P(X>5) into P(X>5+1/2n) where you would normally make it P(X>5-0.5)

I hope this helps.
Are you sure that the continuity correction for P(X<5) is P(X<5+0.5)?
The way I remember it is that you want it <5 so take away 0.5 so you don't include 5 and if you want >5 then add 0.5 so you don't include 5. Likewise, if you want <=5 then add 0.5 to include 5 and if you want >=5 then you take away 0.5 to include 5.
Hope that makes sense, I haven't seen particularly many 1/2n corrections but when I have, I just do my continuity corrections as I said so above and the answer comes out fine.

For when to use 1/2n, I found http://www.mathshelper.co.uk/OCR%20S...on%20Sheet.pdf to be helpful. Basically, do it when you are approximating a discrete distribution using the CLT.
16. Can someone please explain to me when I do and when I do not divide the variance by n - it would be very much appreciated!
17. (Original post by PotAuFeu)
Are you sure that the continuity correction for P(X<5) is P(X<5+0.5)?
The way I remember it is that you want it <5 so take away 0.5 so you don't include 5 and if you want >5 then add 0.5 so you don't include 5. Likewise, if you want <=5 then add 0.5 to include 5 and if you want >=5 then you take away 0.5 to include 5.
Hope that makes sense, I haven't seen particularly many 1/2n corrections but when I have, I just do my continuity corrections as I said so above and the answer comes out fine.

For when to use 1/2n, I found http://www.mathshelper.co.uk/OCR%20S...on%20Sheet.pdf to be helpful. Basically, do it when you are approximating a discrete distribution using the CLT.
P(X<5) is P(X<5 -0.5) as I remember it as well.
18. (Original post by emmacurtis8)
Can someone please explain to me when I do and when I do not divide the variance by n - it would be very much appreciated!
Someone will definitely explain this better than me but whenever you are working with the mean of n occurrences (a sample) you need to divide variance by n. In the question it may explicitly mention "the mean of n occurrences" or "a random sample of n things", or you might see x-bar (x with a line over the top) which indicates that you need to use the mean of the sample. Pretty much if it mentions mean (and you're using the normal distribution) then you need to divide the variance by n.
19. (Original post by emmacurtis8)
Can someone please explain to me when I do and when I do not divide the variance by n - it would be very much appreciated!
I'm not absolutely 100% sure, but this is what I know so far and it's been working, it would be good for someone to double check me.

For a start if you ever have a discrete distribution (Binomial, Poisson) and approximate it with normal, you will NEVER (in S2 OCR) have to divide the variance by n, I don't know why, you just don't.

If you are told, the "population variance" or the question implies somehow that the parameters for your distribution are known for the entire population instead of either given or worked out from a sample, you DON'T divide by n. After looking at some papers, ignore this.

If you have used the central limit theorem, then divide by n (duh), also apply the 1/2n correction if the distribution/data was discrete.

If you see the distribution letter (X, S, Y... e.t.c) with a bar, divide by n.

This isn't as reliable as the ones above, but if you see the words "sample mean" you 70% are likely to divide by n.
20. there's misunderstanding in this thread

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