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Edexcel S3 - Wednesday 25th May AM 2016

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Original post by Zacken



Because it's asking for the probability that the mean number of calls in the five minute interval, that is, you want to find a distribution for the mean number of alls. This means you need to use the CLT, it's not a basic poisson at all, that would be find the probability that the number of calls (see that I didn't use the word mean) is less than x.




thanks! okay final question i promise, ex3D q11a asks for probability of total error, but if it said total mean error would that be different?
Reply 1021
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kennz
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Original post by MarcHughes
It might help if you look at exercise 4A in the S3 Edexcel book. It's basically only used if they ask for it.


Got it thank you, its pretty obvious when they use it to be honest
Original post by AakashG
thanks! okay final question i promise, ex3D q11a asks for probability of total error, but if it said total mean error would that be different?


Yep, you'd need to use CLT.
Original post by Zacken
Yep, you'd need to use CLT.


BUT THE SOLUTIONS USE CLT FOR CALCULATING THE TOTAL ERROR EVEN THOUGH IT DOESNT SAY TOTAL MEAN ERROR

im gunna die ffs:frown:
Original post by Katiee224
Gotcha :wink:

I feel like Will from the inbetweeners when he's revising for his exam and starts doubting eveything he knows :laugh:


Is this a word?

Yes... council?
If you get an expected value of bang on 5, do you still combine?
Original post by Rkai01
If you get an expected value of bang on 5, do you still combine?


The book says <5 so I would assume not
Original post by Rkai01
If you get an expected value of bang on 5, do you still combine?


AFAIK, no.
Original post by AakashG
BUT THE SOLUTIONS USE CLT FOR CALCULATING THE TOTAL ERROR EVEN THOUGH IT DOESNT SAY TOTAL MEAN ERROR

im gunna die ffs:frown:

Total error/n=mean so total error is nmean
Original post by physicsmaths
Total error/n=mean so total error is nmean


bro that makes so much sense! okay fine, but by that definition doesn't that total error is the same as total mean error?
Reply 1030
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Inges
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Original post by Zacken
CLT is used when you want to find the distribution of the sample mean.


Let me break it down,

The central limit theorm says if you have a large sample, say you have a sample of 100 fish lengths (generally large is defined as >50). The mean of those lengths will be normally distributed. So if you were doing a hypothesis test and it says something along the lines of " 100 fishes had mean length 20 and variance 4" WITHOUT saying it is normally distributed. Then you have used to CLT to say they are normally distributed because there is so many of them.

Rule of thumb if you dont understand is that if it gives you a mean, a number of readings (100 fish lengths) and a variance, you will have used the CLT to treat it as a normal distribution.
Original post by Inges
Let me break it down,

The central limit theorm says if you have a large sample, say you have a sample of 100 fish lengths (generally large is defined as >50). The mean of those lengths will be normally distributed. So if you were doing a hypothesis test and it says something along the lines of " 100 fishes had mean length 20 and variance 4" WITHOUT saying it is normally distributed. Then you have used to CLT to say they are normally distributed because there is so many of them.

Rule of thumb if you dont understand is that if it gives you a mean, a number of readings (100 fish lengths) and a variance, you will have used the CLT to treat it as a normal distribution.


Quoted the wrong person there, I think.
Reply 1032
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Original post by AmarPatel98
Hi, just to clarify, how exactly would you set out your working for these q's to gain all the marks?


When you write Sum of O2 / E , You have to write every number your adding together, basically showing how your adding all your observed frequenices squared divided by your expected frequencies and how your taking away the original Total and then how you got the Chi-squared value. Then compared the Chi-squared value with the critical value from the table showing if its in the critical region or not. Then stating your conclusion.
Reply 1033
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Original post by Zacken
Quoted the wrong person there, I think.


damn it, thanks!
Original post by Zacken
S3 seems to be a popular module this year, weird.


Schools finally figured out it'll increase chances of their students getting better grades in FM.
Original post by Zacken
Same way that you would for a non-grouped data. Replace all the sum of x^2 or sum of x whatevs with sum of (x * frequency). It's S1 stuff, come on.


got it thnx again.
Reply 1036
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Original post by Katiee224
In what instances would we want to use the Central Limit Theorem? Like when does it come in handy?

Im worried i'm forgetting everything before the exam :frown:


Let me break it down,

The central limit theorm says if you have a large sample, say you have a sample of 100 fish lengths (generally large is defined as >50). The mean of those lengths will be normally distributed. So if you were doing a hypothesis test and it says something along the lines of " 100 fishes had mean length 20 and variance 4" WITHOUT saying it is normally distributed. Then you have used to CLT to say they are normally distributed because there is so many of them.

Rule of thumb if you dont understand is that if it gives you a mean, a number of readings (100 fish lengths) and a variance, you will have used the CLT to treat it as a normal distribution.
Original post by Zacken
S3 seems to be a popular module this year, weird.


30 people are doing it in my city, as opposed to 13 doing M3 :lol: You'd expect more people to shy away from the module after the reaction to the 2015 paper.
Reply 1038
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PEOPLE! Fire away questions! This is my revision since I've done all the past papers to the point where I remember the mark schemes !
Original post by Katiee224
Gotcha :wink:

I feel like Will from the inbetweeners when he's revising for his exam and starts doubting eveything he knows :laugh:


To be fair, I have played FIFA when I'm supposed to be revising/just before an exam :tongue: just to keep me as calm as a cool cucumber. And doubting myself, and spending time with someone I shouldn't be but not claiming to sleep with people when I haven't :tongue:

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