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# S3: Goodness-of-fit test for normal distribution watch

1. .cccccc
2. bump
3. I don't think enough people do S3 for you to be able to get a good answer
4. (Original post by Nikhilm)
.cccccc
5. (Original post by SeanFM)
Hey,

It was to do with how you lay out an answer for goodness-of-fit tests with normal distributions
I know you have to extrapolate the class widths at the ends, e.g. if question says

3-4
4-5
...
9-10

You would do
< 4
4-5
...
> 9

But, on the june 2013 mark scheme they just left it in the first form, and also the book changes between both
6. (Original post by Nikhilm)
Hey,

It was to do with how you lay out an answer for goodness-of-fit tests with normal distributions
I know you have to extrapolate the class widths at the ends, e.g. if question says

3-4
4-5
...
9-10

You would do
< 4
4-5
...
> 9

But, on the june 2013 mark scheme they just left it in the first form, and also the book changes between both
Okay, I've had a look at the 2013 paper.

I would say that because time is continuous (whereas size is not) you don't need classes for continuous data (0-3, 3-5 etc suffices) but for something like sizes or number of cars, you need those classes.
7. Yes! Quite possibly

Also, by any chance do you know what 'underlying Normal distribution' is, and how it is different from just 'Normal distribution'?

SeanFM
8. (Original post by Nikhilm)
Yes! Quite possibly

Also, by any chance do you know what 'underlying Normal distribution' is, and how it is different from just 'Normal distribution'?

SeanFM
I don't know myself, but see this document, page 27.

From what I understand, an underlying distribution is just what the actual distribution of the sample is. (In similar words, what distribution produces the sample value that we get). Whereas we usually are given sample values and have to work out an estimator (which isn't the underlying distribution) and use that as a distribution.

Not of that much important in S3 as far as I'm aware.
9. (Original post by SeanFM)
From what I understand, an underlying distribution is just what the actual distribution of the sample is. (In similar words, what distribution produces the sample value that we get).
Just to be utterly nit-picky, the underlying distribution is the distribution of the population from which the sample is drawn. A big chunk of statistics involves inferring facts about the population distribution from a sample distribution.
10. (Original post by Gregorius)
Just to be utterly nit-picky, the underlying distribution is the distribution of the population from which the sample is drawn. A big chunk of statistics involves inferring facts about the population distribution from a sample distribution.
I stand corrected.
11. (Original post by SeanFM)
I stand corrected.
Thanks to you both!

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