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November 01, 2025, 10:35:53 am

Author Topic: VCAA 2011 CORE Q11  (Read 1295 times)  Share 

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mooimachicken

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VCAA 2011 CORE Q11
« on: October 28, 2012, 08:19:11 pm »
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hey, for this question I got B, but the answer says D. How are you meant to know the difference between the 2, since the two say similar/the same thing??

« Last Edit: October 28, 2012, 08:36:50 pm by mooimachicken »

astone788

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Re: VCAA 2011 CORE Q6
« Reply #1 on: October 28, 2012, 08:35:52 pm »
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Slap all the values into your calculater. Then perform 1 var stats. Standard deviation is Sx and mean is x

mooimachicken

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Re: VCAA 2011 CORE Q6
« Reply #2 on: October 28, 2012, 08:36:33 pm »
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sorry I meant q11 XD

Felicity Wishes

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Re: VCAA 2011 CORE Q6
« Reply #3 on: October 28, 2012, 08:39:45 pm »
+1
It is D because of the word 'tended'. I don't think you can make absolute assumptions from the situation and because the r isn't high, the cause of the DV isn't certain, hence tended.
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Daenerys Targaryen

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Re: VCAA 2011 CORE Q6
« Reply #4 on: October 28, 2012, 08:40:52 pm »
+2
B: 'students WOULD become' there is no evidence to show that the WILL
where as
D: '... tended to be fitter' is more reasonable and is shown by the 'r' value.
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mooimachicken

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Re: VCAA 2011 CORE Q11
« Reply #5 on: October 29, 2012, 03:04:06 pm »
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yea cheers, so you can only draw conclusions with the r^2 value, not the r value?

Felicity Wishes

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Re: VCAA 2011 CORE Q11
« Reply #6 on: October 29, 2012, 04:01:43 pm »
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yea cheers, so you can only draw conclusions with the r^2 value, not the r value?

I think you can draw from both but the r or r^2 has to be strong.
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Daenerys Targaryen

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Re: VCAA 2011 CORE Q11
« Reply #7 on: October 29, 2012, 04:37:36 pm »
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doesnt 'r' tell you how linear the data is and 'r2' tells us how much variation in DV can be explained by variaotion in IV?
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StumbleBum

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Re: VCAA 2011 CORE Q11
« Reply #8 on: October 29, 2012, 05:22:50 pm »
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yea cheers, so you can only draw conclusions with the r^2 value, not the r value?
You can only draw conclusions (but not definitive ones) about the linearity and strength of the relationship between two variables from the r value. And similarly you can only draw conclusions (again not definitive) about how much of the variation in the DV is explained by the variation in the IV from the r2 value, as HatersGonnaHate has said.
Any conclusions you make are merely estimations, as that the liner regression itself is an estimation.
doesnt 'r' tell you how linear the data is and 'r2' tells us how much variation in DV can be explained by variaotion in IV?
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julie9300

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Re: VCAA 2011 CORE Q11
« Reply #9 on: October 29, 2012, 07:13:29 pm »
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Correlational data doesn't explain a cause and effect relationship between the two variables, and that's what option B is doing.
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Yendall

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Re: VCAA 2011 CORE Q11
« Reply #10 on: October 30, 2012, 09:46:36 am »
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Just look for words like: 'association', 'tended', 'may', 'might'. The Pearson's Correlation Coefficient doesn't provide any grounded evidence.
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