Hi! I'm doing some past papers and there was this question that confused me. So I left it and went back to it after I finished the rest of the paper. I looked at the mark scheme later, and it said that you have to use xbar-ns, but I haven't come across this formula before? I'd really appreciate if someone could explain what it's used for and what it means! Thank you in advance
Edit: I know that xbar is the mean and s is the sample standard deviation. I just don't know when it should be used and what for
Hi! I'm doing some past papers and there was this question that confused me. So I left it and went back to it after I finished the rest of the paper. I looked at the mark scheme later, and it said that you have to use xbar-ns, but I haven't come across this formula before? I'd really appreciate if someone could explain what it's used for and what it means! Thank you in advance
Edit: I know that xbar is the mean and s is the sample standard deviation. I just don't know when it should be used and what for
for the question, n was used as 2 and I'm not sure how the 2 came about either. So the answer came out as 45.8-2(24), where 45.8 is the mean and 24 is the standard deviation
for the question, n was used as 2 and I'm not sure how the 2 came about either. So the answer came out as 45.8-2(24), where 45.8 is the mean and 24 is the standard deviation
Ah, that is something of a nasty question.
You may not have heard explicitly of the 68-7-95-99.7 rule but you will have seen it in action. It just says that 68% of data is within 1 standard deviation of the mean, 95% is within 2 standard deviations and 99.7% is within 3 (which is why they've used 'n=2' and 'n=3' when it seems they've plucked it out of thin air). But anyway, they use that to find a range in which 95% of the data probably lies and since a lot of values are negative it's not good enough to model a distribution where all of the values are positive. And in this case, you're using s, the estimate for standard deviation rather than standard deviation itself.
You may not have heard explicitly of the 68-7-95-99.7 rule but you will have seen it in action. It just says that 68% of data is within 1 standard deviation of the mean, 95% is within 2 standard deviations and 99.7% is within 3 (which is why they've used 'n=2' and 'n=3' when it seems they've plucked it out of thin air). But anyway, they use that to find a range in which 95% of the data probably lies and since a lot of values are negative it's not good enough to model a distribution where all of the values are positive. And in this case, you're using s, the estimate for standard deviation rather than standard deviation itself.