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Mathematics 18 Online
OpenStudy (vane11):

Stats Q attached

OpenStudy (vane11):

OpenStudy (vane11):

I have the formula z= (xbar-u)/\[\sigma/\sqrt{n}\]

OpenStudy (vane11):

not too pretty, but the best I could do since they don't have all the symbols

jimthompson5910 (jim_thompson5910):

Because the two data sets are independent, this means this isn't a paired t-test Basically one set doesn't affect the other, so that's how we know the two sets are independent

jimthompson5910 (jim_thompson5910):

so we use a two sample t-test to answer these questions quick question: does it say that the population variances are assumed to be equal?

OpenStudy (vane11):

let me check

jimthompson5910 (jim_thompson5910):

ok

OpenStudy (vane11):

no it doesn't, the information shown is all they gave...

jimthompson5910 (jim_thompson5910):

ok I'm going to assume that the population variances are not equal

jimthompson5910 (jim_thompson5910):

let s1 and s2 be the sample standard deviations of the first and second lists respectively

OpenStudy (vane11):

ok

jimthompson5910 (jim_thompson5910):

using a calculator, these standard deviations are s1 = 10.2470444248302 s2 = 7.9717382741226

jimthompson5910 (jim_thompson5910):

we also need the sample means xbar1 and xbar2, and they are (found with a calculator) xbar1 = 61.7877777777778 xbar2 = 60.1111111111111

jimthompson5910 (jim_thompson5910):

now we use the sample standard deviations, along with the value of n to find the standard error SE SE = sqrt( ((s1)^2)/(n1) + ((s2)^2)/(n2) ) SE = sqrt( ((10.2470444248302)^2)/(9) + ((7.9717382741226)^2)/(9) ) SE = sqrt( (105.001919444444)/(9) + (63.5486111111111)/(9) ) SE = sqrt( 11.6668799382716 + 7.06095679012346 ) SE = sqrt( 18.7278367283951 ) SE = 4.32756706804124

jimthompson5910 (jim_thompson5910):

we now use this standard error SE in the test statistic formula t=(observed difference - expected difference)/(Standard error for the difference) t=((xbar1-xbar2)-(mu1-mu2))/(SE) t=((61.7877777777778-60.1111111111111)-0)/(4.32756706804124) t=(1.67666666666667)/(4.32756706804124) t=0.387438632447485

jimthompson5910 (jim_thompson5910):

so the test statistic is roughly t = 0.387

OpenStudy (vane11):

hmm... ok I see what I was doing wrong, I was using x1 and x2 instead of the xbars in the formula and kept getting numbers that weren't an option

jimthompson5910 (jim_thompson5910):

what do you mean by x1 and x2?

OpenStudy (vane11):

i was using the first two values on the left side of the chart as my x's

jimthompson5910 (jim_thompson5910):

ah i see

OpenStudy (vane11):

yes, but now i see it was the formula i misunderstood

jimthompson5910 (jim_thompson5910):

and unfortunately #6 is incorrect as well

OpenStudy (vane11):

actually, i fixed that one, but the answer on 5 turned out to be A, the one i had... I'll recheck your math in a few minutes, after I get some more problems turned in

jimthompson5910 (jim_thompson5910):

hmm I guess it was a paired t-test then

OpenStudy (vane11):

could you help me out with one more?

jimthompson5910 (jim_thompson5910):

because this is what I get with a paired t-test t = (d_bar-mu_d)/(S_d/sqrt(n)) t = (1.67666666666667-0)/(5.15497090195473/sqrt(9)) t = (1.67666666666667-0)/(5.15497090195473/(3)) t = (1.67666666666667-0)/(1.71832363398491) t = (1.67666666666667)/(1.71832363398491) t = 0.97575720516534 so that's pretty close...I just wasn't sure if it was a paired t-test

jimthompson5910 (jim_thompson5910):

sure

OpenStudy (vane11):

thanks :)

jimthompson5910 (jim_thompson5910):

np

OpenStudy (vane11):

I'll post a new Q

jimthompson5910 (jim_thompson5910):

ok

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