<p>I’m assumin u mean 2 proportions CI/sig test…</p>
<p>CI:
SRS, pop is 10X n, both n’s are big so that np>=10 or 5 or so, same with n(1-p).</p>
<p>For sig test:
same thing, except p-hat must also = ((x1+x2)/(n1+n2))</p>
<p>I got a question:
Can someone explain when to use a normal, binomial, and geometric cdf/pdf…and how to enter it into calculator???</p>
<p>95% confidence interval means that 95 out of 100 values will be contained in (insert range here)…and that we are 95% confident that the true mean/proportion of (insert problem context here) lies between (insert rang)</p>
<p>to r1400sch</p>
<p>normal is used only when you have z scores it is normcdf(z,99) <you never really need to use pdf</p>
<p>geometric is when you want the probability of only ONE sucess. Geopdf is for on specifically the nth trial geopdf(P sucess, nth trial) if you want a sucess WITHIN n trials (ie n or less) then use cdf Geocdf(P sucess, nth trial)</p>
<p>Binomial is P of x sucesses within n trials again pdf gives exactly and cdf is within. key order is binomialpdf/cdf(n,P,x)</p>
<p>just a quick ?
when a prob asks you to find an sample size with 95% confidence so that the margin of error is 5…
m = z* x sigma/square root of n…</p>
<p>does m = 5 or 2.5 (2.5 on each side makes the total 5)?</p>
<p>CI = test statistic +/- Z* times standard deviaton or SEM
the part after the “+/-” will equal 2.5</p>
<p>as far as CI’s
I suggest not using the phrase “lies between”
instead, use captures or contains</p>
<p>only because the former suggest that the true parameter is moving within the range when actually it is the rang that is moving to “capture” the true parameter</p>
<p>To Dpat, you usually don’t say margin of error is 5 when its actually +/- 2.5, you say that the margin of error is 2.5.</p>
<p>wait … but if the problem asks him to find the sample size WHEN the margin of error is 5, isn’t margin of error just … 5? He’s trying to find sample size n, not margin of error. Or did I misunderstand it?</p>
<p>Lucidity, whenever they ask you to find n for a certain margin of error, You use that number as m in the equation just like you said. Dpat seemed to interpret margin of error in a different way, and that’s why explained it like that in post#27.</p>
<p>In that situation you will set 5=z*std. deviation, since that is your margin of error. You will just solve for n from this equation.</p>
<p>I’m a little confused on two thigns. Which Chi test is the one with the matrixes, what’s the other one (where you do List 1, list 2, than (O-E)^2/E)?</p>
<p>Also, what are the assupmtions for each?</p>
<p>Chi-square</p>
<p>You always do sum of (Observed-Expected)^/E. Observed for a table is (row toatl)(column total)/total.</p>
<p>Assumptions is that all things are bigger than 1, only 20% less than 5.</p>
<p>And if I’m wrong, someone should correct me.</p>
<p>chi-squared test for independence and chi-squared test for goodness of fit are both done using matrices</p>
<p>Not even sure how to do a test for homogeneity, i think it was #6 for one AP FR</p>
<p>Btw, I see a lot of variations between conditions for CHI-SQUARED.
Supposedly our teacher told us to use the following for all three tests:
<p>someone verify? i’m pretty sure it doesn’t apply to homogeneity but i really need to find a fully worked example of that test anyways.</p>
<p>shravas, the expected count is (row total)(column total)/(grand total) NOT the observed</p>
<p>Goodness of fit is done with two lists. Homogeneity and independence are tested using matrices (they are essentially the same test, just different explanations).</p>
<p>One assumption that I’ve seen is independently collected samples, not sure if that one is common though.</p>
<p>EDIT: And of course the data should be counted…</p>
<p>Snipez90–
Those are the same conditions my teacher told us to follow, but I’m looking around various Stat texts for last-minute verification.</p>
<p>This is kinda stupid, but how do you know which hypothesis should be set as the null and which should be set as the alternative?</p>
<p>Your null hypothesis is typically mu=0 or some variant of that (like mu1-mu2=0 for 2-sample tests). Your alternate hypothesis on the other hand usually has doesn’t-equal, greater-than (>), etc.</p>
<p>oh god i didn’t even realize there were 3 different chi tests…i am so screwed</p>
<p>eep. Do we have to know advanced linear regression?</p>