This preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Unformatted text preview: Jamie Julin: ok, so pick the point where the difference is greatest like it says Jamie Julin: even if its just a small difference between the choices roohi: oh so does it make sense to have a difference of .9 roohi: be like the greatest? roohi: does tha sound reasonable Jamie Julin: thats for you to decide roohi: am i on the right track Jamie Julin: if youre using the applet like we just talked about, then yes K. Wei: i have a quick question about q29 ch15 K. Wei: if u have a moment Jamie Julin: go for it K. Wei: i understand that as the number of draws goes up the proximity to the normal curve increases K. Wei: but does percentage of draws/total affect it at all? Jamie Julin: its number of draws that matters, not percent of draws roohi: oh hey jamie si the difference in percentages that we reportor the difference in areas K. Wei: so if you drew 10 draws from 20 possible numbers, it would be less accurate than if u drew 11 draws from 1000 possible numbers? Jamie Julin: percentage is equivalent to area in histograms Jamie Julin: yes, the normal approximation improves based on the # of draws not the percent of draws... even if it seems strange K. Wei: that what i thought at first but when i said that the normal aprox to the distrubtion fo the sample sum of 10 indepdent random draws with replacement from a box of 100 was less accurate than the normal approx to the distrubtion of sample sum of 100 indepdent random draws with replacement from box of 1000 K. Wei: it was marked wrong K. Wei: which is why im confused now Jamie Julin: ok, but is the number of draws the only thing that matters for normal approximations? K. Wei: well skewed ness K. Wei: is what matters as well Jamie Julin: right good Jamie Julin: so do you know anything baout skew here K. Wei: but it doesnt mention skewedness at all K. Wei: so i guess theres no way to determine it Jamie Julin: right K. Wei: so i guess accuracy cannot be determined without knowing if they have same skwewed ness Jamie Julin: yep K. Wei: tricky K. Wei: lol K. Wei: thanks a lot Jamie Julin: no problem James: can i ask a question? janet: hi jamie, for q22, The distribution of the sample sum for a sample of size 10 is (Q22) the distribution of the population janet: oh ok i'll wait Jamie Julin: sure james go ahead Jamie Julin: ill look at q. 22 too though Jamie Julin: ok janet, so did you click the 'take sample' button and can you see the distribution versus the sample janet: oh for me, i'm not sure how to tell whether its less skewed or more skewed James: I have question on Q28,29,30 on problem set 15 janet: does the distribution mean the normal curve Jamie Julin: yes thats what i mean janet: oh ok yeah i did that Jamie Julin: well skewed would be like less normal looking janet: so for q23, when i clicked the take sample button, the bars were all underneath the normal curve, so does that make it more skewed?...
View
Full Document
 Summer '08
 anderes
 Statistics, Normal Distribution, Probability, Standard Deviation, Jamie Julin

Click to edit the document details