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### 50 Cards in this Set

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 formula for SD square root [ average {entries squared} ] r average of { x in SU X y in SU} rms error square root of (1-r squared) formula for r graph y = r (SDy/SDx) x + b addition rule? add probabilities when mutually exclusive to find the chance that at least one thing happens multiplication rule? multiply probabilities when independent to find out the chance that BOTH happen weird formula to find out exact chance? binomial formula law of averages the larger number of tosses, likely to get closer to expected value but less like to be exactly expected value (error large in absolute terms, but small relative to number of tosses) EV number of draws x average of the box SE (normal - when drawing at random with replacement from a box with numbered tickets) square root of draws X (SD of box) what happens if you want to find the probability for a box whose contents do not follow the normal curve doesn't matter, probability will follow nomral curve even if contents don't problems with sampling sampling bias, non-responder bias EV in sampling population percentage SEpercentage SE = (SE for #/ sixe of sample) x 100% how sample size impacts accuracy JUST ABSOLUTE SIZE bootstrap method? use observed results (fracts) as true fracts EVaverage average of box SEaverage SE for sum / # draws SEsum square root (draws) x (SD box) SE average Se sum/ draws SE count Se for sum (1-0 box) SE% Se count/ # draws x 100% z observed-expected (z says how many SES an observed value is from its expected valkue P is less that 5% statistically significant P is less that 1% highly significant SD + square root (number of measurements/(number of measurements - 1)) x SD degrees of freedom (2 sample z test) # measurements - 1 SE difference (if independent) square root (SE1 squared + SE2 squared) zdifference observed difference - expected difference / SE for diference chi squared sum of ((observed frequency-expected frequency)squared/ expected frequency) bootstrap method? use observed results (fracts) as true fracts EVaverage average of box SEaverage SE for sum / # draws SEsum square root (draws) x (SD box) SE average Se sum/ draws SE count Se for sum (1-0 box) SE% Se count/ # draws x 100% z observed-expected (z says how many SES an observed value is from its expected valkue P is less that 5% statistically significant P is less that 1% highly significant SD + square root (number of measurements/(number of measurements - 1)) x SD degrees of freedom (2 sample z test) # measurements - 1 SE difference (if independent) square root (SE1 squared + SE2 squared) zdifference observed difference - expected difference / SE for diference chi squared sum of ((observed frequency-expected frequency)squared/ expected frequency) degrees free (CHI squared test) # of terms in chisquared - 1 Chi squared test to find out how likely observed data is when there is box of tickets w/ contents given draws made @ random w/replacement chi squared formula, degrees free = terms in chi squared - 1 chi squared to show how data was fudged chi squared/ degrees of freedom can be added up chi squared to test independence 1 - add up all and make percentages (on whole) then calculate expected 2 - compute x2 3 degrees of freedom (m-1) x (n-1) a note on tests of singificance arbitrary (lead to cheating)