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50 Cards in this Set

  • Front
  • Back
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)