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quipu    
n. 古秘鲁人的结绳文字

古秘鲁人的结绳文字

quipu
n 1: calculator consisting of a cord with attached cords; used
by ancient Peruvians for calculating and keeping records


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  • When teaching statistics, use normal or Gaussian?
    $\begingroup$ The issue of "which term is more commonly used" can easily be addressed, albeit crudely: A Google search of "Gaussian" distribution has about 2 3 of the hits of a search for "normal distribution " The ratio is a little different on Google Scholar, where now "Gaussian distribution" has half the hits of "normal distribution" (but
  • How are the Error Function and Standard Normal distribution function . . .
    $\begingroup$ Indeed The erf might be more widely used and more general than the CDF of the Gaussian, but most students have a more intuitive sense of the Gaussian CDF so Mathematica's insistence on simplifying everything to erf is not only annoying, but also very confusing $\endgroup$
  • Normal distribution negative values - Physics Forums
    Any normal distribution, regardless of its mean and standard deviation, has infinite tails and therefore nonzero probability of a negative outcome So some physical phenomenon which cannot be negative (such as bus waiting times) cannot truly be normally distributed
  • When (and why) should you take the log of a distribution (of numbers)?
    An approach to evaluating a funky looking distribution could be to take the log of it just to see if it looks more normal; but as IrishStat describes technically above, this path is fraught with danger (of the square peg, round hole variety) $\endgroup$
  • normal distribution - Complete statistic for $\sigma^2$ in a $N(\mu . . .
    If a distribution contains a non-trivial unbiased statistic of zero, then this distribution does not have a complete statistic? 3 Conceptual question - Is it impossible to get a UMVUE for an estimator if another, unknown parameter is required to reach the Cramer-Rao Lower Bound?
  • Understanding Standard deviation in Normal Distribution
    It may help to think of the standard deviation as a measure of central tendency Any normal Gaussian distribution will tend to cluster towards the mean (lets assume the clustering is symmetric to the left and right of the mean) The standard deviation tells us the degree of clustering relative to the mean
  • Why do we assume that the error is normally distributed?
    Due to the Central Limit Theorem, we may assume that there are lots of underlying facts affecting the process and the sum of these individual errors will tend to behave like in a zero mean normal distribution In practice, it seems to be so I'm interested in the second part actually
  • normal distribution - What is the significance of 1 SD . . . - Cross . . .
    It has some additional properties (e g part of the distribution is no more than one standard deviation from the expectation and part of the distribution is no less than one standard deviation from the expectation; $1$ standard deviation above or below the expectation are the inflection points of a normal density curve), but they are ancillary
  • normal distribution - why n gt;=30 for central limit theorem to hold . . .
    From my understanding as size of n increase normal distribution will have smaller standard deviation, this makes sense because using larger sample size will be better at estimating population mean than smaller sample
  • normal distribution - Normalization of data for ANOVA - Cross Validated
    $\begingroup$ The ideal condition for analysis of variance is that conditional distributions (response given predictors) are normal rather than that the marginal distribution is normal Many texts and courses are paranoid to over-cautious on this point, but if in doubt compare results for untransformed and transformed data and certainly proceed





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