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governmental    音标拼音: [g'ʌvɚm,ɛntəl] [g,ʌvɚnm'ɛntəl]
a. 政治的,统治上的,政府的

政治的,统治上的,政府的

governmental
adj 1: relating to or dealing with the affairs or structure of
government or politics or the state; "governmental
policy"; "public confidence and governmental morale"

Governmental \Gov"ern*men"tal\, a. [Cf. F. gouvernemental.]
Pertaining to government; made by government; as,
governmental duties.
[1913 Webster]


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  • statistics - Choosing $H_0$ and $H_a$ in hypothesis testing . . .
    With the rationale that H0 must include the equality, which in this case is greater or equal to 30% Her solution than failed to reject the null hypothesis and concluded that the researcher's claim is therefore correct
  • How old is the universe? - NASA
    The Hubble constant (H0) is a measure of the current expansion rate of the universe Cosmologists use this measurement to extrapolate back to the Big Bang, the explosion which occurred at the start of the known universe This extrapolation depends upon the current density of the universe and on the composition of the universe
  • What is the probability of a null-hypothesis being True?
    Can we say anything about the probability of H0 being true in such case? Since H0 and H1 are mutually exclusive, and in the case of not rejecting H0, the probability of H1 is very small (since the power of the test is very large), can we say with 0 99 (99%) probability that H0 is true?
  • Hypothesis testing. Why center the sampling distribution on H0?
    If one would instead use the sampling distribution of the statistic, i e center the distribution on the sample statistic, then hypothesis testing would correspond to estimating the probability of H0 given the samples
  • hypothesis testing - Is it okay to interpret P values as a probability . . .
    A p-value is the probability of the observed data (or of more extreme data points), given that the null hypothesis H0 is true, defined in symbols as p (D|H0) On the same page, the authors present 6 misconceptions of a p-value
  • Frequentist vs bayesian and P (data | H0) vs P (H0 - Cross Validated
    I understand that I'm comparing expected values but, as far as I understood, in the z-test I'm getting the prob that H0 is true given the differences of means (and std), while in the bayesian approach I estimate the distribution of the data and then compute the probability that H0 is true My confusion comes from seeming to get P (H0|data) = P (data|H0)
  • regression - Does H0:beta_1=0 in SLR assume normality of outcome which . . .
    “Maybe the H0:Yi∼N (β0,σ2) actually means there’s some other association that requires normality of the outcome, just that it’s not linear, so β1=0?” This is predicated on a false assumption that the outcome must be normally distributed Also, in SLR the null only applies to linear relationships
  • Accepting the alternative hypothesis when it is not the complement of . . .
    NHSTs don't provide a proper basis for accepting H0 or H1, we should just say "we reject H0" or "we fail to reject H0" and leave it at that Note that NHSTs are not symmetric, if we exchange H0 and H1 it may give a non-significant result both ways round (which usually indicates we don't have enough evidence from the data)
  • What follows if we fail to reject the null hypothesis?
    Of course, power depends on effect size, so if you say "your test has very high power" that must refer to some pre-specified effect size I'd agree that failing to reject H0 in this case may be interpreted as evidence in favor of an "extended H0", namely that the true effect size if smaller than the target effect size for which power was computed
  • Null hypothesis definition - Cross Validated
    You seem to have stumbled upon something like non-inferiority testing, but the reason is that we want to disprove H0 H 0 in favor of some alternative We are, in some sense, hoping for the alternative to be true





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