approximation

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Related to normal approximation: normal distribution, Central limit theorem

approximation

 [ah-prok″sĭ-ma´shun]
1. the act or process of bringing into proximity or apposition.
2. a numerical value of limited accuracy.
successive approximation shaping.

ap·prox·i·ma·tion

(ă-prok'si-mā'shŭn),
In surgery, bringing tissue edges into desired apposition for suturing.

approximation

/ap·prox·i·ma·tion/ (ah-prok″sĭ-ma´shun)
1. the act or process of bringing into proximity or apposition.
2. a numerical value of limited accuracy.

ap·prox·i·ma·tion

(ă-prok'si-mā'shŭn)
In surgery, bringing tissue edges into desired apposition for suturing.

approximation

surgical apposition of wound edges in preparation for suture insertion

approximation,

n a massage technique in which muscle fibers are pressed together along the direction of the fibers in order to relieve cramping.

approximation

1. the act or process of bringing into proximity or apposition.
2. a numerical value of limited accuracy.

normal approximation
approximation of the actual distribution of a variable by a normal distribution.
References in periodicals archive ?
Stabilization methods provide a rate of normal approximation in the sup norm distance for [[?
lambda]]) satisfies the rate of normal approximation (1.
Recall that the normal approximation to the F-distribution forces each [V.
Whether or not the LC is valid, the normal approximation used here causes the error variance, [[[sigma].
Comparing the exact distribution of L with the normal approximation shows that they are very similar in most cases.
On first acquaintance with the central limit theorem, it is not uncommon for undergraduates to ask how accurate the normal approximation is for a sum of (say) twenty random variables.
For large samples (n [greater than or equal to] 30), we use the normal approximation to the t distribution as the basis for constructing 100 (1 - [alpha]) percent confidence intervals for R,, or to state this another way, we use S as a proxy for Q in the confidence interval from the previous section.
Agresti and Yang (1987) provide nice explanations about why normal approximation works under these conditions.
As before, this is more skewed than Figure 3; the problem with Figure 3 being that the control lines are calculated using the (very rough) normal approximation to the binomial distribution.
The usual approach in such situations is to calculate the probability based upon the Poisson distribution or the normal approximation to the binomial.
We propose an analysis based on a normal approximation that can be used when the variance of number of admissions at the individual level is known and none of the counties is too small.