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where m is the mass flow rate per unit area, ρ 1 and ρ 2 are the mass density of the fluid upstream and downstream of the wave, u 1 and u 2 are the fluid velocity upstream and downstream of the wave, p 1 and p 2 are the pressures in the two regions, and h 1 and h 2 are the specific (with the sense of per unit mass) enthalpies in the two regions.
The multivariate normal distribution is said to be "non-degenerate" when the symmetric covariance matrix is positive definite. In this case the distribution has density [5] where is a real k -dimensional column vector and is the determinant of , also known as the generalized variance.
The scaled sum of a sequence of i.i.d. random variables with finite positive variance converges in distribution to the normal distribution. In probability theory, the central limit theorem (CLT) states that, under appropriate conditions, the distribution of a normalized version of the sample mean converges to a standard normal distribution.
[4] Pearson himself noted in 1895 that although the term "histogram" was new, the type of graph it designates was "a common form of graphical representation". [5] In fact the technique of using a bar graph to represent statistical measurements was devised by the Scottish economist, William Playfair, in his Commercial and political atlas (1786). [4]
All data used in this section were obtained from the NIST Chemistry WebBook. [8] It is useful to note that for N 2 the normal boiling point of the liquid is 77.4 K and the critical point is at 126.2 K and 34.0 bar. Overview of the temperature and pressure dependence of the compressibility factor for N 2.
The simplest case of a normal distribution is known as the standard normal distribution or unit normal distribution. This is a special case when μ = 0 {\textstyle \mu =0} and σ 2 = 1 {\textstyle \sigma ^{2}=1} , and it is described by this probability density function (or density): φ ( z ) = e − z 2 2 2 π . {\displaystyle \varphi (z ...
Morey et al. [27] point out that several of these confidence procedures, including the one for ω 2, have the property that as the F statistic becomes increasingly small—indicating misfit with all possible values of ω 2 —the confidence interval shrinks and can even contain only the single value ω 2 = 0; that is, the CI is infinitesimally ...
In regression analysis, logistic regression[1] (or logit regression) estimates the parameters of a logistic model (the coefficients in the linear or non linear combinations). In binary logistic regression there is a single binary dependent variable, coded by an indicator variable, where the two values are labeled "0" and "1", while the ...