p pa pb pc pd pe pf pg ph pi pk pl pm pn po pp pr ps pt pu pv pw px py

Перевод: poisson

пуассон

Тезаурус:

1. The ordinary least squares model, with forced zero intercept term, is pop 81 = 5.41 houses or, with Poisson errors: pop 81 = 5.40 houses This result implies that each pixel classified as having people living in it, and there are 86 178 of these, will on average contain 5.4 people.
2. The regression model for the i t h individual was log (y i ) =log (T 2 i) + 1 x i 1 + =+ p x i p + i , where y i is the observed number of short spells of absence T i is the number of years of follow up, x i 1 x i p are the explanatory factors of interest, and i is a Poisson error term.
3. The error term was set as Poisson and the logarithm of person years at risk was declared as an offset.
4. In the best observations, group 1, the power spectra of both observations show three features (Fig. 1 c, e ): a red-noise component which rises towards low frequencies, a broad peak with a centroid frequency around 2mHz and a flat component at high frequency due to Poisson noise.
5. The average ward population is 9488 (Table 5.1) and so the RMS errors are comparatively small (the range of RMS errors is from 815.13 for the Shotgun ordinary least squares (OLS) to 1035.29 for the Simple Poisson).
6. For the long observations (1984/193,1986/019 and 1986/062) the null hypothesis was that only red noise was present (other than the experimental Poisson noise).
7. ERIC CANTONA, not yet a rebel with a cause celebre, cut a solitary figure at the back of the United team bus, nibbling poisson and pommes frites by himself as he digested his new club's winning performances.
8. Poisson's Ratio is computed from the ratio of compressional velocity to shear velocity obtained from the analysis of waveforms recorded with the Long Spaced Sonic Tool or Digital Sonic Tool.
9. Noting that , we obtain , which is known as Poisson's equation.
10. The broad feature, which we can loosely refer to as a quasi-periodic oscillation (QPO) cannot be seen directly in the light curve, because many cycles need to be averaged to overcome Poisson noise.
11. For the short observations, the null hypothesis is that only Poisson noise is present.
12. For long spells of absence, the Poisson model was used but no overdispersion was detected.
13. We can stir in the other two data sets, taking Poisson noise only as the null hypothesis; again, the probability that power-law and/or Poisson noise explains all the data sets is less than 5%.