Sentences with phrase «of sqrt»

If each point in the right slide is obtained as the average of 100 more or less normally distributed points in the left slide, the errors bars shrink by a factor of sqrt (100) = 10.

In the simplest idealization, for example, a linear spring with spring constant K attached to a perfectly rigid support at one end and a point mass M on the other will oscillate at a radial frequency sqrt (K / M), or a pendulum of length L in a constant gravity field with acceleration g will oscillate about the equilibrium at a frequency of sqrt (g / L).

It's hard to tell what point Lindzen wants to make here, if not to persuade his audience to ignore the factor of sqrt (n).

Note that despite the «wide» distribution of sqrt (Kv), the TCR and SLR distributions are not open - ended as shown in Figure 6 of Forest et al. (2008).

Gives examples of sqrt (2), pi and e as irrational numbers.

Not exact matches

The distribution of voters in the class is Binomial with mean p N and standard deviation sqrt (N * p * (1 - p)-RRB-.

Pardon my ignorance, but we're now halfway through a doubling of CO2 since preindustrial times (the current 392 ppm divided by sqrt (2) is 277 ppm, right in the 260 - 280 ppm range given by Wikipedia for the level just before the industrial emissions began).

Because heavy - flavor production is dominated by gluon - gluon interactions at $ \ sqrt -LCB- s -RCB- = 200 $ GeV, these measurements offer a unique opportunity... ▽ More The cross section and transverse single - spin asymmetries of $ \ mu ^ -LCB-- -RCB- $ and $ \ mu ^ -LCB- + -RCB- $ from open heavy - flavor decays in polarized $ p $ + $ p $ collisions at $ \ sqrt -LCB- s -RCB- = 200 $ GeV were measured by the PHENIX experiment during 2012 at the Relativistic Heavy Ion Collider.

Just a minor statistical note without knowing how these numbers relate to reality: If you compare two measurements with standard deviation of 6, the standard deviation of the difference is not 12 but sqrt (2) * 6 = 8.5.

Wind speed is a useful measure of storm strength (cat 1 - 5, (E) F scale...), and pressure drop also (~ wind speed * distance scale ~ speed * sqrt (area) if not too elongated)-- although (I think) potential energy ~ area * (change in p ^ 2) and kinetic energy ~ area * speed ^ 2 (assumes same vertical extent, density...)

The transfered power is the same at each layer so we can write T2 at some layer 2 at R2 in terms of T1 at layer 1 at R1 as T2 = T1sqrt (R1 / R2) so it is really a 1 / sqrt (R) dependence rather that sqrt (R) as I originally stated.

T ~ 1 / sqrt (R) falls out of that.

Dyn) and one important concept is that the effective heat capacity of the ocean is proportional to the sqrt (Kv).

In the first place, almost all science can be considered to be «mathematical modeling» in one sense or another; if you were to attempt to predict how long it takes a baseball dropped from six feet to reach the floor, you would probably use Newton's laws and make your prediction based on the equation t = sqrt (2s / g) where s is the distance fallen and g is the local acceleration of gravity.

For a selected metrics of «yearly average», the result comes from 730 samples, such that the error in this average is about SQRT (730) = 27 smaller than the 1C individual error, or about 0.03 C. Therefore, for a given time period the slope of linear fit is a very precise characteristic at that particular location.

Total Forecast Standard Errors from this calculation (including both the coefficient uncertainty and the observation errors) are 2.1 * sqrt (1 + 1/13) = 2.2 dC at the average of the calibration TEX86 values.

If done correctly, the «leave - one - out» procedure will give the coefficient forecast standard error (2.1 * sqrt (1/13) = 0.58 dC at the mean of the TEX86 values), rather than the relevant total forecast standard error, but they have somehow come up with something even smaller than that.

Increasing the number of measurements in this case, does decrease the random component of the instrumental error by 1 / SQRT (n) where n is the the number of observations.

Again, because of the uniform nature of the temperature, a series of n observations taken at various points around the room will have an error that is 1 / SQRT (n) smaller than a single measurement alone.

When the inter-methodological (+ / --RRB- 2 C noted by Bemis, et al., is added as the rms to the average (+ / --RRB- 1.25 C measurement error from the work of McCrae 1950 and Bemis 1998, the combined 1 - sigma error in determined T = (+ / --RRB- sqrt (1.25 ^ 2 +2 ^ 2) = (+ / --RRB- 2.4 C.

The point that systematic error propagates as sqrt -LSB-(sum - over-scatter) ^ 2 / (N - 1)-RSB--- where N is the number of measurements — follows from the fact that a degree of freedom is lost through the use of the mean measurement in calculating the systematic scatter.

That is, he claimed that the 11 - year sunspot cycle plus its secular and millennial variation, which I was modeling very precisely with my model, could be produced also by this kind of formula f (t) = A * cos (2p * (t - T1) / p1) + B * cos (2p * (t - T2) / p2) Some variation on that formula does a good job, e.g. the one I used in my toy - example: «Sunspot Number» = SQRT (ABS (k * cos (π / p1 * t) + cos (π / p2 * t)-RRB--RRB-

That simplifies the discussion as then we can estimate \ (2 \ sigma \ approx 2 -LCB- \ sqrt -LCB- V / (N - 1)-RCB--RCB- \), where N is given by the number of uncorrelated Atlantic ocean areas between 20 ° N and 20 ° S. With a correlation length of ∼ 10 — 15 ° we obtain a rough estimate of N ≈ 12 for the tropical Atlantic sector.

For very large s this simplifies to k = y» / (sqrt (s) * y» ³), which tends to zero as s tends to infinity, while for very small s it becomes k = sy», which again tends to zero but this time as s tends to zero instead of infinity.

The CAGR of any two consecutive periods of the same duration is their geometric mean, which in this case is sqrt -LRB-.9859 * 1.0595) = 1.02204 or 2.20 %.

Adding unit variance white noise to a series that has already been standardized to unit variance may explain why Steve was finding correlations of precisely sqrt (2) with archived versions.

So we can not assume the error will decrease by the sqrt (number of months it is applied).

If so, then the standard deviation of the measurement error is = 1 / sqrt (12) = 0.289.

Instead, Trmse (Month i) = StDev (30 Daily Min + 30 Daily Max) / sqrt (60) If we assume a flat constant avg temp of 10 deg C for the month, coming from thirty 5 deg C min readings and 15 deg C max readings.

With 300 days a year and 1000 stations that gives an error of 0.1 °C / sqrt (300 * 1000) = 0.0002 °C.

but the rmse of the anomaly = sqrt (0.645 ^ 2 + 0.118 ^ 2) TArmse (month, 30 year base) = 0.656 deg C. or + / - 1.079 deg C at 90 % confidence.

As the internal measurement errors and the external inter-equational uncertainties stem from independent sets of systematic errors, they combine as the rms: (+ / --RRB- sqrt [measurement error) ^ 2 + (inter-equational spread) ^ 2] = sqrt -LSB-(1.25) ^ 2 + (1.75) ^ 2] = (+ / --RRB- 2.2 C.

q is sqrt (sum (d [i] ** 2) / n) where d [i] is the difference between the two series at time i; the sum is over the common period; n is the number of elements (months) in the common period.

But all that did not send Maxwell's equations to the dustbin of history; it is enshrined forever, in the velocity of light as c = 1 / sqrt (mu - naught x Epsilon - naught).

The number of free home - team skins awarded per match determined as follows: 2 * SQRT (N), where «N» equals the number of eligible participants (value $ 0).