Sentences with phrase «= sqrt»

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.

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.

NumXPoints = 2000; cycles = 25; out = zeros (NumXPoints, 1); randn («state», sum (100 * clock)-RRB- rand («state», sum (100 * clock)-RRB- V = 2 * 1 / cycles; sV = sqrt (V); r = randn (1, cycles) * sV; rphase = rand (1, cycles).

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-

Let's illustrate by an example: I have a system whose dynamics is given by a continuous solution F (t) = Sqrt (at).

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.

In the simplest case, if the mean increases as N then the standard deviation increases by N / sqrt (N) = sqrt (N).

Not exact matches

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.

Because heavy - flavor production is dominated by gluon - gluon interactions at $ \ sqrt -LCB- s -RCB- = 200 $ GeV, these measurements offer a unique opportunity to obtain information on the trigluon correlation functions.

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.

From their formula the Douglass et al 2 sigma uncertainty would be 2 * 0.113 / sqrt (17) = 0.06 °C / dec..

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.

The difference is the sqrt (n) term (= 2.24).

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.

It is closer to the 0.5 / sqrt (25) = 0.1 cm uncertainty that one would expect if there were random errors in the measurements.

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.

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.

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 %.

Let me add to my previous comment that obviously — though not obviously enough for me to previously notice --[sqrt (n)- sqrt (n - 1)-RSB- = 1 / [sqrt (n) + sqrt (n - 1)-RSB-

res.mean = by (yamal, list (yamal $ age, yamal $ life), function (x) mean (x $ resids)-RRB- res.scmean = by (yamal, list (yamal $ age, yamal $ life), function (x) sqrt (nrow (x)-RRB- * (mean (x $ resids)-1) / sd (x $ resids)-RRB-

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.

In this case Trmse (30 year, month I) = 0.645 / sqrt (30) = 0.118 deg.

But that gave an error too: Error in svd (dat $ X, nu = n.eof, nv = n.eof): infinite or missing values in «x» In addition: Warning message: In sqrt (diag (dat $ X)-RRB-: NaNs produced

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).