Sentences with word «sqrt»

But I'm stuck here: in tsvdRegFn: D = sqrt (diag (dat $ C)-RRB- As far as I can see, dat $ C is a vector of logicals, which has furthermore just been set to NA.

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).

Multiplying the standard deviation by sqrt (5) provides a crude adjustment for the autocorrelation, bringing the standard deviation to 0.068 W / m ².

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.

But in this instance, I'm not averaging the same variable multiple times, just adding two different random variables — no division by N, and no decrease in variance as sqrt (N).

Maybe the GPU doesn't like high sqrt count in the loop?

$ 1000 deductible, coverage is about $ 100 / sqrt for rebuilt.

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 that sense \ (2 \ sigma \ approx 2 -LCB- \ sqrt -LCB- V / (N - 1)-RCB--RCB- \) with N ≈ 12 provides a conservative estimate.

Girls = Time x Money Time = Money Therefore: Girls = Money x Money = (money) ^ 2 Money = sqrt (evil) Girls = (sqrt (evil) ^ 2) Girls = evil.

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.

Keywords: Irrational numbers, proof that sqrt (2) is irrational, rational numbers, decimal expansions, repeating decimals, non-repeating decimals, real numbers, sqrt (2), e, pi, exp (1), terminating decimal expansions, non-terminating decimal expansions, Pythagorean, Pythagoras, proofs.

Provides the proof that sqrt (2) is irrational.

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

The distribution for the means will have error bars bigger by sqrt (2) = 1.4, so that the means differ by 3/1.4 = 2.1 sigma, clearly a statistically significant discrepancy.

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.

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

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

The difference between sqrt (0.65 cm ^ 2 / s) and sqrt (2.3 cm ^ 2 / s) is a more appropriate comparison.

Note: wording in square brackets in the pre-penultimate paragraph inserted 14 March 2016 AM; I wrote the Update late at night and overlooked that Gavin Schmidt's explanation would only account for the discrepancy if the divisor for the standard error of the mean of n runs were sqrt (n - 1), rather than the correct value of sqrt (n).

I realized there was something seriously wrong with the universe when I learned about i (the sqrt of -1), and then found that it had real - world applications.

The appropriate correction, which follows from the form of the Chi - square distribution PDF, is division of the F - distribution PDF by sqrt (delta.r2).

And neither it should, because it's a theorem of statistics, applicable to many distributions but particularly normal distributions, that the average of n random variables each of standard deviation d has standard deviation d / sqrt (n).