Sentences with phrase «average number of errors»

I'm not sure but I think that post may have been a joke given the name and the larger than average number of errors... not sure, but that was my guess.

Not exact matches

«Gifted, determined, ambitious professionals have come into investment management in such large numbers during the past 30 years that it may no longer be feasible for any of them to profit from the errors of all the other sufficiently often and by sufficient magnitude to beat market averages.»

# 2) Error Coherence: When you combine a large number of those measurements to get an average answer, do all the errors «pile up» or do they tend to «cancel each other out» instead?

«The more decisions you make, the more your portfolio trends towards average and the higher number of errors creep into your decision making,» says Hugo Lavallée, manager of Fidelity's Canadian Opportunities Fund.

Of course the errors are even more significant when one inflated figure is multiplied by another — as when Lepczyk et al. [6] multiply the average number of prey items returned by the average number of outdoor cats per owneOf course the errors are even more significant when one inflated figure is multiplied by another — as when Lepczyk et al. [6] multiply the average number of prey items returned by the average number of outdoor cats per owneof prey items returned by the average number of outdoor cats per owneof outdoor cats per owner.

A similar error is made when the authors use an average to describe the number of outdoor cats owned by each landowner.

There is a quantitative effect of this error, both on global average calculations up to the 1970's and on the uncertainty of that number.

There are three parameters, the error - bound s of each sample, the desired error - bound of the average a in each grid, and the number n of samples there for the year.

The mean average of all the linear trends is slightly positive (+1.0 mm / yr, with a standard error of 0.1 mm / yr), but there are a large number of gauges with substantially lower or higher trends.

As you can see, we can't trust any individual data point to better than + / - 5 degs yet by taking the average of 100 data points the error drops by an order of magnitude to (The error falls as the square root of the number of data points) to give an accuracy of a fraction of a degree.

Taking decadal averages based on round numbers of calendar years is arbitrary and error prone.

It can be shown that CO2 levels over the period 1982 — 2007 at all stations (up to a modern number of 9 stations) averaged 0.480 ± 0.065 % below the global average (errors at the ± one standard deviation level).

Therefore, by the law of large numbers, these errors will mostly cancel out as the number of observations gets large (in other words the average of the errors will be very close to zero).

As well, since different numbers of trees contribute at different ages, both the raw averages and the standardized averages (by subtracting the number one and then dividing by the standard error) were calculated.

I found some reference to calculating error margin based on measurement error of 0.1 F, So the.141 is for subtracting Tmx (mn) from Tmx (mn), and.316 is for averaging Tmn and Tmx, count it the number of samples.

The average numbers of male and female births (± 1 standard error) in each month of the year, averaged over 1980 — 2009 (n = 30).

Knutti et al. (2010a) investigated the behaviour of the state - of - the - art climate model ensemble created by the World Climate Research Programme's Coupled Model Intercomparison Project Phase 3 (CMIP3, Meehl et al. 2007), and found that the truth centred paradigm is incompatible with the CMIP3 ensemble: the ensemble mean does not converge to observations as the number of ensemble members increases, and the pairwise correlation of model errors (the differences between model and observation) between two ensemble members does not average to zero (Knutti et al. 2010a; Annan and Hargreaves 2010; hereafter AH10).

Adding the relevant years» total uncertainty estimates for the HadCRUT4 21 - year smoothed decadal data (estimated 5 — 95 % ranges 0.17 °C and 0.126 °C), and very generously assuming the variance uncertainty scales inversely with the number of years averaged, gives an error standard deviation for the change in decadal temperature of 0.08 °C (all uncertainty errors are assumed to be normally distributed, and independent except where otherwise stated).