Sentences with phrase «square root of»

These effect sizes are the ratio of the difference in estimated time trends divided by the square root of the estimated random time effect variance.

Specifically, we divided the within pair covariance by the square root of the product of each twin variance [correlation = covariance (twin1 - twin2) / sq (variancetwin1 * variancetwin2)-RSB-.

Equivalent household income was calculated as the total household income divided by the square root of the number of household members; these scores were then divided into quartiles.

λ1 considers that all of an item variance is error and that only the inter-item covariances reflect true variability; λ2 is a modification of λ1 that considers the square root of the sums of squares of the off diagonal elements; λ3 is equivalent with Cronbach's alpha; λ4 is the greatest split - half reliability; λ5 is a modification of λ1 that replaces the diagonal values with twice the square root of the maximum (across items) of the sums of squared inter-item covariances; λ6 considers the variance of errors (Revelle and Zinbarg 2009, pp. 147 — 149)

The errors of manifest variables were set as the calculation of one minus the measurement's reliability, and the path between the manifest variables and latent variables were set by using the square root of the measures» alpha reliabilities.

The square root of AVE of the variables is between 0.724 ~ 0.829, and the correlation coefficients between latent variables which have significant relationship is between 0.009 ~ 0.609, and the AVE square root of all variables is greater than the correlation coefficient of the latent variables, which means the scales have a high discriminant validity.

The time - domain analysis (the standard deviation of all RR intervals [SDNN] and the square root of the mean squared differences of successive normal sinus intervals [RMSSD]-RRB-, the frequency - domain analysis (very low frequency, low frequency [LF], high frequency [HF], and total power [TP]-RRB-, and a non-linear complexity measure the approximate entropy were computed.

Effect size for the adjusted mean difference between each treatment was calculated by dividing the mean difference in test score by the square root of the within mean square error for the adjusted post-test score.

«Simulations show it is enough to store paths to about the square root of the number of nodes in the network (provided nodes are reasonably well connected).

You'll also still be able to ask Assistant for the square root of Pi, what sound a Zebra makes, or to set a timer - all without lifting a finger.

Diagonal resolution = square root of -LSB-(width x width) + (length x length)-RSB- Using the example of a 4 - inch display with a WVGA resolution, your equation should look like the following: Diagonal resolution = square root of -LSB-(800 x 800) + (480 x 480)-RSB- Diagonal resolution = square root of [640,000 + 230,400] = square root of 870,400 Diagonal resolution = 933 pixels

That is to say that while I may have a hard time at finding the square root of a large number, I can almost instantly identify the most important area to look at on a heat map.

«Like you know square root of eff - a about detecting,» Mitman called back.

I don't know square root of whatever about any money.»)

If the drunk starts out at the light pole and at every time step takes a fixed distance step in a random direction, the expected value of his distance from the light pole increases as the square root of time.

If you treat the volume change as purely a change in potential energy, the kinetic energy (as wind) that results would track with the square root of the volume change, which is inversely related to the absolute temperature.

For example, the MSE calculated for the validation period provides a useful measure of the accuracy of the reconstruction; the square root of MSE can be used as an estimate of the reconstruction standard error.

I wonder how perception would be affected by making the radial distance proportional to the square root of volume (or whatever other quantity is being graphed on such a plot) so that the area on the graph is proportional to volume?

3 K warming of 15 m depth of rock on land, assuming density of 2600 kg / m ^ 3 and specific heat of 733 J / (kg * K)(p. 85; note (also from p. 85)-- for typical soil thermal diffusivity, penetration depth for an annual cycle is ~ 1.5 m; penetration depth is proportional to the square root of time.

The sample mean converges toward the actual mean with the error decreasing as the inverse of the square root of the number of samples.

For independent random errors with the same mean (i.e., no drift), the accuracy increases with the square root of the number of measurements.

As an example, when averaging, signal - to - noise ratio increases as the square root of the number of samples that are being averaged together because white noise has an average of zero.

The problem with this assumption is that, without any external driver, due to random inputs and outputs, the atmospheric concentration would then effectively vary as a random walk, with the dispersion increasing as the square root of time.

The confidence interval of an average does down with the square root of the number of measurements.

This error will decline with the square root of the number of instruments (approximately).

Our results show that technological progress is forecastable, with the square root of the logarithmic error growing linearly with the forecasting horizon at a typical rate of 2.5 % per year.

The variance reduction ratio compared to MLO is on the order of 23,000:1, the square root of the number of minutes in a millennium.

Here's a third: the speed of water that flows out is a function of the square root of the depth, so the water level will rise to a higher level and not overflow.

As long as Cook wants to restrict his analysis to first order effects, that is, eliminating solubility and the subtleties of climate, including assessing cause and effect, his accuracy will increase proportional to the square root of the time interval.

In that case, if we have 10,000 observations in a year, the uncertainty on the annual average would be roughly 10 divided by the square root of 10,000, which is 0.1 degC.

In the absence of serial correlation the standard deviation of this trend estimate is the standard deviation of the period - to - period changes in temperature divided by the square root of the number of periods in the interval.

One could argue that real SST measurements aren't quite so well - behaved, but it is possible to show (see Figure 11 of the HadSST2 paper, Rayner et al. 2006 for details) that the standard deviation of grid box averages falls roughly as one over the square root of the number of contributing observations and that the standard deviation for gridbox averages based on a single observation is a lot less than 10 degrees.

Averaging more years reduces the standard deviation by the square root of the number of years, so by the time you average ten years the standard deviation for a decade is down to 0.03 degrees.

Nobody did, until Gauss systematized this (although Kepler was aware that the precision of measurements was proportional to the inverse square root of the number of measurements)

Try dividing your 1st PC by the square root of 5, see whether it matches mine.]

In statistics, the uncertainty in a statistic estimated from a set of samples commonly varies in inverse proportion to the square root of the number of observations.

This is rather simpler, and follows the normal law of varying in inverse proportion to the square root of the number of data.

Well, per Oke (see Steve M's reference), wind affects UHI in proportion to the inverse square root of the windspeed.

(A-3)-RSB-, is often called the law of propagation of uncertainty and in common parlance the «root - sum - of - squares» (square root of the sum - of - the squares) or «RSS» method of combining uncertainty components estimated as standard deviations.

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.

If the errors are uncorrelated and of the same order of magnitude they will cancel to a significant degree, and the accuracy of the final result will improve proportionally to the inverse square root of the number of observations.

Long wavelength water waves, in deep water, have a (phase) velocity that increases like the square root of the wavelength so can match earth rotation.

«[it is] erroneous to suggest that the estimate of the global average ocean temperature is given by the instrument accuracy divided by the square root of the number of observations (as you would if the observations were of the same quantity).»

It is therefore erroneous to suggest that the estimate of the global average ocean temperature is given by the instrument accuracy divided by the square root of the number of observations (as you would if the observations were of the same quantity):

If you would like your modeling result to be taken seriously as a guide to future planning (I don't mean to presume to know your motives), then keep track of the squared prediction error, the sum of the squared prediction errors (CUSUM), and the square root of the mean prediction error (RMSE) over the next 20 years.

For me it's enough to tell that when the final outcome has the nature of an average of all annual readings we get the first lower limit by dividing the accuracy of individual readings by the square root of the number of all readings.

The relationship is that the reduction in the error is proportional to the square root of the number of measurements.

This seems to an engineer as the square root of bugger all!

Looking at his AGU poster from last year, we see that his calculated uncertainty grows with the square root of time.

However, it turns out that the standard error is inversely proportional to the square root of the number of observations.