Sentences with phrase «large standard error»

Not exact matches

However, for Ochola 2013, outcome one of Elliott - Rudder 2014, arm two of Yotebieng 2015, adjusting for clustering based on the summary statistic made the standard error larger and the width of the confidence interval increased which resulted in a design of < 1.

That is, we found some evidence that small studies (i.e., those with higher standard errors, located to the right of the figure), compared with larger studies, reported larger mean differences in systolic blood pressure between infant feeding groups.

The Boeing 767 is slightly larger than the Boeing 707, which at 168 tons was the standard for large commercial aircraft then flying, but the difference is well within the margin of error.

As it turns out, the standard errors are larger, not smaller, when estimating statistical models that include all students but do not control for baseline test scores.

In four of the countries, Australia, Hong Kong, Scotland, and the United States, the standard error of the estimated effects of class size was extremely large, indicating that little confidence should be placed in the results.

This is greater than the two - point gain we found in Iceland and Greece, but it is within the standard error of these estimates, suggesting that the actual effect of reducing class size in Iceland and Greece could be as large as Krueger found in the United States.

Environmental Topics include: Earth Facts Population Facts Animal Conservation Facts Recycling Facts Human Consumption Facts Media Facts My own statistics Maths Topics include: Big Numbers Standard Form Estimating Fractions Percentages Rounding numbers Multiplying large numbers Adding and Subtracting large numbers Error intervals (upper and lower bounds) Circumference Negative Numbers Speed The document is 12 pages in total.

Furthermore, they say, a test's standard error of measurement may be large enough to throw into question the use of the results.

One last note: the standard deviation of the error term was 6.3383 %, which helps show that in the short run, the volatility of implied volatility is a larger effect than mean - reversion.

However, the standard deviation of the error is relatively large, i.e. about 0.6 %.

For example we found that the standard error of the caudal skull base length (AB) was almost as large as the actual mean difference hence it was a non-significant.

The standard errors of Pg, R and Pg / R expressed as a percentage of the mean are lower than 3 % but are comparatively larger for E, the excess production (6 to 78 %).

If you have errors in a measurement, it is revealed by a larger standard deviation in the results.

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.

Well, this is the same Federal government that can not spare a dime (or more than 0.25 FTE) for bringing up its temperature measurement sites (whose output help drive this whole bill) to its own standards, allowing errors and biases in the measurements 2 - 3 times larger than the historic warming signal we are trying to measure.

apart from the ludicrous «confidence intervals» which show for most if not all predictions that the standard errors are larger than the predicted effects.

I note what you say about deep ocean heat storage, although the amounts reported by Purkey & Johnson are not huge, have large error bounds and will to some extent already be accounted for in the standard 0 - 3000m ocean heat storage datasets.

As youpoint out, the standard errors of these parameters will be extremely large and because this is the period used later for the calibration of the chronology to climate, the results over the entire chronology will suffer.

The very high significance levels of model — observation discrepancies in LT and MT trends that were obtained in some studies (e.g., Douglass et al., 2008; McKitrick et al., 2010) thus arose to a substantial degree from using the standard error of the model ensemble mean as a measure of uncertainty, instead of the ensemble standard deviation or some other appropriate measure for uncertainty arising from internal climate variability... Nevertheless, almost all model ensemble members show a warming trend in both LT and MT larger than observational estimates (McKitrick et al., 2010; Po - Chedley and Fu, 2012; Santer et al., 2013).

is further why the uncertainty in the average temperature, the mean standard error of the TAVE for a month is large, on the order of 0.5 to 0.8 deg C.

For example, when fitting a trendline with random walk errors (H = 1), the intercept is unidentified and hence has an effective sample size of 0, while the slope is identified with a finite (though perhaps large) standard error.

Its standard error is 0.11 degC / decade, so the real trend rate could be as high as 0.36 deg.C / decade, quite a bit larger than the average rate since 1975.

There are many sources of waste and error: pressures on young associates to bill large numbers of hours; billing time in inappropriate increments (the industry standard is 0.1 - hour increments, but some firms don't follow that); insufficient training; and simple carelessness, like not entering time until the end of the week and then having to piece things together from memory.

Although SEM is traditionally utilized with large samples, bootstrap analyses allow model testing with small samples by utilizing the actual data to estimate standard error (Shrout & Bolger, 2002).

In this respect, Maas and Hox (2005) found that level 2 sample sizes exceeding 30 (i.e., 136 in our study) are sufficiently large to produce unbiased estimates and accurate estimations of standard errors and fixed effects.