Sentences with phrase «standard error of»

The Kurtosis was also significant at 5.12 with a standard error of 0.60.

Differences in the strengths of correlations between boys and girls were tested by converting the two correlations to z - scores, dividing the difference between the z - scores with the standard error of difference between the two correlations, and then testing the significance of the z value of the difference score.

Skewness was significant at − 2.06 with a standard error of 0.31.

All error bars represent standard error of the mean (SEM).

An intraclass correlation (2,1), repeated measures ANOVA, and standard error of measurement were used to determine the results of this study.

Figure 2.8 shows a smoothed optimally averaged annual global time - series with estimates of uncertainty at ± twice the standard error of the smoothed (near decadal) estimate.

It is inherent in the data set, being derived by an equation from the standard error of estimate.

Symbols are annual means, with error bars showing the standard error of the means.

I then loaded that data into R and calculated the trend slope, standard error of that slope, the acf and finally the adjusted trend slope CIs using the Nychka procedure from Santer et al. (2008) with the AR1 factor.

I just took the opportunity to reflect that the bigger problem in the mean standard error of the TAVE, TAVErmse, also derives from the Mins and Maxes.

So 0.3 is a reasonable lower limit to the standard error of uncertainty, provided that 34.55 degrees is reliably recorded as 35 and not 34.

Now, whether the standard error of the reading is 0.3 or 0.4, it will likely be a smaller contribution to the total mean standard error than the differences between tmins and tmaxes and even tmaxs across a time period.

If the TAVE for a month is absolutely constant, but the TMin and TMax are separated by 10 deg C = 18 deg F, then the mean standard error of the TAVE should be about 0.6 deg C or about 1.0 deg F.

With one long 115 year record, the mean standard error of the slope is well constrained, even with 0.5 deg uncertainty in any given month.

I'm talking about mean standard error of DAILY TAVE at each STATION.

As long as you include the mean standard error of the adjustments, I have no problem with that.

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.

BEST has released gridded data, and I'm more interested in it at the moment On that subject, is there any uncertainty information, such as mean standard error of the estimates associated with the grid points?

I think if we do the math and re-associate the terms, you end up back with the mean standard error from the average of 62 terms (31 maxes and 31 mins separated by something like 18 deg F) each with an uncertainty of 0.3 to 0.4 deg C. You'll wind up with a mean standard error of 0.6 to 0.7 deg C of the monthly average.

If you want to apply a different TOBS (morning), a TOBS (Afternoon), a TOBS (Noon), and a TOBS (late evening), I have no theoretical objection ---- Provided the mean standard error of the adjustment is applied and another error source is added to account for the probabilistic uncertainty that the wrong adjustment is used.

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).

The convergence statistics for the various cosine fits show that the asymptotic standard error of the fitted parameters are generally very good, though notably higher for HadSST3 than for the simply adjusted ICOADS dataset (see appendix).

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 standard deviation or some other appropriate measure of ensemble spread.

With nonstationary statistics the standard error of the fit over past years is not a good measure of the uncertainty in the prediction.

Here the range is twice the standard error of the proportion.

The mean and standard error of the measurement (and hence its confidence interval) have noting whatsoever to do with the mean and standard error of the proportion of climate scientists holding an opinion.

In English we could say «We are 95 confident that the true value of half of the observed increase in global surface temperature is in the range...» The range is the mean of that measurement plus or minus twice the standard error of the measurement.

The standard error of the difference is 0.224 Deg C, so that the difference is significantly non-zero at the 10 % level (t = 1.84).

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.

Note that the individual errors that you point out are already included in the calculation of the standard error of the average (usually called the SEM, the standard error of the mean).

They then regress ALST on TEX86 as shown in Figure S4, for which the regression residuals have a standard error of 2.1 dC, and then use this line to «forecast» temperature from past values of TEX86 at various depths down the core (not tabulated anywhere, unfortunately).

The uncertainties in the Table S1 are the 90 % spread in the ensemble, not the standard error of the mean.

Using the Hadley CRU time series, I find the slope to be 0.00507173 with a standard error of 0.000266163, which gives it a t - statistic of 19.05490182.

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.

Time spent looking towards content - mismatched DDS and ADS during each phase, where error bars represent 1 standard error of the mean.

Time spent looking towards content - matched DDS and ADS where error bars represent 1 standard error of the mean.

Using LINEST, you can calculate the ratio of the square of the standard error of y at year 20 and the square of the standard error of y at year 10 by squaring the relevant sey terms or, more easily, taking the ratio of ssrid terms.

Short term estimates of growth using curriculum - based measurement of oral reading fluency: Estimates of standard error of the slope to construct confidence intervals

The standard error of measurement (an indicator for measurement precision) shrinks as the test proceeds.

Because some amount of error is expected with any measurement, statisticians developed the term Standard Error of Measurement (SEM) to account for small amounts of error in every result.

If it is broken down into groups that are too small (e.g., individual classes) the standard error of measurement tends to become so great that although the data remains «valid» it is no longer «reliable.»

Having a Standard Error of Measurement associated with a test score can help a teacher determine the level of confidence in that score.

Given its standard error of.96, its actual ranking could fall anywhere between first and sixth.

An important concept in assessment is the standard error of measurement (SEM).

Estimates of the standard error of measurement for curriculum - based measures of oral reading fluency.

The approach includes an adjustment for the standard error of growth to ensure students will only fail to meet their growth goal if their growth is statistically different from the growth norm.

EA creates a «standard error of growth band» equivalent to 1.6 standard errors of growth.

NWEA MAP produces a metric called the «standard error of measurement» (SEM) for every student test event based on many factors.

If a single student were to take the same test repeatedly (with no new learning taking place between testings and no memory of question effects), the standard deviation of his / her repeated test scores is denoted as the standard error of measure.

The variation between these two scores would be called the standard error of measurement.