Sentences with phrase «average correlation using»

Pearson and ICC between AQoL - 8D and other MAU instruments resulted in above average coefficients, with the former technique and the highest average correlation using the ICC; however, differences were generally small.

Not exact matches

Correlation uses a 252 trading day moving average.

Using calculations and data mining, the Spanish astronomers have found that the nodes of the 28 ETNOs analysed (and the 24 extreme Centaurs with average distances from the Sun of more than 150 AU) are clustered in certain ranges of distances from the Sun; furthermore, they have found a correlation, where none should exist, between the positions of the nodes and the inclination, one of the parameters which defines the orientation of the orbits of these icy objects in space.

On average we found only 54 genes per tissue (0.2 %), which showed significant correlation of gene expression with PMI (FDR < 1 %)(Fig. 3a, Supplementary Table 6), compared to 6919 genes per tissue (39.3 %), if using the same model without covariates.

In a second step, the resulting clusters, represented by mean profiles, were clustered using average linkage hierarchical clustering with Pearson correlation distance measure, and visualized in a heatmap representation [52].

They consider the use and calculation of 3 period moving averages; the influences acting upon sales forecasts; extrapolation; correlation analysis techniques; scatter graphs; an evaluation of time - series analysis methods; the line of best fit; qualitative forecasting methods (Delphi Technique; Brainstorming and Intuition) and an evaluation of qualitative forecasting.

Their study identifies 21 leadership «responsibilities» and calculates an average correlation between each responsibility and whatever measures of student achievement were used in the original studies.

Three analyses were conducted with the cross-sectional data using teachers» WSS ratings of student achievement and students» WJ - R standard scores: a) correlations comparing the students» standard scores on the various subtests of the WJ - R and the WSS checklist and summary report ratings of student achievement within the corresponding WSS domains, b) four - step hierarchical regressions examining the different factors that accounted for the variance in students» spring WJ - R scores, and c) Receiver - Operating - Characteristic (ROC) curves, which make possible a determination of whether a random pair of average and below - average scores on the WJ - R would be ranked correctly in terms of performance on the WSS.

Correlations were lower on average in 2017 compared to 2016, and this is a commonly used justification for a better stock pickers» market.

One interesting method to use is the average of the correlation of each stock's RSI when RSI < some oversold threshold to the equal weight or index RSI when it is < the same threshold.

Just looking at the average correlation or average downside correlation of the typical Russell 1000 stock to the S&P 500 using a 252 - day lookback should clearly demonstrate a positive trend over time.

According to Evensky: «The MPT model alone will not necessarily work in bear markets, or at least not using historical averages alone as inputs without other adjustments to forecast the return, volatility and especially correlation.»

Anyway, the models should have a high correlation perhaps 0.9 or 0.95 for what the IPCC is using them for — and they should get the average temperature of the earth right.

It showed, if I remember correctly, how a pretty good correlation between calculated and actual global average temperatures could be obtained for the last century using the NASA graphs of various forcings, here: http://data.giss.nasa.gov/modelforce/RadF.gif

Rhetorical question: Even if the rings show some correlation, (which I doubt) which trees would Mann use to show the average annual value for the planet?

Using these three components alone shows a very strong Pearson correlation of.84 with the 31 - year running average of G7.

Exactly, but using good numbers not a «hotchpotch assembly» for which it is claimed to be global temperature (there is no such thing, there is global energy content, but that is totally different story) So calculate correlation CET - GT from 1880 using 5 year bin averaging http://www.vukcevic.talktalk.net//CETGNH.htm P.S. your statement on natural variability on decadal scale is grossly misleading, you got about 130 years of good records so you need to look at multi-decadal picture.

Linear statistics were used first: area - averaged and Australia - wide spatial correlations of STR intensity and position with precipitation in south - west eastern Australia reveal that STR intensity has a much stronger and more widespread relationship with precipitation in both seasons.

I've added a small essay from 2 years ago questioning whether correlation coefficients can be used on averaged data (yearly, monthly, weekly, daily).

Within the paleoclimate context, where the expectation is that each proxy is weakly correlated to the northern hemisphere mean (for two reasons: proxies generally have a weak correlation with local climate, which in turn is weakly correlated with a hemispheric average) the LASSO as used by MW2010 is simply not an appropriate tool.

As the results show none of the average correlations for the various monthly series are even close to being statistically different than the months used in the Gergis paper and namely the series in the table for Sep - Feb.

For the record, in the case of this «divergence», after dropping that post 1960 portion, the comparison between the reconstruction and the temperature record was done using decadal «smoothing» (basically weighted moving averages) of both series correlated on an annual basis for the 80 year period 1880 to 1960 so that the reported correlation was extremely exaggerated and not interpretable as a simple correlation might be.

With a PC link, the data can be used for all sorts of projects, from simple averaging ones to looking at correlations between different measurements such as wind direction and temperature.

Population average models were used to account for the longitudinal study design and correlation of repeated measurements, and an interaction term between maternal education (our socioeconomic measure) and age was included in order to examine whether differences in health inequalities by age were statistically significant.

Correlations were then transformed using Fisher's Z for all subsequent analyses, as recommended when averaging correlation coefficients (Silver and Dunlap 1987).