Sentences with phrase «as measurement errors»

We can think of this as measurement error because unlike many proxies, we care about the specific estimates of these variables.

Standard error involves both natural variability (including that not well understood because it operates on long time scales, and therefore has not been observed during the period of modern technology) as well as measurement error (or error / uncertainty in the proxies).

I think in this respect the Keenan paper must make some fault as the calibration curve uncertainty must be of the same order in size there as the measurement error.

And in climate data there is plenty of non-linear «noise» as well as measurement error.

Not exact matches

These measurements favor ad platforms who, whether through error of commission or omission, have allowed brands to squander as much as 20 % of their digital advertising budgets on fraudulent impressions.

Successful baking means eliminating as much potential for error as possible, and that means making sure your measurements are exact.

«Think of it as a signal - to - noise ratio — there is an inherent level of noise (technical error of measurement, day - to - day fluctuations), and only signals greater than this noise level will be apparent.»

By aiming at the atoms from opposite directions simultaneously, the laser arrangement cancels a major source of measurement error — the Doppler shift, or the change in the atoms» apparent resonant frequency as they interact and move with the laser light.

But as any undergraduate studying computing discovers, problems arise from converting analogue measurements to digital values: there is always a «quantisation» error.

Naturally this article fails to mention that since the hydrosphere is 271 times as massive as the atmosphere, if oceans are absorbing the heat they are likely to moderate AGW into a nonproblem, as the average ocean temperature has only changed by.1 degrees in 50 years, an amount that is probably smaller than measurement error.

Radio telescopes, including major facilities of the National Science Foundation's National Radio Astronomy Observatory, have provided data needed to measure the winds encountered by the Huygens spacecraft as it descended through the atmosphere of Saturn's moon Titan last month — measurements feared lost because of a communication error between Huygens and its mother ship Cassini.

They revised an earlier HIPPARCOS parallax estimate of l02 ly made in the 1990s that had an error margin (Plx = 31.92 + / - 0.51 mas) just big enough to suggest that the star may actually lie about 100 ly away, in agreement with Earth - based parallax measurements computed before 1978 as reported by Robert Burnham, Jr. (1931 - 93).

And by increasing the dynamic stiffness we can nearly eliminate the effect of electrostatic forces between the sample and cantilever body, which are major sources of error in today's PFM measurements, as mentioned.»

Measurement error in the assessment of dietary variables is likely to be nondifferential as the dietary factors were collected long before death events occurred.

While an element of the unexplained variability will likely have arisen though measurement error, it is more likely that the variation occurred primarily through variation between performances within individuals, as snatch, clean and jerk, and total 1RM varies by around 2.3 — 2.7 % in elite Olympic weightlifters (McGuigan & Kane, 2004), although test - re-test reliability of the 1RM power clean is nearly perfect in adolescent male athletes, with ICC = 0.98, a standard error of measurement (SEM) of 2.9 kg and a smallest worthwhile change (SWC) of 8.0 kg (Faigenbaum et al. 2012).

Please permit 1 - 3 cm (zero.Four - 1.2 inches) error as a result of handbook measurement.

For example, if a student scores an 84 on a test that has a standard error of measurement of three, then his or her performance level could be as low as 81 or as high as 87.

We can think of value - added estimates as measuring three components: (1) true teaching effectiveness that persists across years; (2) true effectiveness that varies from year to year; and (3) measurement error.

Reliability is typically captured using reliability coefficients as well as confidence intervals that help to situate and contextualize VAM estimates and their measurement errors.

Accordingly, and also per the research, this is not getting much better in that, as per the authors of this article as well as many other scholars, (1) «the variance in value - added scores that can be attributed to teacher performance rarely exceeds 10 percent; (2) in many ways «gross» measurement errors that in many ways come, first, from the tests being used to calculate value - added; (3) the restricted ranges in teacher effectiveness scores also given these test scores and their limited stretch, and depth, and instructional insensitivity — this was also at the heart of a recent post whereas in what demonstrated that «the entire range from the 15th percentile of effectiveness to the 85th percentile of [teacher] effectiveness [using the EVAAS] cover [ed] approximately 3.5 raw score points [given the tests used to measure value - added];» (4) context or student, family, school, and community background effects that simply can not be controlled for, or factored out; (5) especially at the classroom / teacher level when students are not randomly assigned to classrooms (and teachers assigned to teach those classrooms)... although this will likely never happen for the sake of improving the sophistication and rigor of the value - added model over students» «best interests.»

As with the cases discussed above, the differences could come from variations in teachers» true value - added across student groups or from measurement error enhanced by the small sample size.

The debate over the new systems has often centered on the frequent errors in what's known as value - added measurement, which can lead to effective teachers being misidentified as ineffective, and whether the potential problems for teachers outweigh the potential benefits for students.

Perhaps a more reasonable explanation, though, is that there is some bias in the tests upon which the TVAAS scores are measured (as likely related to some likely issues with the vertical scaling of Tennessee's tests, not to mention other measurement errors).

If interested, see the Review of Article # 1 — the introduction to the special issue here; see the Review of Article # 2 — on VAMs» measurement errors, issues with retroactive revisions, and (more) problems with using standardized tests in VAMs here; see the Review of Article # 3 — on VAMs» potentials here; see the Review of Article # 4 — on observational systems» potentials here; see the Review of Article # 5 — on teachers» perceptions of observations and student growth here; see the Review of Article (Essay) # 6 — on VAMs as tools for «egg - crate» schools here; see the Review of Article (Commentary) # 7 — on VAMs situated in their appropriate ecologies here; and see the Review of Article # 8, Part I — on a more research - based assessment of VAMs» potentials here and Part II on «a modest solution» provided to us by Linda Darling - Hammond here.

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

If interested, see the Review of Article # 1 — the introduction to the special issue here; see the Review of Article # 2 — on VAMs» measurement errors, issues with retroactive revisions, and (more) problems with using standardized tests in VAMs here; see the Review of Article # 3 — on VAMs» potentials here; see the Review of Article # 4 — on observational systems» potentials here; see the Review of Article # 5 — on teachers» perceptions of observations and student growth here; see the Review of Article (Essay) # 6 — on VAMs as tools for «egg - crate» schools here; and see the Review of Article (Commentary) # 7 — on VAMs situated in their appropriate ecologies here; and see the Review of Article # 8, Part I — on a more research - based assessment of VAMs» potentials here.

If the scores are highly precise, the measurement errors will be small (and therefore will have little variation); hence the denominator of (1) will diminish (as in Figure 2), and reliability will increase.

While NAEP scores are widely cited, score changes over time can be a reflection of trends outside the classroom, such as changing demographics, attrition, measurement error and changes in circumstances, including the effects of the recession.

Results reinforce the importance of accounting for measurement error, as it meaningfully increases effect size estimates associated with teacher attributes.

Improvements in methods and equipment, processes of maintenance and calibration as well as a vastly better understanding of measurement methods have reduced the measurement errors by an order of magnitude over those years.

So the [theoretical] errors in the measurements are of the same order of magnitude as the changes being reported [at least for ocean circulation].

The ``... uneven spatial distribution, many missing data points, and a large number of non-climatic biases varying in time and space» all contribute inaccuracies to to the global temperature record — as do errors in orbital decay corrections, limb - corrections, diurnal corrections, and hot - target corrections, all of which rely on measurements (+ - inherent errors), in the satellite temperature records.

As if it were just some tiny little measurement error that could be ignored (as usualAs if it were just some tiny little measurement error that could be ignored (as usualas usual).

Whether you are gullible enough to accept the figures as accurate depends on how much credibility you put in the multitude of observational measurements taken by different methods over many decades by diverse groups of researchers that form a strong consilience of mutually supporting evidence for the validity of the estimates and the possible errors.

[Response: True, but as long as the errors themselves are iid, then you are still testing for a signal if you have many parallel series (the noise cancels in a similar way to taking the mean over many measurements).

If we are looking to detect very small trends then we really do need to isolate as many other factors as we can and measurement error is one of the simplest things to eliminate.

Generally, the remaining uncorrected effect from urban heat islands is now believed to be less than 0.1 C, and in some parts of the world it may be more than fully compensated for by other changes in measurement methods.4 Nevertheless, this remains an important source of uncertainty.The warming trend observed over the past century is too large to be easily dismissed as a consequence of measurement errors.

Systematic instrumental errors, such as changes in measurement practices or urbanisation, could be more important, especially earlier in the record (Chapter 3), although these errors are calculated to be relatively small at large spatial scales.

Of equal importance is that the range of density variation is of the same order as the density measurement error, determined through repeat measurements.

Of equal importance was that the range of density variation is of the same order as the density measurement error, determined through repeat measurements.

For the estimation of the total ocean heat content (OHC) a lesser precision would probably be almost as good, because errors of individual measurements always cancel to a large extent as long as the floats do not have common systematic errors.

Cogley (1999) referred to the use of this density of measurements to eliminate all but measurement error, as reductio ad absurdum.

If we have inadequate sampling, and short time intervals, the statistical uncertainties from random fluctuations and random measurement errors can be large, but would tend to cancel out as the number of observations and length of time increases.

If I, as a real world engineer, didn't allow for the error in measurements and work out an appropriate construction method, I'd be without a job soon enough.

Initial condition uncertainty arises due to errors in the estimate of the starting conditions for the forecast, both due to limited observations of the atmosphere, and uncertainties involved in using indirect measurements, such as satellite data, to measure the state of atmospheric variables.

Lots of factors make measuring global temperature a difficult task, such as sparse data in remote places, random measurement errors and changes in instrumentation over time.

As an example sampling the Southern oceans must happen in essentially same areas and using methods with errors that don't move in either direction systematically, but as long as there are no such systematic trends the results are not particularly sensitive to errors of individual measurementAs an example sampling the Southern oceans must happen in essentially same areas and using methods with errors that don't move in either direction systematically, but as long as there are no such systematic trends the results are not particularly sensitive to errors of individual measurementas long as there are no such systematic trends the results are not particularly sensitive to errors of individual measurementas there are no such systematic trends the results are not particularly sensitive to errors of individual measurements.

That would lead to permanent oscillations in the fit also in ocean areas and that would in turn cause significant errors in the interpretation of the SST measurements as the oscillating fit varies more than the real observed temperatures and makes the deviation of the observed temperature from that expected vary as well as a artefact.

More exact for the partitioning between oceans and vegetation are found in the oxygen balance, but with large margins of error, as oxygen change measurements (a few ppmv in 200,000 ppmv) are extremely difficult, at the edge of the accuracy of the methods used.

Now, as you say, there are all sorts of problems with the historical records of sea level but — just as with temperatures — it is likely that measurements from the satellites will be more accurate and less prone to random variation and sampling error than measurements from ground based sensors.