Sentences with phrase «for errors in their measurements»

It is not enough merely to republish measured means that are withing the error window of the model, it also needs to account for the error in measurement.

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.

Not exact matches

In Facebook's case, the issue is complicated by the fact that the social network has repeatedly had to admit errors in its audience - measurement analytics, including over-estimating video views for more than two yearIn Facebook's case, the issue is complicated by the fact that the social network has repeatedly had to admit errors in its audience - measurement analytics, including over-estimating video views for more than two yearin its audience - measurement analytics, including over-estimating video views for more than two years.

One diplomat said the IAEA allows for a margin of error of 1 percentage point in such measurements, which means that Iran wasn't technically over the limit.

According to him, the error lies in assuming that one is dealing with the same set of possible spin - measurement results for the particles coming out one side of the apparatus described no matter what orientation one considers for the spin - measuring device (s) on the other side of the apparatus.

I wanted to let you know there were a couple of errors in the measurements for the Kale and Quinoa Salad because mom wrote the recipe from memory...

However, because each of these measurements must be calibrated to account for natural variation in the environment over time, individual dates have large amounts of error and uncertainty, making them difficult to aggregate or interpret in groups.

We can confirm this by doing statistics for a lot of measurements and calculate the statistical standard error of the mean, which in our case is 850 zeptoseconds.»

«There is measurement error in any score,» says Edward Polloway of Lynchburg College in Virginia, co-chair of a task force on the death penalty for the American Association on Intellectual and Developmental Disabilities (AAIDD).

There are several reasons for the variation, including whether courts take into account the measurement error inherent in IQ scores — the fact that an individual, tested repeatedly, would not achieve the same score every time, but rather a distribution of scores clustered around their «true» IQ.

Estimating the errors inherent in the kind of measurements used by Maciejewski and his co-authors is a tough thing to do, Bean says; if the actual errors in the data were larger than the researchers had accounted for, the variations in the observed transit times could vanish.

While there remain disparities among different tropospheric temperature trends estimated from satellite Microwave Sounding Unit (MSU and advanced MSU) measurements since 1979, and all likely still contain residual errors, estimates have been substantially improved (and data set differences reduced) through adjustments for issues of changing satellites, orbit decay and drift in local crossing time (i.e., diurnal cycle effects).

Group 1: Materials, Resonators, & Resonator Circuits A. Fundamental Properties of Materials B. Micro - and Macro-Fabrication Technology for Resonators and Filters C. Theory, Design, and Performance of Resonators and Filters, including BAW, FBAR, MEMS, NEMS, SAW, and others D. Reconfigurable Frequency Control Circuits, e.g., Arrays, Channelizers Group 2: Oscillators, Synthesizers, Noise, & Circuit Techniques A. Oscillators — BAW, MEMS, and SAW B. Oscillators - Microwave to Optical C. Heterogeneously Integrated Miniature Oscillators, e.g., Single - Chip D. Synthesizers, Multi-Resonator Oscillators, and Other Circuitry E. Noise Phenomena and Aging F. Measurements and Specifications G. Timing Error in Digital Systems and Applications Group 3: Microwave Frequency Standards A. Microwave Atomic Frequency Standards B. Atomic Clocks for Space Applications C. Miniature and Chip Scale Atomic Clocks and other instrumentation D. Fundamental Physics, Fundamental Constants, & Other Applications Group 4: Sensors & Transducers A. Resonant Chemical Sensors B. Resonant Physical Sensors C. Vibratory and Atomic Gyroscopes & Magnetometers D. BAW, SAW, FBAR, and MEMS Sensors E. Transducers F. Sensor Instrumentation Group 5: Timekeeping, Time and Frequency Transfer, GNSS Applications A. TAI and Time Scales, Time and Frequency Transfer, and Algorithms B. Satellite Navigation (Galileo, GPS,...) C.Telecommunications Network Synchronization, RF Fiber Frequency Distribution D. All - optical fiber frequency transfer E. Optical free - space frequency transfer F. Frequency and Time Distribution and Calibration Services Group 6: Optical Frequency Standards and Applications A. Optical Ion and Neutral Atom Clocks B. Optical Frequency Combs and Frequency Measurements C. Ultrastable Laser Sources and Optical Frequency Distribution D. Ultrastable Optical to Microwave Conversion E. Fundamental Physics, Fundamental Constants, and Other Applications

This is important because you might have to redefine a similarity between data points or you might have to correct for a slight measurement error in your data.

We don't know for sure if any change in resting metabolism is because of extra muscle, or whether it's due to measurement error.

Despite the measurement of key confounders in our analyses, the potential for residual confounding can not be ruled out, and although our food frequency questionnaire specified portion size, the assessment of diet using any method will have measurement error.

Even with this methodology and controlling for measurement error and other variables, Krueger and Lindahl found that the effect of the change in schooling on growth did not always pass standard tests for a significant statistical relationship.

We also use information on the school's performance composite two years before the year to correct for measurement error in the school's previous - year performance.

Nevada has imposed steep penalties on Harcourt Educational Measurement for errors in administering statewide exams, and Georgia is poised to do the same, following scoring glitches typical of the kind that have plagued state - sponsored testing programs in recent years.

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.»

For comparison, and to distinguish measurement error from true differences in teacher effectiveness, the authors ran similar correlations with randomly separated groups of students.

Observers committed to reducing error should consider multiple measurements for teacher evaluation.Yes, Evaluations Can Be Fair and Accurate In this month's ASCD, Robert Marzano discusses ways to minimize error and maximize accuracy and fairness when principals, coaches, or other administrators are conducting classroom observations.

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.

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 reerror is expected with any measurement, statisticians developed the term Standard Error of Measurement (SEM) to account for small amounts of error in evmeasurement, statisticians developed the term Standard Error of Measurement (SEM) to account for small amounts of error in every reError of Measurement (SEM) to account for small amounts of error in evMeasurement (SEM) to account for small amounts of error in every reerror in every result.

If misalignment is noticed, it is not to be the fault of either measure (e.g., in terms of measurement error), it is to be the fault of the principal who is critiqued for inaccuracy, and therefore (inversely) incentivized to skew their observational data (the only data over which the supervisor has control) to artificially match VAM - based output.

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.

They should control for multiple previous test scores and account for measurement error in those tests.

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.

The state might follow the recommendations of analysts and use tests from multiple subjects and control for measurement error in their value - added calculations.

Our method generalizes the test - retest framework by allowing for i) growth or decay in knowledge and skills between tests, ii) tests being neither parallel nor vertically scaled, and iii) the degree of measurement error varying across tests.

Correcting for Test Score Measurement Error in ANCOVA Models for Estimating Treatment Effects

And since we don't have good ocean heat content data, nor any satellite observations, or any measurements of stratospheric temperatures to help distinguish potential errors in the forcing from internal variability, it is inevitable that there will be more uncertainty in the attribution for that period than for more recently.

In what world are variations for measured temperatures considered of approximately 3K considered «small» or buried within measurement error?

For a lot of folks trained in experimental method, the error term associated with a data point is the measurement error, period.

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 facility has enabled the different instrument teams to calibrate their instruments, and check for uncorrected errors, like excessive scattering and diffusive light contamination in the measurement chambers.

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).

While there remain disparities among different tropospheric temperature trends estimated from satellite Microwave Sounding Unit (MSU and advanced MSU) measurements since 1979, and all likely still contain residual errors, estimates have been substantially improved (and data set differences reduced) through adjustments for issues of changing satellites, orbit decay and drift in local crossing time (i.e., diurnal cycle effects).

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.

Accounting for Both Random Errors and Systematic Errors in Uncertainty Propagation Analysis of Computer Models Involving Experimental Measurements with Monte Carlo Methods.

Morano said the «hottest year» was dealt with by scientists appearing in the film and that in any case, the declaration would only sneak above the margin of error for global land - based measurements.

However, world average temperature measurements are subject to an error of plus or minus 0.1 degrees, while any attempt to calculate a trend for the period 1997 - 2012 has an in - built statistical error of plus or minus 0.4 degrees.

At the end is like pretending you can detect a milligram change in weight using a balance with a precision of one kg, you only need to use many balances, model the measurements for a hundred years and show a mean ensemble of the results without error bars and a very obtuse wording.

Hence, it possible for a large number of measurements at different locations to result in a meaningful reduction in the level of error of a quantity, provided that the value of the quantity does not vary much across the sample space.

Measuring the distance apart and speed of 2 satellites in space orbiting the earth to the width of a human hair with no margin for error [damn those drift recalculations], and taking into account unknown factors with respect to the true values for water depth, water weight at different salt concentrations, ice depth magma flows, volcanic activity etc [ie making a lot of guesses], plus taking human motivation on board [like CO2 increase must melt ice surely] can give you an accurate measurement of the volume ice in Antarctica.

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.

The measurement uncertainties account for correlations between errors in observations made by the same ship or buoy due, for example, to miscalibration of the thermometer.

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.

Trends reflect the mean change in temperature (in K per decade) between 20 ° N and 20 ° S for the period 1979 — 2005, obtained from radiosonde temperature measurements 5 (blue and green colours), climate models 8 (dashed orange, with grey shading indicating 2 - sigma range) and the new reconstructions from radiosonde winds 4 (pink, with error bars indicating 2 - sigma range).