Sentences with phrase «actually measured data»

Those probabilities are then compared to the actually measured data, and adjustments are made to the network in order to make them match better in the next round.

Not exact matches

The statistics are merely sets of data measured in a specific context and at least the person who posted them had the respect to allow us to make our own interpretation rather than inserting his own opinion which is what Buddha actually wanted people to do... not just accept things on blind faith but interpret for themselves and experience for themselves.

There's also the issue that it's very hard to measure teacher quality when we're actually using testing scores as data.

However, using data from the National Assessment of Educational Progress (NAEP) and from New York's state exams, Dr. Aaron Pallas of Teachers College demonstrated that between 2003, just before Mayor Bloomberg's reforms had been implemented, and 2011, the achievement gap demonstrated by the NAEP actually rose and the gap measured by state exams closed by a mere 1 %.

«The problem is that you do not actually directly observe the electron dynamics that causes the changes: you measure the reflection and then you try to find an explanation based on the interpretation of your data,» Prof Dani said.

But our data has led us to believe that it is actually the microtubule tracks themselves that are most capable of producing the amount of force we measured,» Maresca says.

RV: And since you began using data to improve writing, how have you actually measured the impact it has had on teaching and learning?

In addition schools need to ensure that it has proper organisational and technical measures and policies in place to keep personal data safe and secure — having a robust information security policy which is actually adhered to throughout the business is part of this.

The data revealed that reading skill actually helped students compensate for gaps in science knowledge for most measures of science achievement.

The research presented in this volume seems to support incorporating data around adult collaboration into accountability measures — data that «reveals more about how schools actually work — work processes, social interactions, norms and beliefs, and especially how all of this comes together.»

Ultimately, you need longitudinal test score growth data to actually measure improvements in student achievement that can not be observed with the naked eye.

In education and even more so as teachers, we hear the term progress all the time; all students need to make progress, progress checks, planning for progress, data informing progress, progress through effective feedback and so on... but what does progress actually look like in day to day classroom practice and how can we measure... Continue reading →

In education and even more so as teachers, we hear the term progress all the time; all students need to make progress, progress checks, planning for progress, data informing progress, progress through effective feedback and so on... but what does progress actually look like in day to day classroom practice and how can we measure it?

Finally, these articles suggest that we take a closer look at what data are actually measuring and why.

The new study takes a top - down approach, measuring what is actually present in the atmosphere and then using meteorological data and statistical analysis to trace it back to regional sources.

I understand that PIOMAS is forced by NCEP / NCAR data, which I think makes a lot of sense, since that the data that is as close to «reality» as we can get without actually measuring ice volume.

Then, instead of throwing out the data as hopelessly compromised and starting the experiment over with these factors corrected, you (a) do a study estimating how miscalibrated, how defective and how improperly located your instruments were and apply adjustments to all past data to «correct» the improper reading, (b) you do a study to estimate the effect of the external factors at the time you discover the problem and apply adjustments to all past data to «correct» the effects of the external factors even though you have no idea what the effect of the external factor actually was for a given instrument at the time the data was recorded, because you only measured the effect years later and then at only some locations, (c) you «fill in» any missing data using data from other instruments and / or from other measurements by the same instrument, (d) you do another study to determine how best to deal with measurements from different instruments over different time periods and at different locations and apply adjustments to all past data to «correct» for differences between readings from different instruments over different time periods at different locations.

And in looking at the UAH data, the TLT absolute cycle temperatures are way below the surface temperaure time series because they are actually measuring a few KM (But exactly how many KM's?)

Understand that we have now gone all the way from what I actually said (that climate change impacts have become so profound now that we often don't need fancy techniques to see them) to something so patently absurd I couldn't possibly have said it (that we don't need data to measure global warming).

Dear Alex, that may not «sound right», but if we actually collected data from all 500,000,000 room thermostats, and if those -1 / +1 errors are all independent, then yes, we would be able to measure global temperature changes to an accuracy of 0.000045 C.

JCH — if you actually knew anything about it, you would know that the sat data WILL NOT measure the same temperature as at the surface.

oneuniverse, you write «I therefore don't agree with your statement «Until we have the empirical data which actually measures climate sentitrivity, any particular number is as good, or as bad, as any other.»

oneuniverse, you write «Yet according to Jim, «Until we have the empirical data which actually measures climate sensitivity, any particular number is as good, or as bad, as any other.»

On a side note, we should also have a high degree of confidence that the data we depend on actually measures accurately the dimension in question.

Yet according to Jim, «Until we have the empirical data which actually measures climate sensitivity, any particular number is as good, or as bad, as any other.»

We selected this model because the Beaver Creek sub-watershed is actually within our study area (Figure 1), and because the model was based upon measured empirical data.

Would you agree with the statement «Until we have the empirical data which actually measures climate sentitrivity, all we have with respect to CAGW is a hypothesis»?

I therefore don't agree with your statement «Until we have the empirical data which actually measures climate sentitrivity, any particular number is as good, or as bad, as any other.»

Why on earth did we pay so much money for the ARGO floats, the only instrumentation actually designed for the task of measuring global ocean temperature accurately to hundredths of a degree, if we don't use the data?

Actually Fielding's use of that graph is quite informative of how denialist arguments are framed — the selected bit of a selected graph (and don't mention the fastest warming region on the planet being left out of that data set), or the complete passing over of short term variability vs longer term trends, or the other measures and indicators of climate change from ocean heat content and sea levels to changes in ice sheets and minimum sea ice levels, or the passing over of issues like lag time between emissions and effects on temperatures... etc..

For — in any other serious scientific / engineering endeavour — a detailed, available and up to date knowledge of the state of all your measuring instruments, their service history and calibration, their external and internal surroundings and anything else that could potentially affect their readings is a prereq to making sure that you are actually collecting valid data.

I would say that the noisy plot here; which I presume is actually somewhat less «noisy» real data; that is true measured data, looks more like a 1 / f noise signature; rather than Gaussian.

As just one example; «How we can know an average global sea surface temperature back to 1850 when so much of the world was unexplored let alone its oceans measured» should be just one example that should make scientists question whether the models they build are actually using reliable data, or whether they think they already know the answer and therefore just use data that supports it, no matter its doubtful provenance.

See, the first thing to do is do determine what the temperature trend during the recent thermometer period (1850 — 2011) actually is, and what patterns or trends represent «data» in those trends (what the earth's temperature / climate really was during this period), and what represents random «noise» (day - to - day, year - to - random changes in the «weather» that do NOT represent «climate change»), and what represents experimental error in the plots (UHI increases in the temperatures, thermometer loss and loss of USSR data, «metadata» «M» (minus) records getting skipped that inflate winter temperatures, differences in sea records from different measuring techniques, sea records vice land records, extrapolated land records over hundreds of km, surface temperature errors from lousy stations and lousy maintenance of surface records and stations, false and malicious time - of - observation bias changes in the information.)

And BTW Dr. Spencer also says «I would remind folks that the NASA AIRS instrument on the Aqua satellite has actually measured the small decrease in IR emission in the infrared bands affected by CO2 absorption, which they use to «retrieve» CO2 concentration from the data.

In the data above, the R ^ 2 (a measure of correlation) between the temperature and the CO2 is 0.68... but the R ^ 2 between the temperature and the logarithm of CO2, rather than being better as we'd expect if CO2 were actually driving temperature, is marginally worse for the logarithmic relationship (0.67) than the linear.

Here's my concern with an Energy Balance Model, first presuming we can and are actually measuring to the necessarily accuracy, we don't have data for a long enough period of time to understand if it doesn't vary with time, so even if it says there is an imbalance, that may very well be normal.

For the HadSST3 temperature data set and successors, it was assumed that 30 % of the ships shown in existing metadata as measuring SST by buckets actually used engine inlet:

In an article from November 5, 2008, Josh Willis states that the world ocean actually has been warming since 2003 after removing Argo measurement errors from the data and adjusting the measured temperatures with a computer model his team developed.

precisely because this proxy does nt track the variable of interest during the only length of time in that entire period when we actually have directly measured data for that variable.

The data available for training and validation, the data against which the winners were judged is NOT actually a measure of crime.

Actually, quantifying and measuring the effectiveness of premarital counseling is very tough — data is mostly taken from self - report impressions and post-facto scores on «marital satisfaction» and occurrence of divorce.