Sentences with phrase «measurement errors because»

Limitations of the study include possible measurement errors because the data was self - reported, although the authors noted that because the study cumulatively measured diet over time, it reduced such errors.

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

Not exact matches

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...

Second, because both weight measurement and feeding practice were reported by mothers, reporting errors may have occurred.

Because of different measurement methods, Pls allow 2 - 3 cm error.

The conclusion is tentative because of possible measurement errors.

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.

The researchers say that, because of the measurement's margin of error, the finding translates to essentially no methane in the...

Sometimes, these arise because people who commission analytical measurements are ill - informed about the error margins on the data they receive, and do not bother to discover those margins.

Because the signals arriving at a receiver from all satellites are measured at the same time, the distance measurements are all falsified by the same receiver clock error, which must be calculated in order to determine an accurate position.

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.

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.

Nondifferential misclassification because of random measurement errors, especially for VCAM - 1, may have attenuated the observed associations.

This type of measurement error is unlikely to bias our estimates because there is no reason to believe it is related to whether a student won the school - choice lottery.

A New York high school student who received a lower score on the SAT because of errors in grading the October 2005 test plans to sue the College Board, the sponsor of the exam, and Pearson Educational Measurement, the company that scored it, lawyers say.

Inaccurate tests: Scores for an individual can vary greatly because even tests with high reliability can have substantial measurement error.

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.

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).

This is because each measurement and its error are independent.

Moreover, because of the effects of wind speed, evaporation, and precipitation intensity, different types of rain gauge, and observation techniques induce different errors in precipitation measurements.

The principal scientific objective is to make global SSS measurements over the ice - free oceans with 150 - km spatial resolution, and to achieve a measurement error less than 0.2 (PSS - 78 [practical salinity scale of 1978]-RRB- on a 30 - day time scale, taking into account all sensors and geophysical random errors and biases.Salinity is indeed a key indicator of the strength of the hydrologic cycle because it tracks the differences created by varying evaporation and precipitation, runoff, and ice processes.

Again, because of the uniform nature of the temperature, a series of n observations taken at various points around the room will have an error that is 1 / SQRT (n) smaller than a single measurement alone.

This noise is either systemic (caused by measurement errors, etc) or aleatory which is contributions from everything else we don't yet fully comprehend, or can't because of the shear number of other paths.

The former can be said with a great deal of certainty because it relies on and a reality of scientific measurement and the statistical analysis of error bounds.

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.

Measurement and sampling errors (derived in part 1) are larger than in previous analyses of SST because they include the effects of correlated errors in the observations.

An error - free laboratory measurement of modern fraction does not imply that the problem collapses into a deterministic look - up from the calibration curve — even if the curve is monotonic over the relevant calendar interval — because the curve itself carries uncertainty in the form of the variance related to the conditional probability of RC age for a given calendar date.

Because of the nature of these measurement problems and biases, almost all of these errors tend to be in the same direction — biasing temperatures higher — creating a systematic error that does not cancel out.

Corrections to Brahe's measurements to show the orbit is an ellipse could have been made, although Brahe did not know ho to make them because he didn't know how to handle errors of measurements.

That works fine if the errors behave like measurement errors, because measurement errors are independent - if you average them over the globe they tend to cancel out, and the global mean thus has a lower uncertainty than an individual measurement.

What will be left are the systematic errors, the things that don't cancel because they are common to all the measurements.

Indeed this should always be done, because the noise is measurement error, which also biases estimates — unless it is appropriately accounted for, as it can be when retests occur in the survey.

We hoped to eliminate the mechanical devices that required more personal attention (taking measurements, changing charts, etc.) At one point the electronic instruments showed a step change that was not reflected by the mechanical recorders and the electronic equipment designer asserted that the mechanical devices must be in error because they did not show the step change.

And if you take the difference between yesterday's and today's min on a station by station basis where the measure is done the same way, I think you get the best possible value, plus any error in the measurement can't get any larger because they don't accumulate, it can only get as large as the yesterday's and today's error added together.

But whatever the measurement error is, the way I treat processing Tmin and Tmax, I have half the error they have because Tmin and Tmax are not correlated, so Tavg as 2 errors, and Tmin day1 is correlated to Tmin day 2.

I note that these authors take the Thompson et al. 2010 Nature article as implying measurement error - which is odd because that doesn't seem to be what Thompson et al. actually said.

Where I am not connecting is if n includes both the sampling and measurement error it would not be independent of T because of the sampling part.

But when you write in a post here at WUWT, «The problem of «extra heat» in land temperatures (likely to be UHI and more) is escalated by GISS because they extrapolate the ground based land temperature measurements over the oceans in stead of using real ocean data,» I will remind you that GISS notes the errors in the dTs data on their webpage:

(In other words, even if the monthly anomalies reported for GISTEMP, for example, were accurate to 0.00001 degrees, it wouldn't change the fact that we'd need well over a decade of readings to ascertain the long - term trend, because the variability is real feature of the system and not simply a measurement error.)

The two data points DO NOT tell you how much the temperature has actually increased, because they are subject to measurement error, if nothing else.

We can not conclusively argue that stress did not play a mediating role in the association between income change and smoking behaviour because of measurement error.

Latent constructs defined by single indicators are preferred to observed variables, because measurement error is taken into account (Hayduk, 1987).