experiment 1 experimental uncertainty error and data analysis Estill South Carolina

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experiment 1 experimental uncertainty error and data analysis Estill, South Carolina

If the experimenter were up late the night before, the reading error might be 0.0005 cm. Say you used a Fluke 8000A digital multimeter and measured the voltage to be 6.63 V. Maybe we are unlucky enough to make a valid measurement that lies ten standard deviations from the population mean. Your cache administrator is webmaster.

To get some insight into how such a wrong length can arise, you may wish to try comparing the scales of two rulers made by different companies — discrepancies of 3 If a carpenter says a length is "just 8 inches" that probably means the length is closer to 8 0/16 in. For a series of measurements (case 1), when one of the data points is out of line the natural tendency is to throw it out. Pugh and G.H.

Each data point consists of {value, error} pairs. In[7]:= Out[7]= (You may wish to know that all the numbers in this example are real data and that when the Philips meter read 6.50 V, the Fluke meter measured the If yes, you would quote m = 26.100 ± 0.01/Sqrt[4] = 26.100 ± 0.005 g. Generated Sat, 15 Oct 2016 12:44:26 GMT by s_wx1127 (squid/3.5.20) ERROR The requested URL could not be retrieved The following error was encountered while trying to retrieve the URL: http://0.0.0.7/ Connection

Please try the request again. For example, in measuring the height of a sample of geraniums to determine an average value, the random variations within the sample of plants are probably going to be much larger http://www.upscale.utoronto.ca/PVB/Harrison/ErrorAnalysis/ 3.2 Determining the Precision 3.2.1 The Standard Deviation In the nineteenth century, Gauss' assistants were doing astronomical measurements. Lectures and textbooks often contain phrases like: A particle falling under the influence of gravity is subject to a constant acceleration of 9.8 m/.

The function AdjustSignificantFigures will adjust the volume data. The system returned: (22) Invalid argument The remote host or network may be down. In this case the precision of the result is given: the experimenter claims the precision of the result is within 0.03 m/s. Using a better voltmeter, of course, gives a better result.

In[44]:= Out[44]= The point is that these rules of statistics are only a rough guide and in a situation like this example where they probably don't apply, don't be afraid to For example, if the error in a particular quantity is characterized by the standard deviation, we only expect 68% of the measurements from a normally distributed population to be within one Say you are measuring the time for a pendulum to undergo 20 oscillations and you repeat the measurement five times. If an experimenter consistently reads the micrometer 1 cm lower than the actual value, then the reading error is not random.

Please try the request again. Please try the request again. Не удалось открыть файл, поскольку в вашем браузере отключено использование JavaScript. Включите его и перезагрузите страницу.Войти1-1 Experimental Uncertainty.docxОткрыть доступЭта версия Firefox больше не поддерживается. Установите поддерживаемую версию браузера.ЗакрытьФайлПравкаВидИнструментыСправкаСпециальные возможностиОтладкаПоследние измененияСпециальные возможностиТолько You get a friend to try it and she gets the same result.

If ... You get another friend to weigh the mass and he also gets m = 26.10 ± 0.01 g. In[37]:= Out[37]= One may typeset the ± into the input expression, and errors will again be propagated. Theorem: If the measurement of a random variable x is repeated n times, and the random variable has standard deviation errx, then the standard deviation in the mean is errx /

In Section 3.2.1, 10 measurements of the diameter of a small cylinder were discussed. For example, one could perform very precise but inaccurate timing with a high-quality pendulum clock that had the pendulum set at not quite the right length. You remove the mass from the balance, put it back on, weigh it again, and get m = 26.10 ± 0.01 g. Common sense should always take precedence over mathematical manipulations. 2.

Taylor, An Introduction to Error Analysis (University Science Books, 1982) In addition, there is a web document written by the author of EDA that is used to teach this topic to sumx = x1 + x2 + ... + xn We calculate the error in the sum. This completes the proof. This is exactly the result obtained by combining the errors in quadrature.

In both cases, the experimenter must struggle with the equipment to get the most precise and accurate measurement possible. 3.1.2 Different Types of Errors As mentioned above, there are two types However, you're still in the same position of having to accept the manufacturer's claimed accuracy, in this case (0.1% of reading + 1 digit) = 0.02 V. The mean is sometimes called the average. Baird, Experimentation: An Introduction to Measurement Theory and Experiment Design (Prentice-Hall, 1962) E.M.

In[16]:= Out[16]= As discussed in more detail in Section 3.3, this means that the true standard deviation probably lies in the range of values. But, as already mentioned, this means you are assuming the result you are attempting to measure. Applying the rule for division we get the following. Wolfram Science Technology-enabling science of the computational universe.

It also varies with the height above the surface, and gravity meters capable of measuring the variation from the floor to a tabletop are readily available. So you have four measurements of the mass of the body, each with an identical result. So we will use the reading error of the Philips instrument as the error in its measurements and the accuracy of the Fluke instrument as the error in its measurements. There is no fixed rule to answer the question: the person doing the measurement must guess how well he or she can read the instrument.

You should be aware that when a datum is massaged by AdjustSignificantFigures, the extra digits are dropped. If the error in each measurement is taken to be the reading error, again we only expect most, not all, of the measurements to overlap within errors. In[41]:= Out[41]= 3.3.1.2 Why Quadrature? In[1]:= We can examine the differences between the readings either by dividing the Fluke results by the Philips or by subtracting the two values.

In[1]:= In[2]:= In[3]:= We use a standard Mathematica package to generate a Probability Distribution Function (PDF) of such a "Gaussian" or "normal" distribution. Another similar way of thinking about the errors is that in an abstract linear error space, the errors span the space. In complicated experiments, error analysis can identify dominant errors and hence provide a guide as to where more effort is needed to improve an experiment. 3. A valid measurement from the tails of the underlying distribution should not be thrown out.

Support FAQ Wolfram Community Contact Support Premium Support Premier Service Technical Services All Support & Learning » Company About Company Background Wolfram Blog News Events Contact Us Work with Us Careers In[9]:= Out[9]= Notice that by default, AdjustSignificantFigures uses the two most significant digits in the error for adjusting the values. Thus, repeating measurements will not reduce this error. By default, TimesWithError and the other *WithError functions use the AdjustSignificantFigures function.

The major difference between this estimate and the definition is the in the denominator instead of n. It is calculated by the experimenter that the effect of the voltmeter on the circuit being measured is less than 0.003% and hence negligible. We can show this by evaluating the integral. Next, the sum is divided by the number of measurements, and the rule for division of quantities allows the calculation of the error in the result (i.e., the error of the

A reasonable guess of the reading error of this micrometer might be 0.0002 cm on a good day. For n measurements, this is the best estimate.