# Tag Info

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The knowledge you seek is (1) classical Goodness of Fit Statistical Tests and Regression Analysis. Just to get you going: standard linear regression (see the "Linear Regression" section in the Wikipedia "Regression Analysis" page) gives you formulas for the standard errors of the estimated parameters. One of your estimated parameters - the gradient is - ...

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The procedure for using such a container is described here The basis of the procedure is simple - "Make sure your eye is on a level with the mark. A Paralax Error will result, otherwise. Always read the meniscus from the bottom."

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When you run into a problem like this, there are several things to consider: Systematic error. For example, when you say "three, two, one, go" you never release exactly on "go" (plus, there is a time delay between when the person says "go" and when you hear it...). Other example: the floor is not level (as suggested by ACuriousMind). When you measure the ...

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"Infinite resolution" simply means, in this context, that it has a linear (analog) output - one that in principle changes for even the smallest change in position of the actuator. Contrast this with a transducer that produces an output in discrete steps - for example an optical encoder. Quoting from http://www.macrosensors.com/lvdt_tutorial.html : ...

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You have to differentiate between resolution, reproducibility and noise. Resolution can typically be made smaller than reproducibility and noise, at which point improvements in resolution become meaningless. This, by the way, is not just true for actuators but it holds just as much for sensors and measurements in general.

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I think infinite resolution assumes that you have infinitely precise voltage. The chip is infinitely precise in response, but the harder job is to have the voltage so stable to make use of that resolution. Another possibility is that within other characteristics the resolution could be infinite. For example, in some statistical limit calculation you can ...

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Consider a function $f$ of variables $x_1, x_2, \ldots$. If you assume your input quantities' errors are uncorrelated, then the variance of the output is given by the standard error propagation formula $$\sigma_f^2 = \left(\frac{\partial f}{\partial x_1}\right)^2 \sigma_{x_1}^2 + \left(\frac{\partial f}{\partial x_2}\right)^2 \sigma_{x_2}^2 + \ldots.$$ I ...

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