It is completely irrelevant to us now and will

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of the screen) and turning on the voltage in the circuit. It is completely irrelevant to us now, and will destroy any hope at a successful curve fit. As such, it is prudent to exclude it from the dataset before exporting. Select the portion of the dataset most similar to that in Figure 4, then click ‘Scale to fit’. You should see a smooth exponential decay; this is the data we want. Do not worry about being too exact in your selection, just ensure that you haven’t picked up any points from the vertical rise in the data just before the exponential decay. With the data still selected, navigate in the top menu bar to ‘Display’, then ‘Export Data’. Save the .txt file, since we’ve selected data it will only save the data we want; this is very important. As a side-note, the ‘Export Picture’ tool will save the graph as an image file. This is often useful if you need this plot in your lab reports. Congrats, you’re done with Datastudio for this assignment. 4
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Figure 4: Our nice exponential decay data 3 MATLAB fitting The MATLAB curve fitting guide should be used extensively while completing this assignment. To avoid redundancy this section will be sparser in detailed instructions than the last. If you are having difficulties please consult the guide, then ask the LTAC for assistance. We begin assuming you’ve followed the guide through Section 4. Your data should be im- ported and separated into two variables: x and y . x should contain your time data and y your voltage. Gray boxes signify commands to be entered into MATLAB. 3.1 Uncertainties and Model Function Our first task is you define the other two vectors we need: the uncertainties of x and y . The uncertainty for y , the voltage, was found earlier in the assignment (approximation of noise). We’ll label this value as yerror (when I say yerror , use this value; do not just type yerror ). However, we must make dy a vector the same size as y , in which each element is 5
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equal to yerror : dy = yerror . ones ( s i z e ( y ) ) ; As for dx , whenever we use Datastudio recorded times in this lab, we’ll just assume that their uncertainties are negligible (approximately zero: dx = zeros ( s i z e ( y ) ) ; For the model function, we will use an exponential decay: f ( x ) = A * exp( - x τ ) + B . A is a scaling parameter, B shifts the graph vertically, and τ is the decay time (it controls the steepness of the decay).
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