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I. Kripke II. SOMIII. Linear regression. 1. Generate „measurement points” using the equation (emission rate as a function oftemperature T)

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I. Kripke II. SOM

III. Linear regression.

1. Generate „measurement points” using the equation (emission rate  as a function of temperature T)

 

kT

exp E

0

 .

0=109 [s-1] is a preexponential factor, k = 8.6x10-5 [eV K-1] the Boltzmann constant,

E is an activation energy. Choose E from the interval (100400) meV. Superimpose random noise (N/S = 0.1) on the y coordinate (emission rate). Generate about 10 points in the temperature range 100200 K.

2. Find the best fit parameters 0 and E, as well as their error estimates (uncertainties). Note that to use linear regression you have first to linearize the equation, i.e. to transform it in such a way that it becomes linear.

3. Because the task itself is rather trivial, your program should also contain the graphical interface, or at least you should include a few figures of the measurement points and the fitted line in the report (e.g with different noise to signal ratio or different number of points).

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