Run the remote experiment.
Use the controls (see below), gradually set the temperature of different elements.
Read temperature and store the measured values of resistance in the selected moments.
Save the experimental values to the HDD on your PC after measurement of the required data and exit remote experiment.
Open the obtained CSV file in a spreadsheet.
METAL: By the formula (2) determine the values of coefficient of metal resistance and averages them.
plot the dependence of metal resistance in the above chart and data proložíme of linear regression equation (y = a_{k}.x + b_{k}) with displayed regression coefficients.
Regression coefficient b_{k} has direct relevance to the value of R_{0} resistance element of used at 0 °C.
emperature coefficient of electrical resistance α is obtained by dividing the coefficients of of linear regression:
SEMICONDUCTOR: The spreadsheet can make the graph of R_{p}(t)] to t and compare the quality of its temperature-dependent electrical resistance of the metal.
Construct the graph of ln[R_{p}(T)] to ^{1}/_{T0}–^{1}/_{T} in a spreadsheet. We choose T_{0} as 273,15 K (ATTENTION: we are moving from °C to K !).
We will use the data for the the graph of, and according to the formula (4) we get a series of β values that averaged.
We return to the chart and we obtain linear regression equation (y = a_{p}.x + b_{p}) with displayed regression coefficients
Regression coefficient b_{p} has the meaning of ln(R_{0}). We determine from it the value of R_{0} - we can observe with the experimentally determined value at 0 °C.
Regression coefficient a_{p} has the importance of finding value and we obtain the value of activation energy ΔE by relationship ΔE = β.k (k - Boltzmann constant).
We can interspace the experimental values of electrical resistance of the two elements obtained dependencies with completed parameters: R_{0}, α, β: