Error Propogation in Measuring Resistance of I-V Graph

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In summary, when setting up a circuit to measure resistance and create an I-V graph, it is important to consider the error on the evaluation of the resistance. This can be calculated by taking the square root of the sum of the squares of the individual errors, including the error from the equipment used to take the reading and the error from the graph paper used to measure the potential difference. This will provide a more accurate estimate of the resistance.
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Gregg
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I plan to set up a circuit to measure the resistance and basically verify Ohm's Law, I am to measure the current and potential difference across a resistor and plot an I-V graph with error bars included. The resistance on this graph is going to be [itex] \frac{\Delta V}{\Delta I} = \frac{1}{\text{gradient}}[/itex] The question is, is how am I going to estimate an error on the evaluation of the resistance of this graph?

The error on [tex] \alpha_{\Delta X} = \sqrt{ (\alpha_{x_1})^2+(\alpha_{x_2})^2} [/tex]

Where in general [itex]\alpha_x[/itex] is the error of the variable [itex] x [/itex]

And the function is of the form

[tex] R=\frac{\Delta V}{\Delta I} \Rightarrow \alpha_R=R\sqrt{ (\frac{\alpha_{\Delta I}}{\Delta I})^2+(\frac{\alpha_{\Delta V}}{\Delta V})^2 } [/tex]

The problem I have is this:

For say [itex] \Delta V = V_2-V_1[/itex] there is an error [tex] \sqrt{2(\alpha_V)^2} [/tex] from the equipment used to take the reading. But with the graph, depending on the separation. One division in the graph paper being x units gives further error [tex] \pm x [/tex] for [tex] \Delta V [/tex]. How am I supposed to combine them?
 
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The best approach would be to combine the two errors by taking the square root of the sum of the squares of the individual errors. In this case, you would calculate the error on the resistance as:\alpha_R=R\sqrt{ (\frac{\alpha_{\Delta I}}{\Delta I})^2+(\frac{\alpha_{\Delta V}}{\Delta V})^2 +(x)^2 } Where x is the error of one division in the graph paper used to measure the potential difference. This will give you a more accurate estimate of the error on the evaluation of the resistance.
 

What is error propagation in measuring resistance of I-V graph?

Error propagation refers to the process of calculating and determining the uncertainty or error associated with a measured quantity, such as resistance, in an I-V graph. It takes into account all the sources of error that can affect the measurement and calculates the overall uncertainty in the final result.

What are the sources of error in measuring resistance of I-V graph?

The sources of error can include human error in taking readings, limitations of the equipment used, variations in environmental conditions, and inherent variations in the material being measured. These errors can accumulate and affect the final resistance value obtained from the I-V graph.

How is error propagation calculated for measuring resistance of I-V graph?

Error propagation is calculated by using mathematical formulas that take into account the uncertainty and variation in each individual measurement, as well as the relationship between the measured quantities. This can involve techniques such as error propagation equations, Monte Carlo simulations, or using statistical analysis tools.

Why is error propagation important in measuring resistance of I-V graph?

Error propagation is important because it provides a more accurate understanding of the uncertainty associated with a measured quantity. It allows scientists to determine the range of possible values for the resistance and the likelihood of obtaining a particular result. This information is crucial for making informed decisions and drawing meaningful conclusions from the data.

How can error propagation be minimized in measuring resistance of I-V graph?

Error propagation can be minimized by taking multiple readings, using high-precision equipment, controlling environmental conditions, and ensuring proper techniques are used during the measurement process. It is also important to identify and address any potential sources of error and to use appropriate methods for calculating and reporting uncertainties.

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