Lab Experiment: Estimate the error in these measurements of Ohm's law

In summary, the conversation discusses two different experiments to measure the resistance of a circuit using both ac and dc current. The questions raised are whether to use Least Squares fit or the definition of mean and variance to estimate the resistance, and whether to combine the ac and dc measurements. It is also mentioned that the calibration error of the meters used may affect the accuracy of the measurements. The conversation concludes with the suggestion to plot the data and consider both random and systematic errors when analyzing the results.
  • #1
ORF
170
18
Homework Statement
calculate resistance, measuring current and voltage
Relevant Equations
Ohm Law, V = R*I
Hi,

I have measured a set of V-I values, and I have to provide the value of the resistance. I have used ac and dc current.

The circuit was quite simple:
power supply -> ammeter -> resistance -> [power supply]
wave generator -> ammeter -> resistance -> [wave generator]

Voltage was measured using an oscilloscope.

So, I think there are two ways of estimating the resistance, but they are not the same and I do not know which one should be used.

On one side, you may use Least Squares fit to estimate the best value for R. Is it correct to mix measurements with ac and dc current? How shall these measurements be combined?

On the other side, I though about using the definition of mean and variance for the set of "V_i / I _i" values .

Although the resistance mean value of is quite similar using one or the other, the error is not.

So, the questions are:
1. Should "V_i / I _i" be considered independent measurements, and therefore resistance should be calculated using the regular average and variance formulas? or should it be considered as a pair of correlated random variables, and R must be obtained from the fit of V-I plot?
2. Is it correct to combine ac and dc measurements?

Thank you in advance.

Regards,
ORF
 
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  • #2
Hi,

You have done two different experiments. So you want to keep them separate until you have an idea about matching and mismatching conditions.

If you vary one thing and measure something that depends on it, always make a plot.
Consider what are random errors and what are systematic errors.

1. Not really: I suppose you used the same meters (ammeter and oscilloscope), so their caliration error is common to all observations !
2. At the very end only. Suppose one says 220 ##\pm## 2 ##\Omega## and the other says 140 ##\pm## 20 ##\Omega## ?
 
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  • #3
ORF said:
Should "V_i / I _i" be considered independent measurements, and therefore resistance should be calculated using the regular average and variance formulas? or should it be considered as a pair of correlated random variables, and R must be obtained from the fit of V-I plot?
If you plot y against x and get the slope from standard regression analysis, the algebra minimises Σ(Δy)2. In effect, it assumes your x values are exact and the only errors are in y. A more even-handed approach minimises sum square of the distances from the plotted points to the regression line - i.e. as measured along the normal to the line.
More generally, if you can make a priori estimates in accuracies of the two sets of measurements, you can weight the x and y coordinates of the distances accordingly.

If you compute the V/I ratios and take the average, that is something else again. I don't recommend it, but I'd need to do some more analysis to justify that.
 
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What is Ohm's law and why is it important in lab experiments?

Ohm's law is a fundamental principle in physics that describes the relationship between voltage, current, and resistance in an electrical circuit. It states that the current flowing through a conductor is directly proportional to the voltage applied across it, and inversely proportional to the resistance of the conductor. This law is important in lab experiments because it allows us to predict and control the behavior of electrical circuits, making it a fundamental tool in electrical engineering and other scientific fields.

What are the sources of error in measurements of Ohm's law?

There are several potential sources of error in measurements of Ohm's law, including human error, equipment error, environmental factors, and inherent limitations of the law itself. For example, human error can occur when taking readings from instruments, while equipment error can result from faulty or imprecise instruments. Environmental factors such as temperature and humidity can also affect the accuracy of measurements, and the law itself may not hold true under certain conditions, such as with non-ideal conductors.

How can we estimate the error in measurements of Ohm's law?

To estimate the error in measurements of Ohm's law, we can use statistical methods such as calculating the standard deviation or percent error. These methods involve comparing the measured values to the expected values and determining the degree of variation or deviation between them. Additionally, we can also perform multiple trials and take the average of the results to reduce the impact of random errors.

What are the implications of having a large error in measurements of Ohm's law?

A large error in measurements of Ohm's law can have significant implications, depending on the purpose of the experiment. Inaccurate measurements can lead to incorrect conclusions and affect the validity of the results. This can also impact the reliability and reproducibility of the experiment, as well as the ability to make accurate predictions and draw meaningful conclusions. Therefore, it is important to minimize errors in measurements to ensure the accuracy and validity of the experiment.

How can we reduce errors in measurements of Ohm's law?

To reduce errors in measurements of Ohm's law, we can take several precautions such as using high-quality and calibrated instruments, minimizing human error by following proper measurement techniques, controlling environmental factors, and performing multiple trials to obtain a more accurate average. It is also important to understand the limitations of the law and ensure that it is applicable to the specific conditions of the experiment. Additionally, proper data analysis and error estimation techniques can also help to reduce errors in measurements.

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