How Do You Represent Error in a Log-Log Magnetic Field vs. Distance Graph?

In summary, when plotting lnField against lnDistance for an investigation on the variation of Magnetic Field with Distance, you should represent the error in the magnetic field by plotting a range that includes the error. The units on the axis for lnDistance and magnetic field should be ln(metres) and lnTesla respectively, as this is consistent with plotting a quantity against (time) squared.
  • #1
dfx
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Hi,
I'm investigating the variation of Magnetic Field with Distance. The investigation requires me to plot lnField against lnDistance. My question is, say the quantities had an error of +/- 0.01 mT for the Magnetic Field. How would I represent this on the ln Field v/s ln Distance graph? I intuitively thought it would be ln0.01 but that was obviously wrong as it gave a value of -4.6 ... . Any suggestions?

Also, when sketching lnDistance on the axis, should I make the units on the axis ln(metres) or just metres? And likewise with magnetic field should it be lnTesla or just Tesla? I ask this because when plotting say a quantity against (time) squared, we use the units of (s^2).

Any help very much appreciated. Cheers.
 
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  • #2
For the error in the magnetic field, you would need to plot a range on the graph that includes the error. For example, if the magnetic field value is mT, then the range for the graph would be from ln(mT-0.01) to ln(mT+0.01). When plotting lnDistance on the axis, you should use ln(metres). Likewise, when plotting magnetic field you should use lnTesla. This is because when plotting a quantity against (time) squared, the units are (s^2).
 
  • #3


Hello,

First of all, it is important to note that when plotting lnField against lnDistance, you are essentially plotting the logarithm of the Magnetic Field and Distance values. This means that the error bars on your graph should also be represented as logarithms, rather than the actual values. In this case, the error of +/- 0.01 mT would be represented as ln(0.01) = -4.605 on the y-axis. This is because the logarithm of a product is equal to the sum of the logarithms of the individual factors. Therefore, ln(0.01 mT) = ln(0.01) + ln(mT) = -4.605 + ln(mT).

As for the units on the axis, it is common practice to use the same units as the quantity being plotted. In this case, the units on the y-axis would be ln(mT) and the units on the x-axis would be ln(m). This is because the logarithm of a unit is still a unit. So, ln(mT) is still a unit of magnetic field and ln(m) is still a unit of distance.

I hope this helps clarify any confusion. Best of luck with your investigation!
 

FAQ: How Do You Represent Error in a Log-Log Magnetic Field vs. Distance Graph?

1. What is basic error analysis?

Basic error analysis is a method used in science to identify and quantify errors in experimental data. It involves analyzing the sources of error and determining their impact on the final results.

2. Why is error analysis important in scientific research?

Error analysis is important in scientific research because it helps to ensure the accuracy and reliability of experimental data. By identifying and quantifying errors, scientists can improve their methods and draw more accurate conclusions.

3. What are the types of errors in basic error analysis?

The types of errors in basic error analysis include random errors, systematic errors, and human errors. Random errors are caused by chance and can be reduced through repeated measurements. Systematic errors are caused by a flaw in the experimental setup and can be corrected by adjusting the equipment or method. Human errors are mistakes made by the researcher and can be minimized through careful attention to detail.

4. How do you calculate the total error in basic error analysis?

The total error in basic error analysis is calculated by adding together all the individual errors, both random and systematic. This can be done using various statistical methods, such as root mean square error or standard deviation.

5. How can basic error analysis be used to improve experimental results?

Basic error analysis can be used to improve experimental results by identifying and minimizing sources of error. By understanding the types and magnitudes of errors, scientists can make adjustments to their methods and equipment to improve the accuracy and precision of their data. This can lead to more reliable and meaningful conclusions.

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