Data points and fitting functions

[Niles]

Homework Statement

I have made an experiment with a Hall-meter and I have got some data. I know have an expression on the form

$$B(a) = \mu IN\frac{{a^2 }}{{2\left( {a^2 + z^2 } \right)^{\frac{3}{2}} }}$$

where z = 0.02 m and a is my only parameter varyring. I have plottet these data, and I want to fit a regression to these. But which regression should I choose? The dependency is on the form

$$\frac{{a^2 }}{{\left( {a^2 + k^2 } \right)^{\frac{3}{2}} }}$$
where k is a constant. The data looks linear, but still - what regression should I choose here?

 

[Jhero]

Thank you for sharing your experiment and data. It seems like you have already identified the form of your data as a function of a, and you are wondering which regression method would be most appropriate for fitting a line to your data.

Based on the form of your data, it appears that a linear regression would be the most suitable method for fitting a line to your data. This method involves finding the best fit line that minimizes the sum of the squared differences between the observed data points and the predicted values from the line.

However, it is always a good idea to consider other factors such as the distribution of your data, the relationship between your independent and dependent variables, and any underlying assumptions of the regression method you choose. If you are unsure, I would recommend consulting with a statistician or using a statistical software package to help you determine the most appropriate regression method for your data.

I hope this helps and good luck with your experiment!