High School Linearizing a Relation: How to Create a Best Fit Linear Line in Logger Pro

Click For Summary
To linearize the graph in Logger Pro, it is suggested to plot logarithmic transformations of the data, such as log(x) vs. y, log(x) vs. log(y), and x vs. log(y). For a more accurate linear estimator, a multiple linear regression can be performed using the data (x, x², y) to account for the expected nonlinear relationship. The auto-fit line in Logger Pro performs a linear regression on multiple data points, which may be unnecessary but provides a more accurate fit. The discussion also touches on the concept of finding a linearizable function that fits the data, resembling an inverted parabola. Overall, the focus is on utilizing regression techniques to achieve a best-fit linear line for the given dataset.
ChanYoung Park
lq5ySf3.png

Hi! Above is a screenshot of logger pro that I'm currently using.
I need to linearise this graph and draw a best fit linear line but I have no clue how to do it. What should I do?
The table on the left shows the raw data. The first column is showing the values for x-axis, and the second is for y-axis. According to my hypothesis, the y-value should initially increase as x-value increases from 0; the y-value is then expected to decrease to zero as x-value increases further.
 

Attachments

  • lq5ySf3.png
    lq5ySf3.png
    20 KB · Views: 2,640
Physics news on Phys.org
Try taking the logarithm of the data and plotting them again to see if you can come up with a "linear" line.

So, plot:
  • log x vs. y
  • log x vs. log y
  • x vs. log y
From my experience, that's how you would "linearize" data.
 
If you really want a linear estimator, even though you say that logically it is nonlinear, then you can do a simple linear regression of the (x,y) data. There are many programs to do that.

If you want a linear regression estimator that is not linear, but accounts for the logical relationship, then you can do a multiple linear regression of the data (x, x2, y). I think you will be more satisfied with the result and it will account for the logical relationship that you believe.

The auto-fit line in your graph has done a linear regression on the data (x, x2, x3, x4, x5, y). I think that goes too far and may be unnecessary, but it is more accurate. Here is what I get from y = 40.79166667 +0.67583333*x -0.01772619 *x2.
R_multLinReg.png
 

Attachments

  • R_multLinReg.png
    R_multLinReg.png
    2.4 KB · Views: 1,077
Last edited:
Let me see if I understood what you are looking for, please let me know : You want to find f with (y,f(x)) ( where {(y,x)} is your given data ) so that the plot {(y,f(x))} will be linearizable in the sense that it will pass some measure of linear goodness of fit? The plot seemed a bit like an inverted parabola , i.e., ## y-y_0 =c\sqrt x ##, so I considered the linearization ##(x, y_0+c\sqrt x)##. Am I on the right track (finding the right values for ##y_o, c## )?
 
Hey, sorry if I complicated things unnecessarily ; wouldn't be the first time :( . Please
ask a question if so.
 
I am studying the mathematical formalism behind non-commutative geometry approach to quantum gravity. I was reading about Hopf algebras and their Drinfeld twist with a specific example of the Moyal-Weyl twist defined as F=exp(-iλ/2θ^(μν)∂_μ⊗∂_ν) where λ is a constant parametar and θ antisymmetric constant tensor. {∂_μ} is the basis of the tangent vector space over the underlying spacetime Now, from my understanding the enveloping algebra which appears in the definition of the Hopf algebra...

Similar threads

  • · Replies 4 ·
Replies
4
Views
2K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 9 ·
Replies
9
Views
4K
  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 14 ·
Replies
14
Views
3K
Replies
2
Views
2K
Replies
28
Views
4K
  • · Replies 5 ·
Replies
5
Views
4K
  • · Replies 1 ·
Replies
1
Views
3K
  • · Replies 9 ·
Replies
9
Views
4K