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

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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.
 

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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
 

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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.
 
Thread 'How to define a vector field?'
Hello! In one book I saw that function ##V## of 3 variables ##V_x, V_y, V_z## (vector field in 3D) can be decomposed in a Taylor series without higher-order terms (partial derivative of second power and higher) at point ##(0,0,0)## such way: I think so: higher-order terms can be neglected because partial derivative of second power and higher are equal to 0. Is this true? And how to define vector field correctly for this case? (In the book I found nothing and my attempt was wrong...

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