Normalizing Graph in Excel: Step by Step Guide

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To normalize a graph in Excel representing counts versus distance, the user needs to establish a best fit line for the background data. The process involves determining the equation of this line, which can be achieved through various methods such as single-line best fit or crossed lines for uncertainty analysis. Normalization typically requires subtracting the mean from the data values and dividing by the standard deviation. Additional resources are available for detailed guidance on these methods. A clear step-by-step approach will help in achieving the desired normalization effectively.
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Dear all ,

Thank you for any help in advance.

I have a graph in Excel which represents counts (photons) vs pixel i.e distance. This is from a cross sectional profile of an X-ray image.

I would like to normalise my graph with respect to my background. I have attached the graph. I know I have to draw a best fit line on my background and find the equation but a step by step process would be relly helpful.

Thank you
 

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Welcome to PF;
It is tricky to figure what you want because you say "normalize" at first, then you want "best fit line" later... which is it?
It would have help if you showed us what you have tried or how you are thinking about what is needed.
Anyway - off your post, I can suggest the following howto's. You can tell us which was right for you, and, if you get stuck, where.

Single-line 'best fit" method.
http://serc.carleton.edu/mathyouneed/graphing/bestfit.html

Crossed lines method - good if you need uncertainties.
http://www.schoolphysics.co.uk/age16-19/General/text/Uncertainties_in_graphs/index.html

Normalizing curves in excel;
http://www.ehow.com/how_12167214_normalize-curves-excel.html
 
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This appears to be an incomplete Beta function. You have to find its parameters from data, setting the truncation value ahead from experience. Normalization means subtraction of mean from the values and then division by sd.
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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