High School How can I normalize these values to sum 1?

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To normalize a series of view factors so that their sum equals 1, multiply each value by a constant k. This constant k can be calculated by taking the reciprocal of the current sum, which is approximately 0.996221. Thus, k is determined as 1 divided by 0.996221. Applying this method will adjust the values proportionally, ensuring their total equals 1. This approach is a standard normalization technique in data processing.
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I have calculate a serie of view factors for a given geometry and its sum is aproximately one but not exactly.
My values are:
0,1134 0,1307 0,2446 0,12393 0,115053 0,010084 0,007334 0,1071 0,0145 0,0128 0,0919 0,01675 0,00463 0,00344

The sum now is equal 0,996221
How can I normalize the following values that the sum is equal 1? Which method should I use?
 
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Well, the usual way would be to multiply all your numbers by (the same) constant ##k##, and then computing the sum (now as a function of ##k##) and equaling to 1 you have an equation for ##k##. You solve it and you will have an answer for what value of ##k## normalizes the sum.
 
k=1/.996221
 
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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