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Apr 16, 2022 at 14:11 comment added whuber How do you measure or estimate them? This matters because it affects how one might analyze the situation and develop a solution.
Apr 15, 2022 at 21:34 comment added Axxxxx The errors are the accumulation of instrumental errors in observation. These are all mainly observational errors
Apr 15, 2022 at 18:17 comment added whuber It would be nice to know what the errors mean and how they are determined.
Apr 15, 2022 at 18:03 comment added Axxxxx I updated the problem and included the actual data. When it comes to the assumptions about the errors I'm not exactly sure since in both X and Y there are data points with high errors.
Apr 15, 2022 at 18:00 history edited Axxxxx CC BY-SA 4.0
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Apr 15, 2022 at 14:16 comment added EngrStudent Welcome to CV. The following link is step-by-step through bkm for linear fits using the R software tool. Personally I use R in the RStudio IDE, which are both free. It is a solid data-analysis tool. Link: learnbymarketing.com/tutorials/linear-regression-in-r
Apr 15, 2022 at 11:41 answer added frank timeline score: 1
Apr 15, 2022 at 9:59 comment added dipetkov The problem statement is not clear. Can you show with an example exactly what data you have for each data point and say what assumptions you are willing to make about these errors.
Apr 15, 2022 at 9:50 comment added Axxxxx I don't think its possible for me to ignore the error in x
Apr 15, 2022 at 9:26 comment added frank If you are willing to ignore the error in $x$, you could use en.wikipedia.org/wiki/Least_squares#Uncertainty_quantification
Apr 15, 2022 at 9:22 history edited Axxxxx CC BY-SA 4.0
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Apr 15, 2022 at 9:18 comment added Axxxxx Least square method
Apr 15, 2022 at 9:09 comment added Axxxxx Used excel to compute it and it doesn't add the error to the equation
Apr 15, 2022 at 9:03 comment added frank It depends on how you compute your "best fit line".
Apr 15, 2022 at 8:54 history asked Axxxxx CC BY-SA 4.0