Linear Regression Error on Excel

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To determine the uncertainties associated with the coefficients a and b from linear regression in MS Excel, users can calculate these values manually by setting up the appropriate equations in the spreadsheet. Resources like the Wolfram MathWorld website provide equations for standard errors related to least squares fitting. While Excel's statistics pack may not directly provide uncertainty values, users can assess the goodness of fit by displaying the R-squared value on their chart, which can be enabled through the Format Trendline options. This R-squared value offers insights into the model's fit quality. For a more comprehensive understanding of data fitting and uncertainty, the textbook "Data Fitting and Uncertainty" is recommended as a practical introduction to the topic.
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This is for an experimental physics homework,I am using the latest version of MS Excel. I have a set of data, I perform linear regression on them and it gives me a line y=ax + b.

Given that both a and b have physical significance I would like to know how could I know the uncertainty associated with both a and b, so I could get meaningful results.

How do I do this? The statistics pack seems to give everything but these values.

Any help would be appreciated.
 
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Uncertainty with regards to coefficients of a model fit to data can be inferred from so called "Goodness of Fit" measures. There are many different ways to approach this however, since you are using Excel, the easiest for way for you is to have Excel display the R2 value. You can do this by either right clicking on your line, selecting Format Trendline and checking off the box next to "Display R-squared value on chart." If you are fitting the line then the same dialog box will have that option on the bottom. Interpretation of the value is fairly straight-forward, a quick Google search will gives lots of discussions.
 
useful textbook

There is a useful textbook explaining things in an introductory manner. Have a look at: "Data Fitting and Uncertainty (A practical introduction to weighted least squares and beyond)", ISBN 978-3-8348-1022-9.
 
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