Why Use the Least Squares Method for Finding Slope?

AI Thread Summary
The least squares method is preferred for finding the slope because real-world data often includes measurement errors and does not align perfectly on a straight line. This method provides the best average fit to all data points, rather than relying on just two points, which is typical of standard slope formulas. By considering all available data, least squares helps to derive a more accurate representation of the relationship between variables. This approach is essential when dealing with multiple data points to ensure a comprehensive analysis. Understanding this method clarifies its importance in statistical modeling and data fitting.
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Homework Statement



why did you use the least squares method for finding m, rather than the standard slope formula?

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The Attempt at a Solution



I am totally confused about why you have to use the least squares method
 
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In the real world the measured values don't line on a perfect stright line - due to experimental and measurement errors.
The least squares fit gives the best 'average' fit to the data.
 
In addition to mgb's post, you may well have more then 2 points to fit your line to. Since you are attempting to find the line which best approximates ALL of your data you need method which uses information from all of your data points. The normal methods for determining the parameters of a line use information from only 2 points.
 
ok thanks a lot! i think i understand it now
 

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