The document discusses graphical presentation and determining the best line of fit for data points. It explains that the simplest relationship between variables is linear and can be represented by the equation y=mx+b. For non-linear data points, the best fitting line minimizes the total distance to all points using the method of least squares, where the line that has the least sum of squared deviations is considered the best fit. The slope and y-intercept of this line can then be determined.

1. Graphical Presentation

2. Graphical Presentation
• The simplest relation between physical quantities is
linear.
• The equation of the line can be determined from two
parameters, its slope and the constant y-intercept:
y=mx + b
• The line can be determined if the set of points, or
measurements, follow a linear relationship.
• How about we have a non linear set of points?

4. Line of best fit
• The assumption here is that the best fitting line has the
least distance from all the points at once.
• Legendre stated the criterion for the best fitting curve to
a set of points.
• The best fitting curve is the one which has the least sum
of deviations from the given set of data points (the
Method of Least Squares).
• More precisely, the curve with the least sum of squared
deviations from a set of points has the best fit.

5. From this principle the slope and the y-
intercept are determined as follows

6. The lab
report