The least square regression line is one where, given a set of data (which do not necessarily lie on a straight line as in this case)
the {sum of [the squares of (the vertical distances from the data points to the line)]} is a minimum,
where your vertical axis is your dependent variable (displacement), and your horizontal axis is your independent variable (angle). So, geometrically, we interpret that it basically gives you a line with a snug fit to your data points.
If I am not wrong, go to
- [2nd] [STAT] to bring up the editor of your lists. Put your values of x into column L1. Put your corresponding values of y into column L2.
- then play around with [STAT] I think... find a function called LinReg(a+bx). Punch that in, then use [2nd] [1] and [2nd] [2] to get call out your L1/L2 variables. The syntax is: LinReg(a+bx) L2 L1.
Notes:
- The calculator will give you an equation in the form y = mx+c, where b is your gradient, a is your y-intercept.
- In this case, LinReg(a+bx) L2 L1 because you are finding the regression line of y on x (L2 on L1).
- There's also a LinReg(ax+b) if I recall. Don't get mixed up, but it works just as well but a and b and/or L1 and L2 have swapped their roles or something. Play around a little and you'll figure out how to use both. I can't describe further without a graphing calculator with me.
Hope this helps.