I’m getting confused about how I’m supposed to find a line of best fit, and I’d really appreciate some clarity. In class, I was told to draw a straight line so that about half the points are above and half are below. But when I use the regression function on my calculator, the line is different, and the predictions don’t match what I’d estimate from my sketch. Which one counts as the “right” line if the instructions just say “line of best fit”? Should I always default to the calculator unless the question says to draw it by eye?
Here’s a simple dataset I’m practicing with: (1, 2), (2, 3), (3, 5), (4, 4), (5, 6). When I draw a line, it looks steeper to me than the one the calculator gives, and that changes the predicted value at x = 3 quite a bit. I’m not sure if my “half above, half below” approach is actually misleading me, or if I’m misunderstanding what “best” is supposed to mean in this context.
I also get stuck on outliers. If I add a point like (10, 50), my calculator’s line tilts a lot. Should I keep that point or drop it? How do I justify that decision if I’m writing up a solution? Are there clear steps I should follow to decide whether a point is an outlier that should be excluded, or whether it’s a legitimate extreme value that should stay in the analysis?
Another thing I’m unsure about is the intercept. Sometimes the fitted line has a negative y-intercept, which doesn’t make sense for the situation I’m modeling. Is it ever acceptable to force the line through the origin? If so, how do I decide when that’s appropriate and how do I explain that choice?
On tests, I’ve lost marks before because my hand-drawn line led to different estimates than the regression line. I thought getting a visually reasonable line was enough, but apparently not. If a question says “draw a line of best fit and estimate y for x = 3.5,” am I expected to compute the regression line first and then sketch that, or is a careful eyeball okay? Also, does the “equal points above and below” idea actually line up with the regression definition of best fit, or is that more of a rule of thumb that can go wrong?
One last detail: when the axes are scaled differently, my eyeballed slope changes. Is there a standard way to draw the line on paper so it matches the regression line more closely (for example, choosing two points on the fitted line rather than two data points)?
Could someone walk me through a practical, step-by-step way to handle this: check if a linear model is reasonable, decide what to do with outliers, choose whether to include an intercept or force through the origin, and then make and justify predictions? I’m trying to build a reliable checklist I can use so I don’t keep second-guessing myself.