Graphing Example 5

I’ve taken the data from the first graphing example and tweaked it by changing the solution mass for 10.00 mL. This was a sample of water and so the density, which can be determined from the slope, should be close to 1 g/mL. You can see what only one outlier does to the slope (note- without going into detail, any outliers at the ends can have a drastic effect on the slope of the best-fit line).

If you check the “Exclude" checkbox, a new best-fit line is drawn and a new value for both the slope and y-intercept will be displayed. Not only is the slope now closer to 1 g/mL but the y-intercept is now closer to 0 g which is what is expected- a solution volume of 0 mL should result in a solution mass of 0 g.

 Volume (mL) Mass (g) Exclude 10.00 29.787 20.00 20.145 30.00 30.138 40.00 39.855 50.00 49.753 60.00 60.178 70.00 70.159 80.00 80.164 90.00 90.352 100.00 100.286
Uses the JSXGraph JavaScript libraries.

So, when can you exclude data?

That’s not an easy question to answer. At this point in your academic career, the experiments are typically written with results that are easily evaluated by your instructor/professor. Experience and an understanding of the relationship(s) between the x and y variables often allow us to know when any piece of data is an outlier.

What you need to be wary of in the future is declaring a result to be an outlier because it either doesn’t fit your expectations or because excluding it results in what you believe will be a better grade. At this stage in your lab science instruction, it is very likely that you will be given instructions to follow as to when to exclude a piece of data. And if you are still unsure, go ask your instructor!