Normal Quantile Plots

Previously, we talked about z-scores and how they tell position on the Normal Curve - but how do we know if the data fits a Normal Distribution in the first place? A Normal Quantile Plot (also sometimes called a Normal Probability Plot) can give us an idea of Normality of the data. Put your data into L1, then go to Stat Plot and choose the very last graph. Don't forget to go to zoom - 9(stat) to get a good view of your graph!

We look for three characteristics of the data to assess Normality. 1) Does the data roughly form a straight line? Or do we see curvature at the ends? 2) Is the data centered about the x-axis - do we see roughly equal observations above and below? And finally, 3) Does a majority of the data appear to be in the middle of the graph rather than in the tails? If the answer to these 3 questions is "yes" then we can assume that our data appears roughly Normal.

Note that this is our ASSUMPTION based on our OBSERVATIONS. We didn't actually PROVE anything with our Normal Quantile Plot. Choose your language carefully when you interpret this.