Uploaded February 2024 | Updated September 2026, 2 hours ago
Title, essentially.
Properly interpreting normal QQ plots takes some experience, and part of that experience is developing a feel for what natural variability looks like on a normal QQ plot (when sampling from a normal distribution).
Each of these 1000 plots is based on a sample of size 25 from a normal distribution with a mean of 10 and standard deviation of 5. Created with R’s qqnorm and qqline.
Which one is your favourite? Mine are 225, 245, 314, and 330. I’m (kinda) joking. Or not.
Looking for more info about normal QQ plots? I have a full video description of them here:
youtu.be/X9_ISJ0YpGw?si=GCxWpESApUzyiqzO
I’ll try to get back to making real videos, folks. But I think this one is useful and I have places I'd like to use it.
Title, essentially.
Properly interpreting normal QQ plots takes some experience, and part of that experience is developing a feel for what natural variability looks like on a normal QQ plot (when sampling from a normal distribution).
Each of these 1000 plots is based on a sample of size 25 from a normal distribution with a mean of 10 and standard deviation of 5. Created with R’s qqnorm and qqline.
Which one is your favourite? Mine are 225, 245, 314, and 330. I’m (kinda) joking. Or not.
Looking for more info about normal QQ plots? I have a full video description of them here:
youtu.be/X9_ISJ0YpGw?si=GCxWpESApUzyiqzO
I’ll try to get back to making real videos, folks. But I think this one is useful and I have places I'd like to use it.

![An Introduction to the Geometric Distribution
An introduction to the geometric distribution. I discuss the underlying assumptions that result in a geometric distribution, the formula, and the mean and variance of the distribution. I work through an example of the calculations and then discuss the cumulative distribution function.
For those using R, here is the R code for the example in this video:
NB R uses a different definition of the random variable than I do here. I define the random variable X to be the number of trials required to get the first success. R defines the random variable to be the number of *failures* before getting the first success (lets call this Y). Then Y = X - 1, and well have to make this adjustment when using dgeom, pgeom, or rgeom. Some might find this confusing, and if you do, dont use these functions.
Sampling from a large population where 30% have CPR training until we get the first person with CPR training.
Finding the probability that it happens on the sixth person sampled:
(.3)*(.7)^5
[1] 0.050421
or
dgeom(6-1,.3)
[1] 0.050421
Finding the probability that it happens on or before the third person sampled:
.3+.3*.7+.3*.7^2
[1] 0.657
or
1-.7^3
[1] 0.657
or
pgeom(3-1,.3)
[1] 0.657 An Introduction to the Geometric Distribution](https://i.ytimg.com/vi/zq9Oz82iHf0/mqdefault.jpg)