
So, the first one is random. We assign a unique number, 1 to n, to each object.

And then, we randomly choose numbers from 1 to n. This results in every object

in the population having an equal chance of being selected. The second one's

kind of tricky. We don't know for sure if it's random because we don't know if

we randomly assigned a unique number. We have to make sure that if we're going

to give each object a number, it has to be randomly assigned, and each object

has the same chance of getting any number. This one, we took care of that. We

randomly assigned a unique number, and then we chose multiples of six. Now, this

choosing multiples of six might throw you off a little bit, because that doesn't

seem random. But, every object in the population has an equal chance of being a

multiple of 6. And finally, this one is also random. This one's actually called

stratified sampling, because we divided the population into subpopulations, in

this case two. And, every object in the population had an equal chance of being

in either one of the subpopulations. And then, random samples were taken from

each group, so this is also random. So yay, you're done with Lesson 1. Moving

onto the problem set.