- This unit centers around probability and, like the previous
unit, it stresses the importance of how the probabilities associated
with values in a random experiment (which we model using pdfs in this
unit) differ from the relative frequencies of values found within
populations (which we’ve modeled using mathematical functions in
- Simulating probabilities is also a very powerful tool and
is referenced briefly in this unit. The following unit will expand more
on this topic.
- Many probability models are referenced in this unit, but a
lot of the examples you might use will refer back to the normal model
in particular. This model will continuously be referenced in future
- As discussed in earlier units, one can identify what
distribution is appropriate to model a situation by describing
distributional characteristics such as the shape, center and spread.
- As referenced in the previous unit, modeling probabilities
is a key piece to statistical inference to determine whether a sample
statistic is typical or unusual under the assumption of a particular
model. This unit attempts to make this connection more clear and in
future units, this point will be even more explicit.