• Exploratory Data Analysis
- This unit begins with calculating a particular statistic
for a sample (to make some inference about a population) and then to
think about how that statistic might differ from sample to sample, so
we go all the way back to basic exploratory data analysis.
- Since this unit forces students to examine different types
of distributions (sample, population and sampling distributions),
students must revisit examining shapes, centers and spreads.
- As stressed in previous units, it is crucial to label and
title graphs properly so not to confuse a certain type of distribution
- This unit uses models in different ways. For one, the
Central Limit Theorem is built around the idea that the sampling
distribution of the mean (for sufficiently large, independently drawn
samples) follows the normal model.
- Simulation models are another important part of this unit.
It is important to simulate several samples to illustrate the idea
behind sampling distributions.
- This unit will be students’ first taste of what is involved
when making formal inference about a population given a sample, so
remind them of this end goal. It is important to foreshadow to students
that sampling distributions (i.e. how our estimators might vary from
sample to sample) are crucial in determining how far an estimate
(sample statistic) differs from what we might expect under a given
hypothesis and what this ultimately means.