## A.2 Course objectives: what is this course for?

### A.2.1 The big picture

This course is called “Intermediate Statistics,” which is…pretty vague, but here we are. What this really means is that we’ll be doing a combination of things:

• Re-seeing tools or concepts you’ve met in previous courses so that you become really comfortable using them
• Going deeper into how (or even why) those tools and concepts work
• Seeing some more advanced variations or extensions

The goal is that at the end of this course you will be prepared both to succeed in a follow-up stats course (whether it’s a core course or a pre-core elective) and to use some core statistical tools out in the field. That means you can:

• Use some basic statistical tools with facility – you’re comfortable enough with the tool itself that you can focus on the specifics of your application
• Tell when it’s appropriate to use those tools
• Talk about why a particular tool is or isn’t appropriate for a given application
• Work with a boss or colleague who wants to use more sophisticated or specialized tools, or a variation on something you’ve seen before

That last one is partly about getting a first look at advanced topics, but it’s just as much about building up your statistical communication skills – being able to talk about what you know and don’t know, and relate what someone is telling you to what you’ve already learned.

### A.2.2 Specific objectives

The key statistical tools we’ll work with in this course are divided into 6 chunks, or modules, each of which involves a set of learning targets. You can see the list of modules below; you’ll get more details about what each one involves as we get closer to it.

You’ll build your application skills by working on an analysis of your own dataset in the Project, and your communication skills by interacting with your classmates (and me!) throughout the semester, both formally and informally.

The six modules are:

• Data and Descriptives
• Regression Round 1: relating two (or more!) variables
• Probability
• Inference Round 1 (Permutation)
• Inference Round 2 (Classical)
• Regression for Realsies (now with inference!)

There’ll be a bit of extra time at the end of the course, which we’ll use for a combination of additional topics (not covered by Target Assessments, though possibly on the Final Check), review, and project presentations.

Let me take this moment, though, to remind you that I’m neither omniscient nor omnipotent. Things might change :)