Courses in statistics often dive right into some of the topics you’re going to see in this module. But I find it much more timely to bring them at this point, now that you’ve got intuition, key concepts, and foundational frameworks in hand.
Here, we’re going to look at the following::
- Populations and samples
- Data and scales (or levels) of measurement
- The disciplines of probability, inferential statistics, descriptive statistics, and estimation
- Another look at estimation from the perspective of the various disciplines
My own notes and the desired learning outcomes
I assume you’ll be building your own “perfect set of notes” as you go through this material – to make sure you have a succinct reference to help you be efficient on a test or in your future work. So, I’m sharing my own notes here with you to help with that.
I’m also sharing the desired outcomes. As in our previous modules, there aren’t cast as true learning outcomes, despite the title. Rather, I want you to think of the second document below as a list of questions and actions you can use to check yourself as you go through the learning process.
The self-assessment problems
As you know, my practice in a course like this is to share with you two sets of problems so you can assess your understanding of the material. After the lessons below you’ll find some conceptual self-assessment problems to help you make sure you’ve got the fundamental concepts down. You’ll also find some applied self-assessment problems so you can round things out by putting your new knowledge to work.
So, dig into these problems either after you’ve finished looking at the material or as you go. Hustle to figure out the answers. Leave no stone unturned. Get help if you need it. Like the notes, I’m going to assume you’re building the “perfect set of reference problems” to help you be efficient in a test or to look back at in practice.
Okay, now get started by clicking through the lessons below!