You mix concepts from probability and statistics, I feel it would gain from sticking with either or separating them into different parts/books. Crucial probability concepts are relegated to the end (conditional probabilities, Baye's theorem, ...). The plan in general has some issues, I was surprised to see the expected value as one of the last chapters, and you actually reference it dozens of time before it's introduced. For example slide 15 which would mean nothing to someone who'd just started learning about probabilities.
I mostly skipped around but took some time to read carefully the chapters on random variables and PDFs because they're usually the hardest topics to grasp for a beginner (mostly because of the confusing notations). The "random variable" chapter doesn't introduce what a random variable is, it's about dependent and independent variables which are more relevant to the topic of statistical modeling. The PDF chapter is alright but I felt like it was stretching the limits of the lemur abstraction and in the end gave up on it entirely when you start talking about curves and area under the curve. Also, a couple of nitpicks purely about the form, but I don't understand why you refer to probability as "chance" throughout this chapter, and your numbers use too many decimals, you have the luxury of choosing your numbers you can use nice round ones expressed as percentages.
So to conclude, my advice would be: 1) Rework the plan 2) Drop the statistics 3) Restrain yourself to discrete probabilities. It's enough for the student to get an intuition of all important concepts and it will be much easier to explain. For example the chapter on PDFs could have been on PMFs and it would have been much easier to explain without compromising your abstraction.