1. ASV Chapters 1 - Sample Spaces and Probabilities
2 - Conditional Probability and Independence
3 - Random Variables
4 - Approximations of the Binomial Distribution
5 - Transforms and Transformations
6 - Joint Distribution of Random Variables
7 - Sums and Symmetry
8 - Expectation and Variance in the Multivariate Setting
9 - Tail Bounds and Limit Theorems
10 - Conditional Distribution
11 - Appendix A, B, C, D, E, F

2. Y = Exam Score X = Hours / Day studying 100 low probability 9080 70 60
low probability
Conditional distribution (of Y , given that X = 1)
high probability
low probability
Conditional expectation (of Y , given that X = 1)

3. X = Hours / Day studying
Y = Exam Score
100 90 80 70 60

5. “Conditional pmf” of X, given that Y = y
Def:
“Conditional pmf” of X, given that Y = y

6. “Conditional pmf” of X, given that Y = y
Def:
“Conditional pmf” of X, given that Y = y
Def:
“Conditional expectation” of X, given that Y = y

7. “Conditional pmf” of X, given that Y = y
Def:
“Conditional pmf” of X, given that Y = y
Def:
“Conditional expectation” of X, given that Y = y