Khan Academy

Compound probability of independent events (K-A)

Dependent probability introduction (K-A)

General multiplication rule example: dependent events (K-A)

Addition rule for probability (K-A)

General multiplication rule example: independent events (K-A)

Probability with Venn diagrams (K-A)

Independent events example: test taking (K-A)

Deriving the variance of the difference of random variables (K-A)

Example: Analyzing distribution of sum of two normally distributed random variables (K-A)

Example: Analyzing the difference in distributions (K-A)

Intuition for why independence matters for variance of sum (K-A)

Mean of sum and difference of random variables (K-A)

Variance of sum and difference of random variables (K-A)

Constructing a probability distribution for random variable (K-A)

Probability with discrete random variable example (K-A)

Theoretical probability distribution example: multiplication (K-A)

Theoretical probability distribution example: tables (K-A)

Valid discrete probability distribution examples (K-A)

10% Rule of assuming "independence" between trials (K-A)

Binomial probability example (K-A)

Binomial variables (K-A)

Binompdf and binomcdf functions (K-A)

Free throw binomial probability distribution (K-A)

Generalizing k scores in n attempts (K-A)

Graphing basketball binomial distribution (K-A)