Section 1.4
Decompositions
In this section we will see many important uses of the identity
Example. A fair coin is flipped 5 times. Calculate the probability that it lands tails at least 3 times.
and
means
#
Complements
Fact. Let be an event in a probability space and its complement.
Then
Proof.
Example. 4 fair dice are rolled. Calculate the probability that we get at least one pair of doubles.
look at complement
Birthday Problem:
Inclusion-Exclusion
Example. In a given country, 20% of the population owns a cat, 25% owns a dog, and 5% owns one of each.
What is the probability that a person chosen uniformly at random from this country owns neither a cat nor a dog?
owns neither =
Side Note de Morgan's Law
Also says
Monotonicity
Fact. If , then
Proof.
Example. Suppose that out of the total North American population, 50% of the
people have at some point in their lives visited the US, 30% has visited Texas, and 40% has visited California. Knowing only this information, what is the smallest possible percentage of the North American population that has visited both Texas and California? And largest?
Smallest is 20%
30% is largest
Section 2.1
Conditional Probability
Example.
Your friend rolls a pair of fair dice. What is the probability that the sum is 10?
“sum is 10”
Your friend rolls a pair of fair dice and tells you that the sum is a 2 digit number. Now what is the probability that the sum is 10?
Your friend rolls a pair of fair dice and tells you that one of the rolls is a 6. What is the probability that the sum is 10?
answer not