CHAPTER 3: UNCERTAINTY
Liability in insurance: refer to the obligation an insurer has taken on to
pay benefits to policyholders sometime in the future.
RISK AS THREAT:
All companies face risk
Risk associated with the operation of the company are referred to as
threat, they are bad for the company.
RISK AS OPPORTUNITY:
See other peoples risk
as an opportunity
to do business e.g.
insurance companies
I/C created
specifically
to help people
manage risk
, HOW DO WE MEASURE UNCERTAINTY?
The probability of an event =
The number of outcomes which fall into thedefinition of our event
The total number of possible outcomes
Example:
What is the probability of drawing a queen out of a shuffled deck of 52
cards?
1
=
13
ELEMENTARY PROBABILITY:
o Random Variable: (X) the outcome of a particular event
Discrete random variables: quantities of X which can take on one of a
finite or countably infinite number of values, and do so with probabilities
that add up to 1.
o Probability: a number between 0 and 1 indicating how likely a particular
event is.
o Sample space of X: the set of possible outcomes for a random variable X
o Example:
The probability that the die will roll a number of three or above can be
calculated as follows:
Pr(X ≥ 3) = P(X = 3) + P(X = 4) + P(X = 5) + P(X = 6)
= 1/6 + 1/6 + 1/6 + 1/6
= 2/3
PROBABILITY MASS FUNCTION:
Liability in insurance: refer to the obligation an insurer has taken on to
pay benefits to policyholders sometime in the future.
RISK AS THREAT:
All companies face risk
Risk associated with the operation of the company are referred to as
threat, they are bad for the company.
RISK AS OPPORTUNITY:
See other peoples risk
as an opportunity
to do business e.g.
insurance companies
I/C created
specifically
to help people
manage risk
, HOW DO WE MEASURE UNCERTAINTY?
The probability of an event =
The number of outcomes which fall into thedefinition of our event
The total number of possible outcomes
Example:
What is the probability of drawing a queen out of a shuffled deck of 52
cards?
1
=
13
ELEMENTARY PROBABILITY:
o Random Variable: (X) the outcome of a particular event
Discrete random variables: quantities of X which can take on one of a
finite or countably infinite number of values, and do so with probabilities
that add up to 1.
o Probability: a number between 0 and 1 indicating how likely a particular
event is.
o Sample space of X: the set of possible outcomes for a random variable X
o Example:
The probability that the die will roll a number of three or above can be
calculated as follows:
Pr(X ≥ 3) = P(X = 3) + P(X = 4) + P(X = 5) + P(X = 6)
= 1/6 + 1/6 + 1/6 + 1/6
= 2/3
PROBABILITY MASS FUNCTION: