The Addition Rule states that the probability of the union of two events A and B is given by:
P(A or B) = P(A) + P(B) - P(A and B)
P(A and B) represents the probability of both events A and B occurring simultaneously.
The Multiplication Rule states that the probability of the intersection of two independent events A and B is given by:
P(A and B) = P(A) × P(B)
If the events are dependent, the formula becomes:
P(A and B) = P(A) × P(B|A)
The Complementary Rule states that the probability of an event A not occurring is given by:
P(A’) = 1 - P(A)
Here, A’ represents the complement of event A.
Conditional Probability is the probability of an event occurring given that another event has already occurred.
It is denoted as P(A|B), where A and B are events.
The formula for conditional probability is given as:
P(A|B) = P(A and B) / P(B)
Bayes’ Theorem allows us to update the probability of an event based on new information.
It is given by the formula:
P(A|B) = (P(B|A) × P(A)) / P(B)
Bayes’ Theorem is widely used in data analysis, statistics, and machine learning.
A fair six-sided die is rolled. Find the probability of getting an even number.
A bag contains 4 red balls and 6 blue balls. A ball is drawn at random. Find the probability of drawing a red ball.