Stats3
Question 1: Probability of Students in Subjects
Given:
40% of students study both maths and science.
60% study maths.
Required:
Calculate the probability of a student studying science given they are studying maths.
Formulas used:
Conditional Probability: P(A|B) = P(A and B) / P(B)
The solution:
P(Math and Science) = 40% = 0.4
P(Math) = 60% = 0.6
Therefore, P(Science | Math) = P(Math and Science) / P(Math) = 0.4 / 0.6 = 2/3.
Question 2: Rolling a Die and Tossing a Coin
Perform actions: Roll a die and toss a coin.
Required:
Calculate the probability that the die shows an odd number and the coin shows heads.
Definitions:
Independent Events: When the outcome of one event doesn't affect the other.
The solution:
P(Die shows odd) = 3/6 (1, 3, 5 are odd)
P(Coin shows heads) = 1/2 (only one head)
Therefore, P(Odd and Heads) = P(Odd) * P(Heads) = (3/6) * (1/2) = 1/4.
Recap of Previous Concepts
Bayes' Theorem
Overview of Bayes' theorem discussed in previous class.
Application of Bayes' theorem to examples.
Formula: P(A|B) = P(B|A) * P(A) / P(B)
Explained the understanding of 'at least' and 'at most'.
"At least" means greater than or equal to; "At most" means less than or equal to.
Detailed Example using Bayes' Theorem
Example: A bag contains 3 coins (2 fair, 1 fake).
Task: Find probability that the coin chosen was fake given that it showed heads.
Solution steps:
Identify outcomes:
P(Head|Fake) = 1 (always shows head)
P(Fake) = 1/3
P(Head) calculated using law of total probability.
Tree Model used to calculate - P(Head) from both fair and fake coins.
Final probability calculated as 1/2.
New Questions for Practice
Card Example:
There are 3 types of cards with different color distributions:
1 card with red on both sides.
1 card with black on both sides.
1 card with red on one side, black on the other.
Query: If one side of the picked card is red, what is the probability it's the red-black card?
Advanced Problem on Email Detection
Scenario: Emails received are classified as spam with certain detection rates.
Given that an email was detected as spam, find the probability that it was not spam.
Relevant Probabilities:
50% chance of receiving a spam email.
99% accuracy in detecting actual spam.
5% chance of a non-spam email being incorrectly flagged.
Application of Bayes' theorem to calculate probability.
Addition of Probabilities
Concept clarification:
Probability of A or B: P(A or B) = P(A) + P(B) - P(A and B).
Example using cards to demonstrate how to correctly calculate probabilities when events overlap.