1. Chapter 1
Basics of Probability

2. Chapter 1 – Basics of Probability
Introduction
Basic Concepts and Definitions
Counting Problems
Axioms of Probability and the Addition Rule
Conditional Probability and the Multiplication Rule
Bayes’ Theorem
Independent Events

3. 1.1 - Introduction
Probability deals with describing random experiments
An activity in which the result is not known until it is performed
Most everything in life is a random experiment
Informally
Probability is a measure of how likely something is to occur
Probabilities take values between 0 and 1

4. 1.2 – Basic Concepts and Definitions
An outcome is a result of a random experiment
The sample space of a random experiment is the set of all possible outcomes
An event is a subset of the sample space
Notation
Events are typically denoted by capital letters
P(A) = probability of event A occurring

5. Calculating Probabilities
Relative Frequency Approximation: To estimate the probability of an event A, repeat the random experiment several times (each repetition is called a trial) and count the number of times the event occurred. Then
The fraction on the right is called a relative frequency.

6. Calculating Probabilities
Theoretical Approach: If all outcomes of a random experiment are equally likely, S is the finite sample space, and A is an event, then where n (A) is the number of elements in the set A and n (S) is the number of elements in the set S.

7. Relation
Law of Large Numbers: As the number of trials gets larger, the relative frequency approximation of P (A) gets closer to the theoretical value. A probability is an average in the long run.

8. Example Bag with 4 blue, 3 red, 2 green, and 1 yellow cube
Question: “How likely is it that we get a green cube?”
Let G = event we get a green cube
We want to know P(G)

9. Relative Frequency
Choose a cube, record its color, replace, and repeat 50 times
Student 1 Student 2

10. Theoretical Approach
Let
then

11. Law of Large Numbers Combine students’ results 50 + 50 = 100 trials
= 19 green cubes

12. Example 1.2.3 Roll two “fair” six-sided dice and add the results
Let A = event the sum is greater than 7
Find P(A)

13. Sample Space

14. Random Variable Let X = the sum of the dice Called a random variable
Table is called the distribution of X

15. Subjective Probabilities
Subjective Probabilities: P(A) is estimated by using knowledge of pertinent aspects of the situation

16. Example 1.2.4 Will it rain tomorrow? Let then
since the outcomes are not equally likely

17. Example 1.2.4
What is P(A)?
Weather forecasters use knowledge of weather and information from radar, satellites, etc. to estimate a likelihood that it will rain tomorrow

18. Example 1.2.5 Suppose we randomly choose an integer between 1 and 100
Let
then

19. Example 1.2.5 Suppose we randomly choose any number between 1 and 100
Let
then a reasonable assignment of probability is

20. 1.3 – Counting Problems
Fundamental Counting Principle: Suppose a choice has to be made which consists of a sequence of two sub-choices. If the first choice has n1 options and the second choice has n2 options, then the total number of options for the overall choice is n1 × n2.
Definition Let n > 0 be an integer. The symbol n! (read “n factorial”) is
For convenience, we define 0! = 1.

21. Permutations
Definition Suppose we are choosing r objects from a set of n objects and these requirements are met:
The n objects are all different.
We are choosing the r objects without replacement.
The order in which the choices are made is important.
Then the number of ways the overall choice can be made is called the number of permutations of n objects chosen r at a time.

22. Combinations
Definition Suppose we are choosing r objects from a set of n objects and these requirements are met:
The n objects are all different.
We are choosing the r objects without replacement.
The order in which the choices are made is not important.
Then the number of ways the overall choice can be made is called the number of combinations of n objects chosen r at a time.

23. Arrangements
Definition Suppose we are arranging n objects, n1 are identical, n2 are identical, … , nr are identical. Then the number of unique arrangements of the n objects is

24. Example 1.3.7
A local college is investigating ways to improve the scheduling of student activities. A fifteen-person committee consisting of five administrators, five faculty members, and five students is being formed. A five-person subcommittee is to be formed from this larger committee. The chair and co-chair of the subcommittee must be administrators, and the remainder will consist of faculty and students. How many different subcommittees could be formed?

25. Example 1.3.7 Two sub-choices: Number of choices:
Choose two administrators.
Choose three faculty and students.
Number of choices:

26. Binomial Theorem
Example

27. 1.4 – Axioms of Probability and the Addition Rule
Axioms of Events Let S be the sample space of a random experiment. An event is a subset of S. Let E be the set of all events, and assume that E satisfies the following three properties:

28. 1.4 – Axioms of Probability and the Addition Rule
Axioms of Probability Assume that for each event 𝐴∈𝐸, a number P(A) is defined in such a way that the following three properties hold:

29. Theorem 1.4.2

30. Theorem 1.4.3

31. The Addition Rule Example 1.4.4: Randomly choose a student
E = event student did not complete the problems
F = event student got a C or below

32. Example 1.4.4

33. 1.5 – Conditional Probability and the Multiplication Rule
Definition The conditional probability of event A given that event B has occurred is

34. Example 1.5.2
If we randomly choose a family with two children and find out that at least one child is a boy, find the probability that both children are boys.
A = event that both children are boys
B = event that at least one is a boy

35. Example 1.5.2 A = event that both children are boys
B = event that at least one is a boy

36. Multiplication Rule
Definition The probability that events A and B both occur is one trial of a random experiment is

37. Example 1.5.4
Randomly choose two different students. Find the probability they both completed the problems
A = event first student completed the problems
B = event second student completed the problems

38. Example 1.5.4

39. 1.6 – Bayes’ Theorem
Definition Let S be the sample space of a random experiment and let A1 and A2 be two events such that The collection of sets {A1, A2} is called a partition of S.

40. 1.6 – Bayes’ Theorem
Theorem If A1 and A2 form a partition of S, and B ⊂ S is any event, then for i = 1, 2

41. Example 1.6.1
Medical tests for the presence of drugs are not perfect. They often give false positives where the test indicates the presence of the drug in a person who has not used the drug, and false negatives where the test does not indicate the presence of the drug in a person who has actually used the drug. Suppose a certain marijuana drug test gives 13.5% false positives and 2.5% false negatives and that in the general population 0.5% of people actually use marijuana. If a randomly selected person from the population tests positive, find the conditional probability that the person actually used marijuana.

42. Example 1.6.1
Define the events
Note that {U, NU} forms a partition.

43. Example 1.6.1

44. Example 1.6.1
“Tree diagram”

45. 1.7 – Independent Events
Definition Two events A and B are said to be independent if
If they are not independent, they are said to be dependent.
Informally: Independence means the occurrence of one event does not affect the probability of the other event occurring

46. Example 1.7.2
Suppose a student has an 8:30 AM statistics test and is worried that her alarm clock will fail and not ring, so she decides to set two different battery-powered alarm clocks. If the probability that each clock will fail is Find the probability that at least one clock will ring.
Let F1 and F2 be the events that the first and second clocks fail
Assume independence

47. Example 1.7.2

48. Mutual Independence
Definition Three events A, B, and C are said to be pairwise independent if They are said to be mutually independent (or simply independent) if they are pairwise independent and

49. Example 1.7.7
Suppose that on a college campus of 1,000 students (referred to as the population), 600 support the idea of building a new gym and 400 are opposed. The president of the college randomly selects five different students and talks with each about their opinion. Find the probability that they all oppose the idea.

50. Example 1.7.7
Let
A1 = event that the first student opposes the idea,
A2 = event that the second student opposes it, etc.
These events are dependent since the selections are made without replacement

51. Example 1.7.7 Now suppose the selections are made with replacement
The events are independent

52. Example 1.7.7 Without replacement: Prob ≈ 0.010087
With replacement: Prob =
Practically the same
5% Guideline: If no more than 5% of the population is being selected, then the selections may be treated as independent, even though they are technically dependent