slide1 n.
Skip this Video
Loading SlideShow in 5 Seconds..
Chapter Four Elementary Probability Theory PowerPoint Presentation
Download Presentation
Chapter Four Elementary Probability Theory

play fullscreen
1 / 64
Download Presentation

Chapter Four Elementary Probability Theory - PowerPoint PPT Presentation

yardley
107 Views
Download Presentation

Chapter Four Elementary Probability Theory

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Understandable StatisticsSeventh EditionBy Brase and BrasePrepared by: Lynn SmithGloucester County College Chapter Four Elementary Probability Theory

  2. Probability • Probability is a numerical measurement of likelihood of an event. • The probability of any event is a number between zero and one. • Events with probability close to one are more likely to occur. • If an event has probability equal to one, the event is certain to occur.

  3. Probability Notation If A represents an event, P(A) represents the probability of A.

  4. Three methods to find probabilities: • Intuition • Relative frequency • Equally likely outcomes

  5. Intuition method based upon our level of confidence in the result Example: I am 95% sure that I will attend the party.

  6. Probability as Relative Frequency Probability of an event = the fraction of the time that the event occurred in the past = f n where f = frequency of an event n = sample size

  7. Example of Probability as Relative Frequency If you note that 57 of the last 100 applicants for a job have been female, the probability that the next applicant is female would be:

  8. Law of Large Numbers In the long run, as the sample size increases and increases, the relative frequencies of outcomes get closer and closer to the theoretical (or actual) probability value.

  9. Equally likely outcomes No one result is expected to occur more frequently than any other.

  10. Probability of an event when outcomes are equally likely =

  11. Example of Equally Likely Outcome Method When rolling a die, the probability of getting a number less than three =

  12. Statistical Experiment activity that results in a definite outcome

  13. Sample Space set of all possible outcomes of an experiment

  14. Sample Space for the rolling of an ordinary die: 1, 2, 3, 4, 5, 6

  15. For the experiment of rolling an ordinary die: 3 = 1 6 2 • P(even number) = • P(result less than four) = • P(not getting a two) = 3 = 1 6 2 5 6

  16. Complement of Event A the event not A

  17. Probability of a Complement P(not A) = 1 – P(A)

  18. Probability of a Complement If the probability that it will snow today is 30%, P(It will not snow) = 1 – P(snow) = 1 – 0.30 = 0.70

  19. Probabilities of an Event and its Complement • Denote the probability of an event by the letter p. • Denote the probability of the complement of the event by the letter q. • p + q must equal 1 • q = 1 - p

  20. Probability Related to Statistics • Probability makes statements about what will occur when samples are drawn from a known population. • Statistics describes how samples are to be obtained and how inferences are to be made about unknown populations.

  21. Independent Events The occurrence (or non-occurrence) of one event does not change the probability that the other event will occur.

  22. If events A and B are independent, P(A and B) = P(A) • P(B)

  23. Conditional Probability • If events are dependent, the occurrence of one event changes the probability of the other. • The notation P(A|B) is read “the probability of A, given B.” • P(A, given B) equals the probability that event A occurs, assuming that B has already occurred.

  24. For Dependent Events: • P(A and B) = P(A) • P(B, given A) • P(A and B) = P(B) • P(A, given B)

  25. The Multiplication Rules: • For independent events: P(A and B) = P(A) • P(B) • For dependent events: P(A and B) = P(A) • P(B, given A) P(A and B) = P(B) • P(A, given B)

  26. For independent events:P(A and B) = P(A) • P(B) When choosing two cards from two separate decks of cards, find the probability of getting two fives. P(two fives) = P(5 from first deck and 5 from second) =

  27. For dependent events:P(A and B) = P(A) • P(B, given A) When choosing two cards from a deck without replacement, find the probability of getting two fives. P(two fives) = P(5 on first draw and 5 on second) =

  28. “And” versus “or” • And means both events occur together. • Or means that at least one of the events occur.

  29. For any events A and B, P(A or B) = P(A) + P(B) – P(A and B)

  30. When choosing a card from an ordinary deck, the probability of getting a five or a red card: P(5 ) + P(red) – P(5 and red) =

  31. When choosing a card from an ordinary deck, the probability of getting a five or a six: P(5 ) + P(6) – P(5 and 6) =

  32. For any mutually exclusive events A and B, P(A or B) = P(A) + P(B)

  33. When rolling an ordinary die: P(4 or 6) =

  34. Survey results: P(male and college grad) = ?

  35. Survey results: P(male and college grad) =

  36. Survey results: P(male or college grad) = ?

  37. Survey results: P(male or college grad) =

  38. Survey results: P(male, given college grad) = ?

  39. Survey results: P(male, given college grad) =

  40. Counting Techniques • Tree Diagram • Multiplication Rule • Permutations • Combinations

  41. Tree Diagram a method of listing outcomes of an experiment consisting of a series of activities

  42. Tree diagram for the experiment of tossing two coins H H T start H T T

  43. Find the number of paths without constructing the tree diagram: Experiment of rolling two dice, one after the other and observing any of the six possible outcomes each time . Number of paths = 6 x 6 = 36

  44. Multiplication of Choices If there are n possible outcomes for event E1 and m possible outcomes for event E2, then there are n x m or nm possible outcomes for the series of events E1 followed by E2.

  45. Area Code Example Until a few years ago a three-digit area code was designed as follows. The first could be any digit from 2 through 9. The second digit could be only a 0 or 1. The last could be any digit. How many different such area codes were possible? 82 10 = 160

  46. Ordered Arrangements In how many different ways could four items be arranged in order from first to last? 4 3 21 = 24

  47. Factorial Notation • n! is read "n factorial" • n! is applied only when n is a whole number. • n! is a product of n with each positive counting number less than n

  48. Calculating Factorials 5! = 5 • 4 • 3 • 2 • 1 = 3! = 3 • 2 • 1 = 120 6

  49. Definitions 1! = 1 0! = 1

  50. Complete the Factorials: 24 4! = 10! = 6! = 15! = 3,628,800 720 1.3077 x 1012