1 / 19

CHAPTER THREE

CHAPTER THREE. Probability. Section 3.1. Basic Concepts of Probability and Counting. Probability Experiment:. … an action, or trial, through which specific results are obtained. The result of a single trial is called an OUTCOME. The set of all possible outcomes is called the SAMPLE SPACE.

csilla
Download Presentation

CHAPTER THREE

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. CHAPTER THREE Probability

  2. Section 3.1 Basic Concepts of Probability and Counting

  3. Probability Experiment: • … an action, or trial, through which specific results are obtained. • The result of a single trial is called an OUTCOME. • The set of all possible outcomes is called the SAMPLE SPACE. • An EVENT is a subset of the sample space.

  4. EX: Identify the sample space and determine the # of outcomes • 16. Guessing a student’s letter grade (A, B, C, D, F) in a class. • 18. Tossing three coins. (Hint… draw a tree diagram)

  5. The Fundamental Counting Principle • If one event can occur in m ways and a second event can occur in n ways, the number of ways the two events can occur in sequence is m · n • EX: For dinner you select one each from 3 appetizers, 4 entrees, and 2 desserts. How many different ‘meals’ can you make if you choose one from each category?

  6. 3 Types of Probability #1 Classical Probability (AKA Theoretical Probability): used when each outcome in a sample space is equally likely to occur. P(E) = probability of event E P(E) = # of outcomes in event E Total # of outcomes in sample space

  7. #2 Empirical Probability (AKA Statistical Probability) Based on observations obtained from probability experiments. Same as relative frequency of event. P(E) = Frequency of Event E = f Total Frequency n

  8. #3 Subjective Probability • Result from intuition, educated guesses, and estimates.

  9. The Law of Large Numbers • As an experiment is repeated over and over, the empirical probability of an event approaches the theoretical probability of the event.

  10. Classify as an example of classical, empirical, or subjective probability. • The probability of choosing 6 numbers from 1 to 40 that matches the 6 numbers drawn by a state lottery is 1/3,838,380 ≈ 0.00000026.

  11. Rules of Probability 0 < P(E) < 1 The probability of an event is between 0 and 1 P(E) = 0 means the event CANNOT occur. P(E) = 1 means the event is CERTAIN. ΣP(E) = 1 The sum of the probabilities of all outcomes in the sample space is one.

  12. Complementary Events • The complement of event E (denoted E’) is the set of all outcomes in the sample space that are NOT part of event E. • P(E) + P(E’) = 1 • P(E’) = 1 – P(E) • P(E) = 1 - P(E’)

  13. Section 3.2 Conditional Probability & the Multiplication Rule

  14. Conditional Probability … the probability of an event occurring, GIVEN that another event has occurred. The conditional probability of event B occurring given that event A occurred is P(B | A)

  15. Independent & Dependent Events • Two events are INDEPENDENT if the occurrence of one does not affect the probability of the other event. • A and B are independent if… P(B | A) = P(B) or if… P(A | B) = P(A)

  16. Dependent or Independent? • 8. Returning a rented movie after the due date and receiving a late fee. • 12. A ball numbered 1 through 52 is selected from a bin, replaced, and a second numbered ball is selected from the bin.

  17. The Multiplication Rule • The probability that A and B will occur in sequence is: P(A and B) = P(A) · P(B | A) • If A and B are independent, use: P(A and B) = P(A) · P(B)

More Related