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2-1 Sample Spaces and Events

2-1 Sample Spaces and Events. 2-1.1 Random Experiments. Figure 2-1 Continuous iteration between model and physical system. 2-1 Sample Spaces and Events. 2-1.1 Random Experiments. Figure 2-2 Noise variables affect the transformation of inputs to outputs. 2-1 Sample Spaces and Events.

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2-1 Sample Spaces and Events

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  1. 2-1 Sample Spaces and Events 2-1.1 Random Experiments Figure 2-1Continuous iteration between model and physical system.

  2. 2-1 Sample Spaces and Events 2-1.1 Random Experiments Figure 2-2Noise variables affect the transformation of inputs to outputs.

  3. 2-1 Sample Spaces and Events 2-1.1 Random Experiments Definition

  4. 2-1 Sample Spaces and Events 2-1.1 Random Experiments Figure 2-3A closer examination of the system identifies deviations from the model.

  5. 2-1 Sample Spaces and Events 2-1.1 Random Experiments Figure 2-4Variation causes disruptions in the system.

  6. 2-1 Sample Spaces and Events 2-1.2 Sample Spaces Definition

  7. 2-1 Sample Spaces and Events 2-1.2 Sample Spaces Example 2-1

  8. 2-1 Sample Spaces and Events Example 2-1 (continued)

  9. 2-1 Sample Spaces and Events Example 2-2

  10. 2-1 Sample Spaces and Events Example 2-2 (continued)

  11. 2-1 Sample Spaces and Events Tree Diagrams • Sample spaces can also be described graphically withtree diagrams. • When a sample space can be constructed in several steps or stages, we can represent each of the n1 ways of completing the first step as a branch of a tree. • Each of the ways of completing the second step can be represented as n2 branches starting from the ends of the original branches, and so forth.

  12. 2-1 Sample Spaces and Events Figure 2-5Tree diagram for three messages.

  13. 2-1 Sample Spaces and Events Example 2-3

  14. 2-1 Sample Spaces and Events 2-1.3 Events Definition

  15. 2-1 Sample Spaces and Events 2-1.3 Events Basic Set Operations

  16. 2-1 Sample Spaces and Events 2-1.3 Events Example 2-6

  17. 2-1 Sample Spaces and Events Definition

  18. 2-1 Sample Spaces and Events Venn Diagrams Figure 2-8Venn diagrams.

  19. 2-1 Sample Spaces and Events 2-1.4 Counting Techniques Multiplication Rule

  20. 2-1 Sample Spaces and Events 2-1.4 Counting Techniques Permutations

  21. 2-1 Sample Spaces and Events 2-1.4 Counting Techniques Permutations : Example 2-10

  22. 2-1 Sample Spaces and Events 2-1.4 Counting Techniques Permutations of Subsets

  23. 2-1 Sample Spaces and Events 2-1.4 Counting Techniques Permutations of Subsets: Example 2-11

  24. 2-1 Sample Spaces and Events 2-1.4 Counting Techniques Permutations of Similar Objects

  25. 2-1 Sample Spaces and Events 2-1.4 Counting Techniques Permutations of Similar Objects: Example 2-12

  26. 2-1 Sample Spaces and Events 2-1.4 Counting Techniques Combinations

  27. 2-1 Sample Spaces and Events 2-1.4 Counting Techniques Combinations: Example 2-13

  28. 2-2 Interpretations of Probability 2-2.1 Introduction Probability • Used to quantify likelihood or chance • Used to represent risk or uncertainty in engineering applications • Can be interpreted as our degree of belief or relative frequency

  29. 2-2 Interpretations of Probability 2-2.1 Introduction Figure 2-10Relative frequency of corrupted pulses sent over a communications channel.

  30. 2-2 Interpretations of Probability Equally Likely Outcomes

  31. 2-2 Interpretations of Probability Example 2-15

  32. 2-2 Interpretations of Probability Figure 2-11Probability of the event E is the sum of the probabilities of the outcomes in E

  33. 2-2 Interpretations of Probability Definition

  34. 2-2 Interpretations of Probability Example 2-16

  35. 2-2 Interpretations of Probability 2-2.2 Axioms of Probability

  36. 2-3 Addition Rules Probability of a Union

  37. 2-3 Addition Rules Mutually Exclusive Events

  38. 2-3 Addition Rules Three Events

  39. 2-3 Addition Rules

  40. 2-3 Addition Rules Figure 2-12Venn diagram of four mutually exclusive events

  41. 2-3 Addition Rules Example 2-21

  42. 2-4 Conditional Probability • To introduce conditional probability, consider an example involving manufactured parts. • Let D denote the event that a part is defective and let F denote the event that a part has a surface flaw. • Then, we denote the probability of D given, or assuming, that a part has a surface flaw as P(D|F). This notation is read as the conditional probability of D given F, and it is interpreted as the probability that a part is defective, given that the part has a surface flaw.

  43. 2-4 Conditional Probability Figure 2-13Conditional probabilities for parts with surface flaws

  44. 2-4 Conditional Probability Definition

  45. 2-5 Multiplication and Total Probability Rules 2-5.1 Multiplication Rule

  46. 2-5 Multiplication and Total Probability Rules Example 2-26

  47. 2-5 Multiplication and Total Probability Rules 2-5.2 Total Probability Rule Figure 2-15Partitioning an event into two mutually exclusive subsets. Figure 2-16Partitioning an event into several mutually exclusive subsets.

  48. 2-5 Multiplication and Total Probability Rules 2-5.2 Total Probability Rule (two events)

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