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- 2. Random Variables Represent possible numerical values from a random experiments. Which outcome will occur, is not
- 3. Discrete random variable A discrete random variable is a possible outcome from a random experiment. It
- 4. Discrete random variable
- 5. Continuous random variable A random variable that has an unlimited set of values. Therefore called continuous
- 6. Continuous random variable A continuous random variable has an unlimited set of values. The probability of
- 7. Time, in seconds, it takes 110 employees to assemble a toaster 271 236 294 252 254
- 8. Continuous random variable
- 9. Employee assembly time in seconds Completion time (in seconds) Frequency Relative frequency % 220 – 229
- 10. Probability Models For both discrete and continuous variables, the collection of all possible outcomes (sample space)
- 11. Ch. 4- DR SUSANNE HANSEN SARAL Probability Model for Discrete Random Variables Let X be a
- 12. Ch. 4- DR SUSANNE HANSEN SARAL Probability Model, also Probability Distributions Function, P(x) for Discrete Random
- 13. Probability Distributions Function, P(x) for Discrete Random Variables (example) Sales of sandwiches in a sandwich shop:
- 14. Graphical illustration of the probability distribution of sandwich sales between 14:00 -16:00 hours DR SUSANNE HANSEN
- 15. Requirements for a probability distribution of a discrete random variable DR SUSANNE HANSEN SARAL Ch. 4-
- 16. Cumulative Probability Function DR SUSANNE HANSEN SARAL Ch. 4- (continued) X Value P(x) F(x) 0 0.25
- 17. DR SUSANNE HANSEN SARAL Ch. 4- (continued) x Value P(x) F(x0) A 0.18 0.18 = 0.18
- 18. Graphical illustration of P(x) DR SUSANNE HANSEN SARAL Ch. 4- Example: Let the random variable, X,
- 19. Graphical illustration of F(x0) Cumulative probability distribution, Ogive DR SUSANNE HANSEN SARAL Ch. 4- Example: Let
- 20. Cumulative Probability Function, F(x0) Practical application DR SUSANNE HANSEN SARAL The cumulative probability distribution can be
- 21. Cumulative Probability Function, F(x0) Practical application: Car dealer DR SUSANNE HANSEN SARAL The random variable, X,
- 22. Cumulative Probability Function, F(x0) Practical application DR SUSANNE HANSEN SARAL Example: If there are 3 cars
- 23. Cumulative Probability Function, F(x0) Practical application DR SUSANNE HANSEN SARAL Example: If only 2 cars are
- 24. 3/9/2017 DR SUSANNE HANSEN SARAL
- 25. Cumulative probability - solution DR SUSANNE HANSEN SARAL
- 26. Exercise In a geography exam the grade students obtained is the random variable X. It has
- 27. Cumulative probabilities - exercise Based on this, calculate the following: The cumulative probability distribution of X,
- 28. Properties of Discrete Random Variables The measurements of central tendency and variation for discrete random variables:
- 29. Expectations
- 30. Expected Value of a discrete random variable X: Example: Toss 2 coins, random variable, X =
- 31. Properties of Discrete Random Variables Expected Value (or mean) of a discrete random variable X: We
- 32. Expected value So, the expected value, E[X], of a discrete random variable is found by multiplying
- 33. Concept of expected value of a random variable A review of university textbooks reveals that 81
- 34. Expected value – calculation (example) Find the expected mean number of mistakes on pages: = (0)(.81)+(1)(.17)+(2)(.02)
- 35. Probability distribution of mistakes in textbooks DR SUSANNE HANSEN SARAL
- 36. Exercise A lottery offers 500 tickets for $ 3 each. If the biggest prize is $
- 37. Exercise A lottery offers 500 tickets for $ 3 each. If the biggest prize is $
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