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Continuous Random Variables and Probability Distributions

Explore probability distributions, density functions, cumulative functions, and more related to engineering statistics at the Islamic University of Gaza.

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Continuous Random Variables and Probability Distributions

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  1. Islamic University of Gaza Statistics and Probability for Engineers (ENGC 6310) Lecture 3: Continuous Random Variables and Probability Distribution Prof. Dr. Yunes Mogheir Civil and Environmental Engineering Dept. First Semester/2019

  2. 4-2 Probability Distributions and Probability Density Functions Figure 4-2Probability determined from the area under f(x).

  3. 4-2 Probability Distributions and Probability Density Functions Definition

  4. 4-2 Probability Distributions and Probability Density Functions Figure 4-3Histogram approximates a probability density function.

  5. 4-2 Probability Distributions and Probability Density Functions

  6. 4-2 Probability Distributions and Probability Density Functions Example

  7. b) What is the probability that a metal cylinder has a diameter between 49.8 mm and 50.1 mm?

  8. 4-2 Probability Distributions and Probability Density Functions Example 4-2

  9. 4-2 Probability Distributions and Probability Density Functions Figure 4-5Probability density function for Example 4-2.

  10. 4-2 Probability Distributions and Probability Density Functions Example 4-2 (continued)

  11. 4-3 Cumulative Distribution Functions Definition

  12. 4-3 Cumulative Distribution Functions Example

  13. 4-3 Cumulative Distribution Functions Example 4-4

  14. 4-3 Cumulative Distribution Functions Figure 4-7Cumulative distribution function for Example 4-4.

  15. 4-4 Mean and Variance of a Continuous Random Variable Definition

  16. 4-4 Mean and Variance of a Continuous Random Variable Example 4-6

  17. 4-4 Mean and Variance of a Continuous Random Variable Example 4-8

  18. Example Suppose that the diameter of a metal cylinder has a pdf of: What is the expected value of the cylinder diameter?

  19. 4-5 Continuous Uniform Random Variable Definition

  20. 4-5 Continuous Uniform Random Variable Figure 4-8Continuous uniform probability density function.

  21. 4-5 Continuous Uniform Random Variable Mean and Variance

  22. 4-5 Continuous Uniform Random Variable Example 4-9

  23. 4-5 Continuous Uniform Random Variable Figure 4-9Probability for Example 4-9.

  24. 4-5 Continuous Uniform Random Variable

  25. 4-6 Normal Distribution Definition

  26. 4-6 Normal Distribution Figure 4-10Normal probability density functions for selected values of the parameters  and 2.

  27. 4-6 Normal Distribution Some useful results concerning the normal distribution

  28. 4-6 Normal Distribution Definition : Standard Normal

  29. 4-6 Normal Distribution Example 4-11 Figure 4-13Standard normal probability density function.

  30. 4-6 Normal Distribution Standardizing

  31. 4-6 Normal Distribution Example 4-13

  32. 4-6 Normal Distribution Figure 4-15Standardizing a normal random variable.

  33. 4-6 Normal Distribution To Calculate Probability

  34. 4-6 Normal Distribution Example 4-14

  35. 4-6 Normal Distribution Example 4-14 (continued)

  36. 4-6 Normal Distribution Example 4-14 (continued) Figure 4-16Determining the value of x to meet a specified probability.

  37. 4-7 Normal Approximation to the Binomial and Poisson Distributions • Under certain conditions, the normal distribution can be used to approximate the binomial distribution and the Poisson distribution.

  38. 4-7 Normal Approximation to the Binomial and Poisson Distributions Figure 4-19Normal approximation to the binomial.

  39. 4-7 Normal Approximation to the Binomial and Poisson Distributions Example 4-17

  40. 4-7 Normal Approximation to the Binomial and Poisson Distributions Normal Approximation to the Binomial Distribution

  41. 4-7 Normal Approximation to the Binomial and Poisson Distributions Example 4-18

  42. 4-7 Normal Approximation to the Binomial and Poisson Distributions Figure 4-21Conditions for approximating hypergeometric and binomial probabilities.

  43. 4-7 Normal Approximation to the Binomial and Poisson Distributions Normal Approximation to the Poisson Distribution

  44. 4-7 Normal Approximation to the Binomial and Poisson Distributions Example 4-20

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