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Copyright © 2010 Pearson Addison-Wesley. All rights reserved.
Chapter 3
Random
Variables
Copyright © 2010 Pearson Addison-Wesley. All rights reserved. 3 - 2
Definition 3.1
Concept of a Random Variable
Section 3.1
Example: Two balls are drawn in succession without replacement from
an urn containing 4 red balls and 3 black balls. The random variable Y is
the number of red balls.
Copyright © 2010 Pearson Addison-Wesley. All rights reserved. 3 - 3
Definition 3.2
Definition 3.3
Copyright © 2010 Pearson Addison-Wesley. All rights reserved. 3 - 4
Definition 3.4
Section 3.2
Discrete Probability Distribution
Copyright © 2010 Pearson Addison-Wesley. All rights reserved. 3 - 5
Definition 3.5
Example: If a car agency sells 50% of its inventory of a certain foreign
car equipped with side airbags, find a formula for the probability
distribution of the number of cars with side airbags among the next 4
cars sold by the agency.
Copyright © 2010 Pearson Addison-Wesley. All rights reserved. 3 - 6
Figure 3.1 Probability mass
function plot
Copyright © 2010 Pearson Addison-Wesley. All rights reserved. 3 - 7
Figure 3.2 Probability histogram
Copyright © 2010 Pearson Addison-Wesley. All rights reserved. 3 - 8
Figure 3.3 Discrete cumulative
distribution function
Copyright © 2010 Pearson Addison-Wesley. All rights reserved. 3 - 9
Figure 3.4 Typical density functions
Section 3.3
Continuous Probability Distributions
Copyright © 2010 Pearson Addison-Wesley. All rights reserved. 3 - 10
Figure 3.5 P(a < X < b)
Copyright © 2010 Pearson Addison-Wesley. All rights reserved. 3 - 11
Definition 3.6
Copyright © 2010 Pearson Addison-Wesley. All rights reserved. 3 - 12
Definition 3.7
Copyright © 2010 Pearson Addison-Wesley. All rights reserved. 3 - 13
Figure 3.6 Continuous cumulative
distribution function

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Walpole ch03 edit

  • 1. Copyright © 2010 Pearson Addison-Wesley. All rights reserved. Chapter 3 Random Variables
  • 2. Copyright © 2010 Pearson Addison-Wesley. All rights reserved. 3 - 2 Definition 3.1 Concept of a Random Variable Section 3.1 Example: Two balls are drawn in succession without replacement from an urn containing 4 red balls and 3 black balls. The random variable Y is the number of red balls.
  • 3. Copyright © 2010 Pearson Addison-Wesley. All rights reserved. 3 - 3 Definition 3.2 Definition 3.3
  • 4. Copyright © 2010 Pearson Addison-Wesley. All rights reserved. 3 - 4 Definition 3.4 Section 3.2 Discrete Probability Distribution
  • 5. Copyright © 2010 Pearson Addison-Wesley. All rights reserved. 3 - 5 Definition 3.5 Example: If a car agency sells 50% of its inventory of a certain foreign car equipped with side airbags, find a formula for the probability distribution of the number of cars with side airbags among the next 4 cars sold by the agency.
  • 6. Copyright © 2010 Pearson Addison-Wesley. All rights reserved. 3 - 6 Figure 3.1 Probability mass function plot
  • 7. Copyright © 2010 Pearson Addison-Wesley. All rights reserved. 3 - 7 Figure 3.2 Probability histogram
  • 8. Copyright © 2010 Pearson Addison-Wesley. All rights reserved. 3 - 8 Figure 3.3 Discrete cumulative distribution function
  • 9. Copyright © 2010 Pearson Addison-Wesley. All rights reserved. 3 - 9 Figure 3.4 Typical density functions Section 3.3 Continuous Probability Distributions
  • 10. Copyright © 2010 Pearson Addison-Wesley. All rights reserved. 3 - 10 Figure 3.5 P(a < X < b)
  • 11. Copyright © 2010 Pearson Addison-Wesley. All rights reserved. 3 - 11 Definition 3.6
  • 12. Copyright © 2010 Pearson Addison-Wesley. All rights reserved. 3 - 12 Definition 3.7
  • 13. Copyright © 2010 Pearson Addison-Wesley. All rights reserved. 3 - 13 Figure 3.6 Continuous cumulative distribution function