2. Copyright © 2011 Pearson Education, Inc. Random Variables Chapter 9

3. 9.1 Random Variables Will the price of a stock go up or down?  Need language to describe processes that show random behavior (such as stock returns)  “Random variables” are the main components of this language Copyright © 2011 Pearson Education, Inc. 3 of 33

4. 9.1 Random Variables Definition of a Random Variable  Describes the uncertain outcomes of a random process  Denoted by X  Defined by listing all possible outcomes and their associated probabilities Copyright © 2011 Pearson Education, Inc. 4 of 33

5. 9.1 Random Variables Suppose a day trader buys one share of IBM  Let X represent the change in price of IBM  She pays $100 today, and the price tomorrow can be either $105, $100 or $95 Copyright © 2011 Pearson Education, Inc. 5 of 33

6. 9.1 Random Variables How X is Defined Copyright © 2011 Pearson Education, Inc. 6 of 33

7. 9.1 Random Variables Two Types: Discrete vs. Continuous  Discrete – A random variable that takes on one of a list of possible values (counts)  Continuous – A random variable that takes on any value in an interval Copyright © 2011 Pearson Education, Inc. 7 of 33

8. 9.1 Random Variables Graphs of Random Variables  Show the probability distribution for a random variable  Show probabilities, not relative frequencies from data Copyright © 2011 Pearson Education, Inc. 8 of 33

9. 9.1 Random Variables Graph of X = Change in Price of IBM Copyright © 2011 Pearson Education, Inc. 9 of 33

10. 9.1 Random Variables Random Variables as Models  A random variable is a statistical model  A random variable represents a simplified or idealized view of reality  Data affect the choice of probability distribution for a random variable Copyright © 2011 Pearson Education, Inc. 10 of 33

11. 9.2 Properties of Random Variables Parameters  Characteristics of a random variable, such as its mean or standard deviation  Denoted typically by Greek letters Copyright © 2011 Pearson Education, Inc. 11 of 33

12. 9.2 Properties of Random Variables Mean (µ) of a Random Variable  Weighted sum of possible values with probabilities as weights Copyright © 2011 Pearson Education, Inc. 12 of 33

13. 9.2 Properties of Random Variables Mean (µ) of X (Change in Price of IBM) The day trader expects on average to make 10 cents on every share of IBM she buys. Copyright © 2011 Pearson Education, Inc. 13 of 33

14. 9.2 Properties of Random Variables Mean (µ) as the Balancing Point Copyright © 2011 Pearson Education, Inc. 14 of 33

15. 9.2 Properties of Random Variables Mean (µ) of a Random Variable  Is a special case of the more general concept of an expected value, E(X) Copyright © 2011 Pearson Education, Inc. 15 of 33

16. 9.2 Properties of Random Variables Variance (σ 2 ) and Standard Deviation (σ)  The variance of X is the expected value of the squared deviation from µ Copyright © 2011 Pearson Education, Inc. 16 of 33

17. 9.2 Properties of Random Variables Calculating the Variance (σ 2 ) for X Copyright © 2011 Pearson Education, Inc. 17 of 33

18. 9.2 Properties of Random Variables Calculating the Variance (σ 2 ) for X Copyright © 2011 Pearson Education, Inc. 18 of 33

19. 9.2 Properties of Random Variables The Standard Deviation (σ ) for X Copyright © 2011 Pearson Education, Inc. 19 of 33

20. 4M Example 9.1: COMPUTER SHIPMENTS & QUALITY Motivation CheapO Computers shipped two servers to its biggest client. Four refurbished computers were mistakenly restocked among 11 new systems. If the client receives two new systems, the profit for the company is $10,000; if the client receives one new system, the profit is $9,600. If the client receives two refurbished systems, the company loses $800. What are the expected value and standard deviation of CheapO’s profits? Copyright © 2011 Pearson Education, Inc. 20 of 33

21. 4M Example 9.1: COMPUTER SHIPMENTS & QUALITY Method Identify the relevant random variable, X, which is the amount of profit earned on this order. Determine the associated probabilities for its values using a tree diagram. Compute µ and σ. Copyright © 2011 Pearson Education, Inc. 21 of 33

22. 4M Example 9.1: COMPUTER SHIPMENTS & QUALITY Mechanics – Tree Diagram Copyright © 2011 Pearson Education, Inc. 22 of 33

23. 4M Example 9.1: COMPUTER SHIPMENTS & QUALITY Mechanics – Probabilities for X Copyright © 2011 Pearson Education, Inc. 23 of 33

24. 4M Example 9.1: COMPUTER SHIPMENTS & QUALITY Mechanics – Compute µ and σ E(X) = µ = $9,215 Var(X) = σ 2 = 6,116,340 $ 2 SD(X) = σ = $2,473 Copyright © 2011 Pearson Education, Inc. 24 of 33

25. 4M Example 9.1: COMPUTER SHIPMENTS & QUALITY Message This is a very profitable deal on average. The large standard deviation is a reminder that profits are wiped out if the client receives two refurbished systems. Copyright © 2011 Pearson Education, Inc. 25 of 33

26. 9.3 Properties of Expected Values Adding or Subtracting a Constant (c)  Changes the expected value by a fixed amount:E(X ± c) = E(X) ± c  Does not change the variance or standard deviation:Var(X ± c) = Var(X) SD(X ± c) = SD(X) Copyright © 2011 Pearson Education, Inc. 26 of 33

27. 9.3 Properties of Expected Values Multiplying by a Constant (c)  Changes the mean and standard deviation by a factor of c:E(cX) = c E(X) SD(cX) = |c| SD(X)  Changes the variance by a factor of c 2 : Var(cX) = c 2 Var(X) Copyright © 2011 Pearson Education, Inc. 27 of 33

28. 9.3 Properties of Expected Values Rules for Expected Values (a and b are constants)  E(a + bX) = a + bE(X)  SD(a + b X) = |b|SD(X)  Var(a + bX) = b 2 Var(X) Copyright © 2011 Pearson Education, Inc. 28 of 33

29. 9.4 Comparing Random Variables  May require transforming random variables into new ones that have a common scale  May require adjusting if the results from the mean and standard deviation are mixed Copyright © 2011 Pearson Education, Inc. 29 of 33

30. 9.4 Comparing Random Variables The Sharpe Ratio  Popular in finance  Is the ratio of an investment’s net expected gain to its standard deviation Copyright © 2011 Pearson Education, Inc. 30 of 33

31. 9.4 Comparing Random Variables The Sharpe Ratio – An Example S(Disney) = 0.0253 S(McDonald’s) = 0.0171 Disney is preferred to McDonald’s Copyright © 2011 Pearson Education, Inc. 31 of 33

32. Best Practices  Use random variables to represent uncertain outcomes.  Draw the random variable.  Recognize that random variables represent models.  Keep track of the units of a random variable. Copyright © 2011 Pearson Education, Inc. 32 of 33

33. Pitfalls  Do not confuse with µ or s with σ.  Do not mix up X with x.  Do not forget to square constants in variances. Copyright © 2011 Pearson Education, Inc. 33 of 33