Business Statistics: A Decision-Making Approach 6th Edition Chapter Using Probability and Probability Distributions Business Statistics: A Decision-Making Approach, 6e © 2005 PrenticeHall, Inc Chap 4-1 Chapter Goals After completing this chapter, you should be able to: Explain three approaches to assessing probabilities Apply common rules of probability Use Bayes’ Theorem for conditional probabilities Distinguish between discrete and continuous probability distributions Compute the expected value and standard Business Statistics: A Decisiondiscrete probability distribution Makingdeviation Approach, for 6e ©a2005 Prentice-Hall, Inc Chap 4-2 Important Terms Probability – the chance that an uncertain event will occur (always between and 1) Experiment – a process of obtaining outcomes for uncertain events Elementary Event – the most basic outcome possible from a simple experiment Sample Space – the collection of all possible elementary outcomes Business Statistics: A DecisionMaking Approach, 6e © 2005 Prentice-Hall, Inc Chap 4-3 Sample Space The Sample Space is the collection of all possible outcomes e.g All faces of a die: e.g All 52 cards of a bridge deck: Business Statistics: A DecisionMaking Approach, 6e © 2005 Prentice-Hall, Inc Chap 4-4 Events Elementary event – An outcome from a sample space with one characteristic Example: A red card from a deck of cards Event – May involve two or more outcomes simultaneously Example: An ace that is also red from a deck of cards Business Statistics: A DecisionMaking Approach, 6e © 2005 Prentice-Hall, Inc Chap 4-5 Visualizing Events Contingency Tables Ace Not Ace Black 24 26 Red 24 26 Total 48 52 Tree Diagrams Sample Space Ca r k c a Bl d Full Deck of 52 Cards Business Statistics: A DecisionRed C Making Approach, 6e © 2005 ard Prentice-Hall, Inc Total Ac e Not an Ace Ace Not an A Sample Space 24 ce 24 Chap 4-6 Elementary Events A automobile consultant records fuel type and vehicle type for a sample of vehicles Fuel types: Gasoline, Diesel Vehicle types: Truck, Car, SUV possible elementary events: e1 Gasoline, Truck e2 Gasoline, Car e3 Gasoline, SUV e4 Diesel, Truck e5 Diesel, Car Business Statistics: A Decisione6 Diesel, SUV Making Approach, 6e © 2005 Prentice-Hall, Inc Ga ine l o s Die sel k Truc Car e1 SUV e3 k Truc Car SUV e2 e4 e5 e6 Chap 4-7 Probability Concepts Mutually Exclusive Events If E1 occurs, then E2 cannot occur E1 and E2 have no common elements E1 Black Cards E2 Red Cards Business Statistics: A DecisionMaking Approach, 6e © 2005 Prentice-Hall, Inc A card cannot be Black and Red at the same time Chap 4-8 Probability Concepts Independent and Dependent Events Independent: Occurrence of one does not influence the probability of occurrence of the other Dependent: Occurrence of one affects the probability of the other Business Statistics: A DecisionMaking Approach, 6e © 2005 Prentice-Hall, Inc Chap 4-9 Independent vs Dependent Events Independent Events E1 = heads on one flip of fair coin E2 = heads on second flip of same coin Result of second flip does not depend on the result of the first flip Dependent Events E1 = rain forecasted on the news E2 = take umbrella to work Probability of the second event is affected by the Business Statistics: A Decisionoccurrence of the first event Making Approach, 6e © 2005 Prentice-Hall, Inc Chap 4-10 Bayes’ Theorem Example Let S = successful well and U = unsuccessful well P(S) = , P(U) = (prior probabilities) Define the detailed test event as D Conditional probabilities: P(D|S) = (continued ) P(D|U) = Revised probabilities Event Prior Prob Conditional Prob Joint Prob Revised Prob S (successful) 4*.6 = 24 24/.36 = 67 6*.2 = 12 12/.36 = 33 Business Statistics: A DecisionU (unsuccessful) Making Approach, 6e © 2005 Prentice-Hall, Inc Sum = 36 Chap 4-27 Bayes’ Theorem Example (continued ) Given the detailed test, the revised probability of a successful well has risen to 67 from the original estimate of Event Prior Prob Conditional Prob Joint Prob Revised Prob S (successful) 4*.6 = 24 24/.36 = 67 U (unsuccessful) 6*.2 = 12 12/.36 = 33 Business Statistics: A DecisionMaking Approach, 6e © 2005 Prentice-Hall, Inc Sum = 36 Chap 4-28 Introduction to Probability Distributions Random Variable Represents a possible numerical value from a random event Random Variables Discrete Random Variable Business Statistics: A DecisionMaking Approach, 6e © 2005 Prentice-Hall, Inc Continuous Random Variable Chap 4-29 Discrete Random Variables Can only assume a countable number of values Examples: Roll a die twice Let x be the number of times comes up (then x could be 0, 1, or times) Toss a coin times Let x be the number of heads Business Statistics: (then A x Decision= 0, 1, 2, 3, 4, or 5) Making Approach, 6e © 2005 Prentice-Hall, Inc Chap 4-30 Discrete Probability Distribution Experiment: Toss Coins T T H Probability Distribution T H T Business H Statistics: H A DecisionMaking Approach, 6e © 2005 Prentice-Hall, Inc x Value Probability 1/4 = 25 2/4 = 50 1/4 = 25 Probability possible outcomes Let x = # heads .50 25 x 4-31 Chap Discrete Probability Distribution A list of all possible [ xi , P(xi) ] pairs xi = Value of Random Variable (Outcome) P(xi) = Probability Associated with Value xi’s are mutually exclusive (no overlap) xi’s are collectively exhaustive (nothing left out) P(xi) for each xi Business Statistics: A Decision P(x ) = i Making Approach, 6e © 2005 Prentice-Hall, Inc Chap 4-32 Discrete Random Variable Summary Measures Expected Value of a discrete distribution (Weighted Average) E(x) = xi P(xi) Example: Toss coins, x = # of heads, compute expected value of x: x P(x) 25 50 25 E(x) = (0 x 25) + (1 x 50) + (2 x 25) Business Statistics: = 1.0A Decision- Making Approach, 6e © 2005 Prentice-Hall, Inc Chap 4-33 Discrete Random Variable Summary Measures (continued ) Standard Deviation of a discrete distribution σx {x E(x)} P(x) where: E(x) = Expected value of the random variable x = Values of the random variable P(x) = Probability of the random variable having the value ofAxDecisionBusiness Statistics: Making Approach, 6e © 2005 Prentice-Hall, Inc Chap 4-34 Discrete Random Variable Summary Measures (continued ) Example: Toss coins, x = # heads, compute standard deviation (recall E(x) = 1) σx {x E(x)} P(x) σ x (0 1)2 (.25) (1 1)2 (.50) (2 1)2 (.25) 50 .707 Possible number of heads = 0, 1, or Business Statistics: A DecisionMaking Approach, 6e © 2005 Prentice-Hall, Inc Chap 4-35 Two Discrete Random Variables Expected value of the sum of two discrete random variables: E(x + y) = E(x) + E(y) = x P(x) + y P(y) (The expected value of the sum of two random variables is the sum of the two expected values) Business Statistics: A DecisionMaking Approach, 6e © 2005 Prentice-Hall, Inc Chap 4-36 Covariance Covariance between two discrete random variables: σxy = [xi – E(x)][yj – E(y)]P(xiyj) where: xi = possible values of the x discrete random variable yj = possible values of the y discrete random variable P(xi ,yj) = joint probability of the values of x i and yj occurring Business Statistics: A DecisionMaking Approach, 6e © 2005 Prentice-Hall, Inc Chap 4-37 Interpreting Covariance Covariance between two discrete random variables: xy > x and y tend to move in the same direction xy < x and y tend to move in opposite directions xy = x and y not move closely together Business Statistics: A DecisionMaking Approach, 6e © 2005 Prentice-Hall, Inc Chap 4-38 Correlation Coefficient The Correlation Coefficient shows the strength of the linear association between two variables σxy ρ σx σy where: ρ = correlation coefficient (“rho”) σxy = covariance between x and y σx = standard deviation of variable x Business Statistics: A Decisionσy = standard deviation of variable y Making Approach, 6e © 2005 Prentice-Hall, Inc Chap 4-39 Interpreting the Correlation Coefficient The Correlation Coefficient always falls between -1 and +1 =0 x and y are not linearly related The farther is from zero, the stronger the linear relationship: = +1 x and y have a perfect positive linear relationship = -1 x and y have a perfect negative linear relationship Business Statistics: A DecisionMaking Approach, 6e © 2005 Prentice-Hall, Inc Chap 4-40 Chapter Summary Described approaches to assessing probabilities Developed common rules of probability Used Bayes’ Theorem for conditional probabilities Distinguished between discrete and continuous probability distributions Examined discrete probability distributions and their summary measures Business Statistics: A DecisionMaking Approach, 6e © 2005 Prentice-Hall, Inc Chap 4-41 ... between discrete and continuous probability distributions Compute the expected value and standard Business Statistics: A Decisiondiscrete probability distribution Makingdeviation Approach, for 6e... possible from a simple experiment Sample Space – the collection of all possible elementary outcomes Business Statistics: A DecisionMaking Approach, 6e © 2005 Prentice-Hall, Inc Chap 4-3 Sample Space... collection of all possible outcomes e.g All faces of a die: e.g All 52 cards of a bridge deck: Business Statistics: A DecisionMaking Approach, 6e © 2005 Prentice-Hall, Inc Chap 4-4 Events Elementary