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Unformatted text preview: Section 3.6 Joint Distributions Earlier we discussed how to display and summarize 1 , , n x x K on a variable x . We extended these ideas to the population distribution with density ( ) f x (continuous case) or mass function ( ) p x (discrete case). We now discuss the joint distribution for two variableas x and y . 1. Discrete Case . Let x and y be discrete variables. For example, x = number of courses taken y = number of hours spent (in a day) Definition . The joint distribution ( , ) p x y (or ( , ) f x y ) of x and y is defined by (i) ( , ) p x y x ; (ii) ( , ) 1 p x y = . Also, ( ) ( , ) y p x p x y = and ( ) ( , ) x p y p x y = are respectively called the marginal distribution of x and y . 1 The mean (or the expected value ) of ( , ) h x y is ( , ) ( , ) ( , ) h x y y x h x y p x y = . (Also, denoted by )) , ( ( y x h E .) Example 1 . The joint distribution of ) , ( y x p of x (number of cars) and y (the number of buses) per signal cycle at a particular leftturn lane is given by y p (x,y) 1 2 x 1 2 3 4 5 .025 .050 .125 .150 .100 .050 .015 .030 .075 .090 .060 .030 .010 .020 .050 .060 .040 .020 a) Find the proportion of cycles with the same number of cars and buses. b) Find the marginal functions of x and y . c) Suppose a bus occupies three vehicle spaces and a car occupies just one. What is the mean number of vehicle spaces occupied during a signal cycles?...
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This note was uploaded on 07/25/2008 for the course STT 351 taught by Professor Palaniappan during the Summer '08 term at Michigan State University.
 Summer '08
 Palaniappan

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