Continuous Probability Distributions

Continuous Probability Distributions - 1 of 5 Continuous...

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Continuous Probability Distributions 1 of 5 Objectives: By the end of this lesson, you should be able to do the following: 1. State the properties of a probability density function (continuous pmf). 2. State the properties of a continuous cdf. 3. Calculate probabilities of events in continuous situations. 4. Apply this general thinking to the special case of the Normal Distribution Function . Review We looked at discrete probability mass functions . Aside from a complicated notation, they really boil down to addition and subtraction. Underlying all the calculations was the idea that probabilities of the discrete sort can be related to an area calculation. The critical next step in going to a continuous situation is comparable to moving from a Riemann Sum to a definite integral. Example: The function for creates the geometric sequence . 1 () 2 x fx = { } 1, 2, 3, x { } 111 ,,, 248 S = 1. This is a discrete, infinite sequence. 2. You might recall that the sum of this infinite series has a limit of 1. 3. Further all values of x are bounded: . 0( ) 1 << Let’s use it by saying that for the random variable , the probability for any whole number x is given XS by . Now we have a pmf ! 1 2 x fX x == We can easily answer questions like this set: 1. Calculate the probability that . . 5 x = 5 11 (5 ) 32 2 fX = 2. Calculate the probability that x is 2, 4, or 6. 246 1112 1 ( {2,4,6}) 64 222 ∈= + + = 3. Write the associated cumulative distribution function. 1 1 2 x k k FX x = 4. Calculate the probability that . This equate to . This might seem like it’ll be 10 X (1 0 ) FX = tough. However, the sum for the first n terms of finite geometric sequences is calculated by . 0 1 1 n n ar S r = In our case . 22 1 2 1 2 1 1 1 x x x S = = From there it’s short jump to . 10 1 2 1023 ( 10) 1 1024 = © 2010, Arizona State University, School of Mathematical and Statistical Sciences
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Continuous Probability Distributions 2 of 5 Example: Look at . Find the k that { } () , 0 , 1 , 2 , 3 fX x k xx == makes this a pmf.
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This note was uploaded on 06/16/2011 for the course MAT 211 taught by Professor Seal during the Summer '08 term at ASU.

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Continuous Probability Distributions - 1 of 5 Continuous...

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