Lecture1-1

Lecture1-1 - BUAD 310 Applied Business Statistics 1/11/10...

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BUAD 310 Applied Business Statistics 1/11/10
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Probability vs Statistics Example 1: Variation among all individuals The population of all USC college students each counting equally – a probability space . The number of courses each student is enrolled in this semester -- a random variable (r.v.), which could be 1, 2, 3, etc. This r.v. is theoretically accessible out there but may not be readily known to us. With the information for each student available, probability guides us to figure out the mean of the above r.v. (e.g., 5). 2
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Probability vs Statistics What if it’s not realistic for us to get the information for each and every student? Statistics can guide us to randomly select a subpopulation of the students. A new probability space with each student counting equally and a new r.v. which is now known to us. Averaging those numbers gives nothing but the mean of this new r.v. (e.g., 4.5). 4.5 is close to 5. We reduced cost and saved time while still getting reliable information about the whole population . 3
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Probability vs Statistics sampling A sample of population units inference The whole population data r.v. population distribution statistics 4
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Probability vs Statistics Ex 2: Uncertainty The closing price of Google stock last Friday was $602.06. This r.v. is completely unknown to us. 602.06 - price closing today's ay return tod Its -- a r.v. 5
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Probability vs Statistics We can make inference on its mean and variance using the past data of daily returns (e.g., within last year, 252-1 = 251). A new probability space with each return counting equally (i.e., equally likely to occur ) and a new r.v. which is now known to us. Infer based on this new probability space . One might conclude with a certain level of confidence that today’s return will be within [-0.5%,1.5%].
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Lecture1-1 - BUAD 310 Applied Business Statistics 1/11/10...

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