2201_Prelim_2_Review_Session_Final[1] (1)

2201_Prelim_2_Review_Session_Final[1] (1) - Click to edit...

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Unformatted text preview: Click to edit Master subtitle style 5/10/10 2201 Prelim 2 Review Session 5/10/10 Probability Distr ibutions Discrete Binomial Poisson Continuous Unifor m Exponential 5/10/10 Binomial Distr ibution Count Data Hard Cap Two possible outcomes (i.e. success/failure) I ndependent Probability I get 3 heads when I f lip a coin 5 times 5/10/10 Poisson Distr ibution Count data No Hard Cap I ndependent Probability that I get 3 people walk int o my restaurant in the next five minutes given that on average we can expect 2 people. 5/10/10 Unifor m Distr ibution All values equally likely Flat distr ibution X~U(a,b) where “a” and “b” are endpoints of an inter val in which all the values fall. 5/10/10 Exponential Waiting Time distr ibution Time between events Parameter = lambda Y~Exp(lambda) Relationship with Poisson X number of events over time and Y is the 5/10/10 Nor mal Distr ibution Most I mpor tant Distr ibution Bell Shaped Cur ve 5/10/10 Nor mal – Z Standardization Z= (Y-mean)/st andard deviation Allows you to compare between two different nor mal distr ibutions Can compare SAT score with ACT score distr ibutions As long as you have the mean and st. dev. for each. Someone who got a 1500 SAT: (1500-average)/st dev 5/10/10 Nor mal Z- example Nick got a 66 on a Chang Finance midter m where the average was a 55 and st dev was 3 Der r ick got a 95 on a Lloyd Stats midter m where the average was a 90 and st dev was 4 5/10/10 Nor mal Z-Example Nick: Z = (66-55)/3 = 3.667 Table Value: < .00023263 = Top 99.97 percentile Der r ick: Z = (95-90)/4 = 1.250 Table Value: .1056 = Top 89.44 percentile 5/10/10 T Distr ibution Bell Shaped Distr ibution with fatter tails than Nor mal Distr ibution Have a paramet er called degrees of freedom As sample size gets larger and larger, degrees of freedom get larger and larger and the tails get smaller and smaller 5/10/10 Statistical Techniques Statistics 1) Collecting Data = Sampling 2) Descr ibing & Ar ranging data = Descr iptive Stats 3) Analyzing & Drawing conclusions = I nferential Stats 5/10/10 1) Sampling Let’s Say: We are interested in the average weight of aut omobiles in the world Ever y t enth car that came through a t oll booth in Chicago was weighed We weighed 20 aut omobiles Descr ibe the sampling scheme: target pop, 5/10/10 1) Sampling Target: Sample: Sample Pop: Scheme: Parameter of int erest: Subjective I nference Needed?: 5/10/10...
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This note was uploaded on 04/28/2010 for the course HADM 2236 taught by Professor Spies during the Spring '09 term at Cornell.

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2201_Prelim_2_Review_Session_Final[1] (1) - Click to edit...

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