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Unformatted text preview: Random Variables – the numerical values a variable assumes in a randomized experiment, each possible outcome has a specific probability of occurring Probability distribution – specifies the possible values and probabilities of random variables, described by mean and standard deviation, you always want to know the center and spread of the distribution Discrete random variable – when a random variable has separate possible values (i.e. a coin), probability is between 0 and 1 Parameters – summary measures for population distributions Expected value – weighted average of probability distribution, μ = n 1 (p) + n 2 (p) + n 3 (p) Continuous probability distributions – can take any shape and then you find the probability of an interval Normal Distribution – standard bell curve (i.e. 68%, 95%, 99.7%) Standard normal distribution – converting all probabilities to zscores (z = (obsmean)/sd) Binomial distribution – used when outcomes are binary, trials are independent, number of successes can range from 0 to...
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This note was uploaded on 04/12/2010 for the course ILRST 2100 at Cornell.
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