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Cheat Sheet - Standard Normal Distribution Mean = 0...

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Random Experiment: Different outcomes are obtained in repeated trials. Sources of Variability: Manufacturing Process; Environment; Measurement Instrument; Purity; Heat input. Sample Space: The set of all possible outcomes in a random experiment. Discrete: Whole numbers, i.e. the number of cars in an intersection. Continous: Any numbers, i.e. the timer on the rocket timer. Union (E 1 E 2 ): Combines outcomes of E 1 and E 2 Intersection (E 1 E 2 ): Includes outcomes that are in common in E 1 and E 2 Complement (E 1 ’): Contains outcomes not in E 1 Mutually Exclusive: If events do not have any outcomes in common, i.e. they can’t occur at the same time: E 1 E 2 = 0 Exhaustive Events: E 1 E 2 = S Permutations: Order is important Combinations: Order is irrelevant Conditional Probability: P(B | A) = P(A B) / P(A) Independence: The occurrence of one event has no effect on the other , i.e. P(A | B) = P(A); P(B | A) = P(B); P(A B) = P(A)*P(B) Normal Probability Plot: Shows if the data is approximately normally distributed.
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Unformatted text preview: Standard Normal Distribution: Mean = 0; Variance = 1 Skew: Median < Mean = Right Skewed; Median > Mean = Left Skewed Notation: X ~ N(10,5 2 ), 10 is the mean, 5 is the SD. Variance of Uniform Distribution: ((b – a + 1) 2 – 1) / 12 = V(x) on [a,b] Binomial Distribution: Random experiment consisting of n repeated trials which satisfy: Independent trials; Each trial has two outcomes, success and failure; probability of a success P in each trial is consistent. Mean of Binomial Distribution = n*p Variance of Binomial Distribution: n*p – (n*p) 2 Poisson Process: A random experiment is defined as a Poisson process if: The probability of more than one occurrence is negligible; the occurrences of the event in non-overlapped sub-intervals are independent; the probability of one occurrence of the event in a sub-interval is the same throughout all sub-intervals and proportional to length....
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