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HW Set # 4
HW Set # 4

... -.788 to 2.788 and includes the values=0, x=1,x=2. Now sum all the values of the relative frequency or probability to determine the interval the population value falls within. Ie P(0)+p(1)+p(2)= .32768+.40960+.20480= .94208. The value shows agrees with the empirical rule that 95% will lie within the ...
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... Suppose X1 ,X2 ...,Xn are i.i.d. with finite E(Xi) = . Then, as n , X n converges to  in probability. ...
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Law of large numbers



In probability theory, the law of large numbers (LLN) is a theorem that describes the result of performing the same experiment a large number of times. According to the law, the average of the results obtained from a large number of trials should be close to the expected value, and will tend to become closer as more trials are performed.The LLN is important because it ""guarantees"" stable long-term results for the averages of some random events. For example, while a casino may lose money in a single spin of the roulette wheel, its earnings will tend towards a predictable percentage over a large number of spins. Any winning streak by a player will eventually be overcome by the parameters of the game. It is important to remember that the LLN only applies (as the name indicates) when a large number of observations are considered. There is no principle that a small number of observations will coincide with the expected value or that a streak of one value will immediately be ""balanced"" by the others (see the gambler's fallacy)
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