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... The Central Limit Theorem There are many Central Limit Theorems. We state two in terms of box models. The second is a special case of the first and it covers the model we are dealing with in our stick tossing problem. It goes back to the early eighteenth century. When drawing at random with replace ...
An elementary introduction to large deviations
An elementary introduction to large deviations

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Dp2007-08 - Research portal

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... Ex. 7 A survey showed that 47 % of students worked during the summer. Of those who worked, 62 % watched 2 hours or more of TV per day. Those who didn’t work, 79 % watched 2 or more hours of TV. What is the probability that you choose a random student who watched fewer than 2 hours of TV per day. ...
Integers and the Number Line
Integers and the Number Line

Document
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Intro to Integers
Intro to Integers

... numbers. For any two numbers graphed on a number line, the number to the right is the greater number and the number to the left is the smaller number. ...
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Number Concepts Review notes

Which numbers are not integers?
Which numbers are not integers?

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Probability and Graph Theory

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Unit 5 Test Name: Part 2 (Exponential Functions) Block: ______ A

... Which statement about the function is true? a. The range is the set of all real numbers less than 0. b. The domain is the set of all real numbers greater than -4. c. The range is the set of all real numbers greater than 0. d. The domain is the set of all real numbers less than -4. ...
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A Continuous Analogue of the Upper Bound Theorem

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Year 8 Maths Knowledge Map – Spring Term Key Word Definition

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Dice Probabilities, Intro to Binomial Probabilities

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Activity 5 - InterMath

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Chapter 3. POPULATION DISTRIBUTIONS

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Simplifying Rational Expressions

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A Continuous Analogue of the Upper Bound Theorem

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A Continuous Analogue of the Upper Bound Theorem

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Vocabulary Flashcards

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7 CONTINUOUS PROBABILITY DISTRIBUTIONS

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Probability Lab

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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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