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**Unformatted text preview: **Click to edit Master subtitle style Page ‹#› Lecture #4: Statistics Tutorial (Part 2 of 2) Statistics Tutorial (Part 2 of 2) 1 1 Lecture #4: Statistics Tutorial (Part 2 of 2) Page ‹#› Lecture #4: Statistics Tutorial (Part 2 of 2) Statistics Tutorial (Part 2) What we learned last time expected value, variance, standard deviation, covariance, correlation, and skewness discrete and continuous probability distributions Bernoulli processes and the binomial distribution Inferring probabilities from market prices (prediction 2 2 Lecture #4: Statistics Tutorial (Part 2 of 2) Page ‹#› Lecture #4: Statistics Tutorial (Part 2 of 2) In this lecture. . . Central Limit Theorem Normal Distribution 3 3 Lecture #4: Statistics Tutorial (Part 2 of 2) Page ‹#› Lecture #4: Statistics Tutorial (Part 2 of 2) Abraham de Moivre (1667-1754) invented the Central Limit Theorem (CLT). Central Limit Theorem : The distribution of the mean value of a set of “ n ” independent and identically distributed random variables, each having mean μ and variance σ2, approaches a normal distribution with mean μ and variance σ2/ n as n tends toward infinity. In other words, the probability The Central Limit Theorem 4 4 Lecture #4: Statistics Tutorial (Part 2 of 2) Page ‹#› Lecture #4: Statistics Tutorial (Part 2 of 2) The Central Limit Theorem in Action 5 5 Lecture #4: Statistics Tutorial (Part 2 of 2) Page ‹#› Lecture #4: Statistics Tutorial (Part 2 of 2) The Central Limit Theorem in Action 6 6 Lecture #4: Statistics Tutorial (Part 2 of 2) Page ‹#› Lecture #4: Statistics Tutorial (Part 2 of 2) The Central Limit Theorem in Action 7 7 Lecture #4: Statistics Tutorial (Part 2 of 2) Page ‹#› Lecture #4: Statistics Tutorial (Part 2 of 2) The Central Limit Theorem in Action 8 8 Lecture #4: Statistics Tutorial (Part 2 of 2) Page ‹#›...

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