Population- universe or “totality of items or things” Sample- portion of the universe that has been selected for analysis Statistical Inference- The process of using sample statistics to draw conclusions about true population Normal Distribution- mean, median, mode same; 68.26% betweem +1 -1 s.d. from mean; Not discrete Power of a test- NOT to make Type 1 Error Standardized Form, normal distribution- has a mean of zero and a s.d. of one Central Limit Theorem is important in stats b/c for a large n, it says that the sampling distribution of the sample mean is approximately normal, regardless of the shape of the population. The s.d. of the sampling distribution of x is also known as the standard error of the mean. P(B|A) = P(A|B) * P(B) / P(A) P(A|B) = P(B|A) * P(A) / (B) E(x) = ∑xP(x) variance= ơ 2 = ∑(x-E(x)) 2 P(x) Standard Dev = ơ 1/2 E(x) = Return = risk ơ Co-variance ơ xy = ∑(x-E(x))(y-E(y))P(xy) negative Co-variance is ok ơ 2 x+y = ơ x 2 + ơ y 2 +2 xy ơ Portfolio Expected Return = E(p) = wE(x) + (1-w)(E(y))
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