Random variables
Random variables with zero correlation are not necessarily independent. Give a simple example.
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Let X be a normally-distributed random variable with
Mean zero. Let Y = X^2. Obviously, X and Y are not independent: knowing X, gives the value of Y.
The covariance of X and Y is Cov(X,Y) = E(XY) - E(X)E(Y) = E(X^3) - 0*E(Y) = E(X^3) = 0,
because the distribution of X is symmetric around zero. correlation r(X,Y) = Cov(X,Y)/Sqrt[Var(X)Var(Y)] = 0, the random variables are not independent, but correlation is zero.
Explain sampling bias and describe how random sampling serves to avoid bias in the process of data collection.
Define the term Frequency Distributions?
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Instructions: Do your work on this question and answer sheet. Please print or write legibly, and, as always, be complete but succinct. Record your answer and your supporting work in the designated space. Explain your method of solution and be sure to label clearly any
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what are the advantages and disadvantages of seasonal variation
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