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.
Discuss the following statements and explain why they are true or false: a) Increasing the number of predictor variables will never decrease the R2 b) Multicollinearity affects the int
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what are the advantages and disadvantages of seasonal variation
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The table below illustrates the relationship between two variable X and Y. A
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The number of trucks coming to a certain warehouse each day follows the Poisson distribution with λ= 8. The warehouse can handle a maximum of 12 trucks a day. What is the probability that on a given day one or more trucks have to be sent away? Round the answer
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Activity 10: MANOVA and Reflection 4Comparison of Multiple Outcome Variables This activity introduces you to a very common technique - MANOVA. MANOVA is simply an extension of an ANOVA and allows for the comparison of multiple outcome variables (again, a very common situation in research a
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