Recent content by jasper90

I really don't know. Every problem we have done in class was done the reverse way. Like, I know for max likelihood estimator, we take the Ln of f(x) and then derive it. Then we set to 0 and solve for our estimator. But I have never had to choose one. I tried reversing the process, but it...

anyone?

Consider a uniform distribution on the interval 0≤ X ≤ θ. We are interested in estimated θ from a random sample of draws for the PDF. Two potential estimators are: θ1 = (2/n) Ʃ Yi and θ2 = (n/θ)(y/θ)^(n-1) which estimator would you prefer and why? What statistical properties did you...

I think this is right, just want to double check. Cov(Y1+Y2, Ʃ n i=2 Yi)= Cov(Y1, Ʃ n i=2) + Cov(Y2, Ʃ n i=2 Yi) = 0 + Var(Y2) = σ^2 is this right?

can someone help me with C and D? I am not rly sure what to do

help?

can someone help me out? i don't need the answer straight up, just need help even getting this started is this right for a)? yhat = E[y (conditional) x1, x2] = E[ B0+B1x1+B2x2+u (conditional) x1, x2] = B0+B1x1+B2x2

does my previous post look right?

Hi, I just editted my previous post, is that right?

ok, thank you, so now i have this Var( O hat) = var( B hat 1 + B hat 2) = var( b hat 1) + var( b hat 2) + 2 Cov( B hat 1, B hat 2) Now, I am supposed to have this in terms of Corr(B hat 1, B hat 2) also, how do I do that? This may sound dumb, but since Corr(x, y) = (cov(x,y))/( square root(...

can anyone help? or does anyone have the link to a similar problem?

mainly looking for help in B)...where do I even start?

Consider the multiple regression model containing three independent variables y = B0 + B1x1 + B2x2 + B3x3 + u You are interested in estimating the sum of the parameters on x1 and x2; call this O1 = B1 + B2 a) Show that O hat1 = B hat 1 + B hat 2 is an unbiased estimator of O1. b)...

I am sooo lost in this class, please help. 1. Let the true (population) model be y = B0+B1x1+B2x2+u where u is an unobserved error term with u (conditional) x1, x2 and N(0, sigma^2). Hence, u is normally distributed with mean 0 and variance sigma^2 (i.e., E[u (conditional) x1, x2] = 0 and V...