We have the following data:1. To generate point estimates of , calculate the following: - x - y - Cov(
Y,X) - Var(X) - ^1 - ^0 Hint: try making a table like we did on the board in class to keep track of
things like 2. To examine goodness-of-fit of the model, calculate: - SST, the sum of squares total
for the variable you are trying to explain, Y- SSE, the sum of squares explained - SSR, the sum of
squares residual - R2 using SST,SSE, and SSR - Does the model explain much of the total
variance? 3. For inference, calculate the following: - y^ using results from 2 . - u^ - ^U2 (remember
to use n2) - SSTx - se(^1) - se(^0) (we did not derive this in class; see Wooldridge) 4. Test the
following hypotheses: - H0:1=0 - H0:0=0.
We have the following data1 To generate point estimates.pdf
1. We have the following data:1. To generate point estimates of , calculate the following: - x - y - Cov(
Y,X) - Var(X) - ^1 - ^0 Hint: try making a table like we did on the board in class to keep track of
things like 2. To examine goodness-of-fit of the model, calculate: - SST, the sum of squares total
for the variable you are trying to explain, Y- SSE, the sum of squares explained - SSR, the sum of
squares residual - R2 using SST,SSE, and SSR - Does the model explain much of the total
variance? 3. For inference, calculate the following: - y^ using results from 2 . - u^ - ^U2 (remember
to use n2) - SSTx - se(^1) - se(^0) (we did not derive this in class; see Wooldridge) 4. Test the
following hypotheses: - H0:1=0 - H0:0=0
