Recent content by stats_student

let me try in notation \hat{y}(\bar{x})=\bar{Y}

ahhh... so after doing some algebra i get yhat(Xbar) = Y(bar)

should i get y(Xbar) = a(hat)+b(hat)X(bar)

or should i get, Y(bar) = a(hat) +b(hat)X(bar)

y(hat)(Xbar) = Y(bar)? still hopeless at notation :(

so if i do this should i get \hat{y} =Y(bar)?

Homework Statement note a linear regression model with the response variable Y=(Y1..Yn) on a predictor variable X=(X1..Xn). the least squares estimates of the intercept and slope a(hat) and B(hat) are the values that minimize the function: (see attached image) and the problem reads on further...

Or perhaps i could ignore the bottom part of my curve and do a least squares regression on the upper part only? not really sure if I'm thinking about this the right way. any input would be appreciated. Cheers.

i was thinking maybe a method where i attach weights to each point so that the upper part of the curve is matched more closely? Does a formal procedure for that exist?

Oh also the equation i have used is also shown in the picture.

Hi guys, i have been tasked with matching the upper a theoretical curve (seen in the picture-blue) to the upper experimental part. So far in an attempt to do this i have tried changing a single parameter in the equation i used to generate the theoretical curve, so that the sum of the differences...

Thanks BvU.

Ah, so i think homoscedasiticty cannot be assumed but the assumption of linearity may still be good however there will be a lot of inaccuracy in the model at higher DV values?

the question that I'm asked is based solely on what the residual plot looks like which is in the file attached. It asks if there are any issues with the assumption of linearity and/or homoscedasticity. not really sure what to conclude...

what if i just had residuals against fitted values, and the pattern began to fan out towards the right of the residual plot. Does this tell me anything about linearity? i.e can i assume linearity is appropriate for my model? Or should i assume non linearity instead?