1. Originally Posted by yoma819
why would i be only interested in the normality of my residuals?
The only reason we even care is because one of the assumptions we make when deriving the theory is that the errors are normally distributed. We don't care how the data itself is distributed because we say that once we adjust for our predictors the errors/residuals will be normally distributed.

I guess one way to see why this is what we care about is consider we are comparing two groups.
Code:
``````#data from first group
y1 <- rnorm(100,5)
#data from second group
y2 <- rnorm(100,100)
#data overall
y <- c(y1,y2)
hist(y) #clearly not normal
hist(y1) #once we adjust though they look normal
hist(y2)``````
Clearly the overall data isn't normally distributed... but who cares. Once we look at each group individually they look normal so we're all right. This is why we only care if the residuals are normally distributed.

2. I don't know what the "&#37;" is in the Y-axis, but it looks like you plotted it wrong. You're supposed to plot the fitted values against the residuals.

Edit: Looking at the graph again, I think I see what kind of graph it is. If it's what I think it is, then yes, you do have a problem.

3. ok got it:
Code:
``http://i884.photobucket.com/albums/ac50/yoma819/fittedresiduals.jpg``
so we have assertained that i do infact need to transform my data.
and advice on what kind of transformation?
thanks again
--yoma

4. Originally Posted by Dason
The only reason we even care is because one of the assumptions we make when deriving the theory is that the errors are normally distributed. We don't care how the data itself is distributed because we say that once we adjust for our predictors the errors/residuals will be normally distributed.

I guess one way to see why this is what we care about is consider we are comparing two groups.
Code:
``````#data from first group
y1 <- rnorm(100,5)
#data from second group
y2 <- rnorm(100,100)
#data overall
y <- c(y1,y2)
hist(y) #clearly not normal
hist(y1) #once we adjust though they look normal
hist(y2)``````
Clearly the overall data isn't normally distributed... but who cares. Once we look at each group individually they look normal so we're all right. This is why we only care if the residuals are normally distributed.
ok i understand why i am looking at the residuals now , thanks for clearing that confusion up.
i take it in your R code you are generating random data and putting it into y1?
Code:
``y1 <- rnorm(100,5)``
and then normally distributed data into y2

Code:
``y2 <- rnorm(100,100)``
but what does:
Code:
``y <- c(y1,y2)``
do?
sorry just trying to understand your R code!
cheers
Yoma

5. does anyone know of any software that will transform data automatically (like quickfit does for distributions)
i know quickfit is not 100% but it gives a great direction to go in and then further test.
many thanks
yoma