In reply to Herman's response :
There is no reason to assume that the data are normal. For
linear regression to be exactly the MLE procedure, it is the
residuals from the true regression which need to have certain
properties. In well designed experiments, the independent
variables are
In article 8fhfuf$[EMAIL PROTECTED],
Steve Gregorich [EMAIL PROTECTED] wrote:
Mike,
As a demonsrtation to myself, I once fit OLS regression
models to data with (1) a non-uniformly distributed
binary outcome and (2) a continuous outcome with a
U-shaped distribution. I then used the same models
Mike wrote in message 8ffek1$1q2$[EMAIL PROTECTED]...
I would like to obtain a prediction equation using linear regression for
some data that I have collected. I have read in some stats books that
linear regression has 4 assumptions, 2 of them being that 1) data is
normally distributed and 2)
Hi Mike.
For the most popular linear regression Ordinary least squares (OLS), you
also need to have your X variable (i.e. the independent variable) having a
relatively small error. Your initial work suggests large-ish error in both
variables with non-normal error structure. This makes things a
PROTECTED]]
On Behalf Of Mike
Sent: Thursday, May 11, 2000 3:39 PM
To: [EMAIL PROTECTED]
Subject:normality and regression analysis
I would like to obtain a prediction equation using linear regression for
some data that I have collected. I have read in some stats books that
linear regression
In article 8ffek1$1q2$[EMAIL PROTECTED], Mike [EMAIL PROTECTED] wrote:
I would like to obtain a prediction equation using linear regression for
some data that I have collected. I have read in some stats books that
linear regression has 4 assumptions, 2 of them being that 1) data is
normally
In reply to Mike's question Allan makes the important point:
There is absolutely no requirement that the predictors (or
independent variables) should have a normal distribution, in fact
the opposite. Ideally, the predictors should be from a designed
experiment and hence will not even be
Mike:
It's really the error terms in the regression model that are required to
have normal distributions with constant variance. We check this by looking
at the properties of the residuals from the regression. You shouldn't expect
the response (dependent) variable to have a normal distribution
