… If the null hypothesis can be rejected and if we assume a symmetric error distribution
under the true linear regression model, we can conclude that Y is the response and X is the …

… of data for determining regression models and with … linear regression model is proposed
using an enhancement of a minimal vagueness criterion already discussed in fuzzy regression …

… 5 provide guidance on building regression models, including … apply the linear regression
modeling techniques considered … to the multiple linear regression model and outlines some …

… When the values of x are not fixed, a form of Model II regression analysis should be used.
The trouble is that a very large number of forms of Model II linear regression analysis have …

… ∎ We will fit this model to data, and then look at some modifications of the … Regression -
Overdispersion ∎ We can also get R to estimate the overdispersed poisson regression model …

… ) linear regression models; Section 3 deals with models that possess interactions, and Section
4 deals with other sorts of models, such as generalized linear models… on linear regression, …

… linear regression models for panel data and illustrates how to estimate each model using
SAS … nonlinear models (ie, logit and probit models) and dynamic models, but focuses on basic …

… First we extend the Bayesian graphical model selection algorithm of Madigan and Raftery (…
to account for model uncertainty in variable selection for linear regression models. In addition …

… It is well known that when the assumptions of the linear regression model are correct,
ordinary least squares (OLS) provides efficient and unbiased estimates of the parameters. …

… We propose a new test for the constancy of regression coefficients in linear models. The
test does not require that possible change points be known. We derive the limiting null …