Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels.

Course Topics"Generalized Linear Models," Tuesday, April 6: The third LISA mini course for the 2010 Spring semester focuses on appropriate model building using generalized linear models. While ...

It emphasizes the importance of linear multivariate statistical models, particularly autoregressive (AR) models and the Kalman filter, in time series modeling and prediction of cerebral processes. The ...

Finally, we extend the methodology to apply generalized linear models and principal components to standard missing data problems. Series B (Statistical Methodology) of the Journal of the Royal ...

Linear Models (LM) are one of the most commonly used statistical methods to analyze continuous outcomes. However, many studies in Engineering, Medical Study, Education, etc. involve categorical ...

Sound Bites • The development of generalised linear models (GLMs) led to other important advances in statistics, particularly when the assumption of independence between responses is violated.

The Canadian Journal of Statistics / La Revue Canadienne de Statistique, Vol. 43, No. 3 (September/septembre 2015), pp. 358-377 (20 pages) Diagnostics for heteroscedasticity in linear regression ...

Linear and logistic regression models are essential tools for quantifying the relationship between outcomes and exposures. Understanding the mathematics behind these models and being able to apply ...