The adjusted r-squared is helpful for multiple regression and corrects for erroneous regression, giving you a more accurate ...

Watson (1955) investigated the performance of a regression analysis based on the assumption that the error covariance matrix is σ2γ when it is, in fact, σ2α. In ...

We review recent results for high-dimensional sparse linear regression in the practical case of unknown variance. Different sparsity settings are covered, including coordinate-sparsity, group-sparsity ...

Linear regression remains a cornerstone of statistical analysis, offering a framework for modelling relationships between a dependent variable and one or more independent predictors. Over the past ...

Description: Use of existing computer programs and subroutines to solve statistical problems. Areas of application include analysis of variance, linear and nonlinear regression analysis, nonparametric ...

In most GWAS, the participants are assumed to be unrelated and to come from a single population. However, even in carefully designed studies, some degrees of relatedness and population stratification ...

Description: This course will explore fundamental concepts of statistical methods and their application in biological research. The following topics will be included: experimental and sampling designs ...