This article considers the analysis of clustered data via partial linear regression models. Adopting the idea of modeling the within-cluster correlation from the method of generalized estimating ...

We propose nonparametric methods for functional linear regression which are designed for sparse longitudinal data, where both the predictor and response are functions of a covariate such as time.

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, ...

Model building via linear regression models. Method of least squares, theory and practice. Checking for adequacy of a model, examination of residuals, checking outliers. Practical hand on experience ...

Offers an alternative to Markowitz’s “Portfolio Selection”. Outlines the nuts and bolts of correlation between past and future performance, or between expected and actual returns. Explains ...

Negative coefficients indicate opposite direction of movement in most cases. The other key result is the correlation of the two. Regression statistics will typically include an R-squared value.

In my opinion, reversion to the mean is one of the most powerful, but least understood, concepts in statistics. Regression to the mean explains that in many cases data will tend to even out—i.e ...