Interactive graphics for data analysis

TL;DR: A new projection pursuit algorithm for exploring multivariate data is presented that has both statistical and computational advantages over previous methods and the emphasis here is on the discovery of nonlinear effects such as clustering or other general nonlinear associations among the variables.

TL;DR: The cartographic knowledge of Descartes allows non-cartographers to receive proper presentations of their data, and the automation of map construction helps the users to save valuable time that can better be used for data analysis and problem-solving.

TL;DR: The graphic portrayal of quantitative information has deep roots, which reach into histories of thematic cartography, statistical graphics, and data visualization, which are intertwined with each other.

TL;DR: This article proposed a path model for education and stratification, which is one of the most influential applications of statistical modeling technique to social data and has been shown to be useful in many applications.

TL;DR: In this article, Lindley et al. make the less restrictive assumption that such a normal, homoscedastic, linear model is appropriate after some suitable transformation has been applied to the y's.

TL;DR: Robust locally weighted regression as discussed by the authors is a method for smoothing a scatterplot, in which the fitted value at z k is the value of a polynomial fit to the data using weighted least squares, where the weight for (x i, y i ) is large if x i is close to x k and small if it is not.

TL;DR: In this article, a nonparametric multiple regression (NMM) method is presented, which models the regression surface as a sum of general smooth functions of linear combinations of the predictor variables in an iterative manner.