We have discussed the basis of linear regression as fitting a straight line through a plot of data. However, there may be circumstances where the relationship between the variables is non-linear (i.e.

Exploratory data analysis plays a central role in applied statistics and econometrics. In the popular regression-discontinuity (RD) design, the use of graphical analysis has been strongly advocated ...

One option for working with survey data in R is to use the “survey” package. For an introduction on working with survey data in R, see our earlier blog post. The first step involves creating a survey ...

The R package rcssci offers an intuitive solution for visualizing Restricted Cubic Splines (RCS) in regression analyses. It automates the generation of spline plots for outcomes like odds ratios (OR), ...

One of the simplest prediction methods is linear regression, in which we attempt to find a 'best line' through the data points. Correlation and linear regression are closely linked—they both ...

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, ...

To calculate the sum of squares, subtract the mean from the data points, square the differences, and add them together. There are three types of sum of squares: total, residual, and regression.

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.