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 ...

A linear regression is a statistical model that attempts to show the relationship between two variables with a linear equation. A regression analysis involves graphing a line over a set of data ...

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 ...

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 ...

The least-squares criterion is a method of measuring the accuracy of a line in depicting the data that was used to generate it. That is, the formula determines the line of best fit.

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.