Now that you've got a good sense of how to 'speak' R, let's use it with linear regression to make distinctive predictions.

R 2 (R-squared) is a statistical measure of the goodness of fit of a linear regression model (from 0.00 to 1.00), also known as the coefficient of determination.

The purpose of this tutorial is to continue our exploration of regression by constructing linear models with two or more explanatory variables. This is an extension of Lesson 9. I will start with a ...

Course Topics Ordinary linear regression (OLR) assumes that response variables are continuous. Generalized Linear Models (GLMs) provide an extension to OLR since response variables can be continuous ...

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THE “simplified” method of calculating a linear regression put forward by Aldridge, Berry and Davies 1 is the well-known method of orthogonal polynomials which was put on a practical working ...

When multiple variables are associated with a response, the interpretation of a prediction equation is seldom simple.

Statistical texts written by geographers invariably illustrate and calculate the prediction limits about an estimated regression line as pairs of parallel lines. Such limits should be hyperbolic when ...

Comparing regression coefficients between models when one model is nested within another is of great practical interest when two explanations of a given phenomenon are specified as linear models. The ...

Nonlinear regression is a form of regression analysis in which data is fit to a model and then expressed as a mathematical function. Simple linear regression relates two variables (X and Y) with a ...