Discover how linear regression works, from simple to multiple linear regression, with step-by-step examples, graphs and real-world applications.

The least squares approach is a popular method for determining regression equations, and it tells you about the relationship between response variables and predictor variables.

Multiple regression equations designed to explain or predict should be validated. This tutorial shows how recalculation of the coefficient of determination on hold-out sample data or new sample data ...

In our study, we refer to meta-regression analysis as a method to develop a single regression equation that summarizes the findings of multiple regressions found in a number of studies.

This note presents a simple way to estimate time equations using regression analysis in Excel. The note quickly outlines regression analysis, then presents a real-life case example from the natural ...

10.1 Kitchen sink model We can extend the lm (y~x) function to construct a more complicated “formula” for the multi-dimensional model: lm (y ~ x1 + x2 + ... + xn ). This tells R to find the best model ...

Linear regression analysis or linear least-squares fitting (LLSF) refers to regression equations that are linear in their parameters (this, of course, includes but is not limited to equations that ...

Block-recursive regression equations are derived as the key to understanding the relation between two main approaches, between graphical chain models for continuous variables on the one hand and ...

If the dots tightly adhere to the zero baseline, the regression equation is reasonably accurate. If the dots are wildly scattered, the regression equation may have limited usefulness.