Linear regression is a statistical method used to understand the relationship between an outcome variable and one or more explanatory variables. It works by fitting a regression line through the ...

For example, you might use regression analysis to find out how well you can predict a child's weight if you know that child's height. The following data are from a study of nineteen children. Height ...

Simple linear regression is commonly used in forecasting and financial analysis—for a company to tell how a change in the GDP could affect sales, for example. Microsoft Excel and other software ...

Wrapping Up The k-NN regression system presented in this article is the simplest possible version. In the early days of machine learning, several variations of k-NN regression were explored, for ...

In the example below, I use an e-commerce data set to build a regression model. I also explain how to determine if the model reveals anything statistically significant, as well as how outliers may ...

Linear ridge regression (LRR) is a relatively simple variation of standard linear regression. However, LRR and standard linear regression are usually considered distinct techniques, in part because ...

In the literature on tests of normality, much concern has been expressed over the problems associated with residual-based procedures. Indeed, the specialized tables of critical points which are needed ...

Linear Regression vs. Multiple Regression Example Consider an analyst who wishes to establish a relationship between the daily change in a company's stock prices and daily changes in trading volume.

In our example of simple linear regression 1, we saw how one continuous variable (weight) could be predicted on the basis of another continuous variable (height). To illustrate classification ...