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

Proper handling of continuous variables is crucial in healthcare research, for example, within regression modelling for descriptive, explanatory, or predictive purposes. However, inadequate methods ...

Learn how to build a multivariate linear regression model step by step—no libraries, just pure C++ logic!

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 ...

Nothing is set in stone with a regression model, but if the data you feed it is very good, the prediction will be good, too. What sort of data is required for machine learning regression?

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

Regression models for competing risks data analysis The most commonly used regression model for analyzing event-time data is the Cox proportional hazards model.

Compared to standard linear regression, which predicts a single numeric value based only on a linear combination of predictor values, linear regression with interactions can handle more complex data ...

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 ...