Linear Regressions are a statistical tool that can be used to predict and model the relationship between an independent and a dependent variable. In Geosciences, this is often used for simple trend analysis models or to quantify the relationship between two variables. Linear Regressions are so easy to implement yet useful for all kinds of research, that they are among the most popular and widely used statistical tools in data science.

 

Advantages:

Basic mathematic concept is rather easy to understand and interprete

Can be applied on almost any research question, easy to implement

 

Disadvantages:

Many real-world-problems are not linear

Outliers might have a big impact on the result

 

Advancing Literature to dive deeper into the theoretical part:

Weisberg, S. (2005): Applied Linear Regressions. Wiley Series in Probability and Statistics. Third Edition.

Kumar, K., Yadav, S. (2018): Linear Regression Analysis Study. In: Journal of the Practice of Cardiovascular Sciences. 4.