10 Statistical Techniques Data Scientists Should Master AISOMA AG Frankfurt

The more statistical techniques a Data Scientist has mastered, the better the results can be. In this blog article, we want to introduce you to ten common techniques that should not be missing in the repertoire of a data scientist. In statistics, linear regression is a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression.