Learn how to use regression and correlation analysis to understand how multiple dependent and independent variables relate to each other and affect an outcome.

Multiple linear regression is a statistical technique that allows you to explore the relationship between a continuous dependent variable and two or more independent variables.

Learn the difference between linear regression and multiple regression and how investors can use these types of statistical analysis.

Linear regression analysis,also known as linear modelling entails fitting a straight line,a plane or polynomial to a data.Like most of the machine learning algorithms,the goal of linear regression ...

Regression with qualitative variables is different from analysis of variance and analysis of covariance. Analysis of variance uses qualitative independent variables only. Analysis of covariance uses ...

Multiple linear regression (MLR) is a statistical technique that uses several explanatory variables to predict the outcome of a response variable.

Multiple regression models add additional dimensions of independent variables, each with their own slopes. This can be helpful for identifying confounding variables and avoiding spurious associations, ...

One common problem in the use of multiple linear or logistic regression when analysing clinical data is the occurrence of explanatory variables (covariates) which are not independent, ie ...