This can be extended to more than two explanatory variables. However, in practice it is best to keep regression models as simple as possible as it is less likely to violate the assumptions. - Multiple ...

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

Multiple linear regression is a classical statistics technique that predicts a single numeric value from two or more numeric predictor variables, for example, predicting income from age and height.

Regression is used to model the relationship between two variables and predict the value of one variable based on the value of the other variable. Correlation and Regression Formula ...

Estimating a fixed effects model is equivalent to adding a dummy variable for each subject or unit of interest in the standard OLS model. To illustrate equivalence between the two approaches, we can ...

David Oakes, John Ritz, Regression in a Bivariate Copula Model, Biometrika, Vol. 87, No. 2 (Jun., 2000), pp ... The former strategy does well if the explanatory variables are balanced over pairs and ...

Joel L. Horowitz, Sokbae Lee, Nonparametric Instrumental Variables Estimation of a Quantile Regression Model, Econometrica, Vol. 75, No. 4 (Jul., 2007), pp. 1191-1208 ...