To make a linear regression model, the variability of Publications variable was studied when related with the Gender, R_user, Python_user, and Age variables.
Linear Regression Model
In linear regression model, the functional relationship between the dependent variable and the independent variables 
are (Maroco, 2011):
In this model, 
are the regression coefficients and 
is the errors or residuals of the model. 
is the y-intercept, and 
represents the partial slopes (i.e. a measure of the influence of 
in 
, i.e., the 
variation per variation unit of 
). The term 
(errors or residuals of the model) reflects the measurement errors and the natural variation in 
. If there is only one independent variable, the model is called simple linear regression model. If the model has more than one independent variable, it is called multiple linear regression model.
Considering that the population is not defined, the linear regression analysis should start with the estimation of the regression coefficients from a representative sample of the population under study, using the estimators
producing sample estimates
of the population's parameters
. The commonly used methods for estimation of these coefficients are mostly very laborious. Most software has extensive modules for linear regression. Thus, they eliminate the task of estimating these parameters; its detailed presentation will not be available in this book.