Regression modeling in practice - Week 2 - Basic linear regression model
In the context of the regression modeling practice course from Wesleyan University, I am going to test a basic linear regression model. The informations about the sample, procedure and measures used in this post are detailed in the previous post (here).
The response variable (alcohol abuse/dependence) is caterogical with 4 scales (0 to 3). For this assignment, the variable has been considered as quantitative. The explanatory variable used in this assignment is the major depression, which is a categorical variable with 2 categories (0=No and 1=Yes).
The image below shows the program I used to subset my data (employed individuals aged from 30 to 40), create some variables to better calculate what I want to analyze (alcohol abuse/dependence during lifetime), and convert the categorical scales of my response variable into quantitative variable.
After the completion of these data, I created a frequency table for the chosen explanatory variable (major depression) and tested a linear regression.
The frequency table shows that there are more individuals who did not face major depression during lifetime (variable value = 0).
The results of the linear regression model indicates that major depression has a positive correlation with alcohol abuse/dependence (beta = 0.5 and p=3e-68).
