In linear models we need to check if a relationship exists among the explanatory variables. If they correlate too much then there is collinearity (i.e., the variables partly explain each other). I am currently just looking at the pairwise correlation between each of the explanatory variables.
Question 1: What classifies as too much correlation? For example, is a Pearson correlation of 0.5 too much?
Question 2: Can we fully determine whether there is collinearity between two variables based on the correlation coefficient or does it depend on other factors?
Question 3: Does a graphical check of the scatterplot of the two variables add anything to what the correlation coefficient indicates?