![]() 14.1.1 Recreating the graph with more manual labour.13.3 Other ways to visualize two continuous variables.13 Visualizing two continuous variables.12.4 An important note on mean-error-plots.12.3.3 Using the built-in mean_se() function.12.3.2 Creating your own “se” function within geom_errorbar().I imagine that if you get a sufficient model (solve the second point, that your linear relationship is bad), then you may possibly worry less about the values going negative (depending on how much you want every single value to be correct and use difficult stuff for this, or just get a simple and quick impression). In case you can't manage to distribute the variation in the residual terms and apply proper weights to the values at different income levels. Or use a different transformation than the log transform that you currently tried. ![]() You have sufficient degrees of freedom to add additional terms. Simply said, your current model gives everybody the same hourly salary. (currently the different groups act only as an intercept, but I can imagine that the increase of salary as function of worktime is not the same for say, a server in a burger place, a data scientist, and a ceo of a bank). Possibly the slope of the linear relationship with arbeidstid is different for different classes.
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