All of the models we've seen so far could also be set to tackle regression problems and not only classification problems. In order to do so, the only thing that we would need to do is to start the formulas with a continuous variable then. Instead of the regular vote ~ ., we would use <some continuous variable's name> ~ <independent variable #1> + <...> + <independent variable #n>.
A misspecified model is either missing important (left out) variables, adding unimportant (irrelevant) variables, or both.
The dot sign shortcut still works for regression problems, but it's probably best to name each variable by name. This way you pay more attention to which variables you are using. Depending on the model you train and sampling size, misspecification will badly injury the out-of-sample performance, in other words, your model...