Hi - Is it possible or advisable to attempt to improve the fit of my multiple linear regression model by also incorporating an ordinal variable? My current model contains 2 continuous explanatory variables and one continuous response variable. I know that in the real world the ordinal variable DOES matter and clearly effects the response variable...I'm just not sure how to capture that effect. For context, I am modeling (i.e. predicting) how ecoli (response variable) is affected by turbidity (continuous), rainfall in certain nearby locations (continuous), and other variables that did not improve the model fit. I know, however, that the point at which the river level increases (referred to as rising stage) typically contains higher ecoli due to initial washoff from the watershed and often this interval of rising stage contains more ecoli than even at peak stage. We know this from observations. So....these instances of rising stage should, ideally, be captured in the model, and it seems that the best way to do this is to use an ordinal variable (e.g. rapid rise = 2, slow rise = 1, no rise, very slow rise, or fall = 0). So far so good. But I'm not sure how to incorporate this into my predictive model of "ecoli" vs "continuous and ordinal variables". Any suggestions or direction on this? Thanks in advance.
