I have a nominal categorical predictor and a continuous dependent variable..I want to perform linear regression using lm in R. If the contrasts are such that the resulting dummy variables are uncorrelated then the regression is merely the direct linear combination of dummy variables weighted by their respective coefficients obtained from regression of continuous variable with individual dummy variable..To have this advantage what way should the categorical predictor be contrast coded?I found this method here .. It is helpful but the only problem is the order seems to be important here..The relation between only adjacent categories can be interpreted from the result of linear regression..
So my question is - for nominal categorical predictor is there anyway to get good insights about dependent variable at category level of the predictor from regression analysis.
I'd like to provide some clarifications here
Why do i need uncorrelated dummies?
bcoz in case of uncorrelated dummies i need not worry about which dummy enters the regression model first. The p value for the dummy1 is different when it enters the model second when compared to that when it enters first..By 'enters the model' i mean stepwise linear regression..So to avoid that problems i want them to be uncorrelated.
But if you see the pain vs treatment regression from the link provided by me the order certainly matters while doing contrast coding..I have no prior knowledge of the categories of my nominal category variable..so i cant order them like in pain vs treatment case. For more details - my dependent variable is Sales and category variable is product category which has 15 categories.