![]() ![]() If the idea of hidden nominal variables in regression confuses you, you can ignore it. I think this rule helps clarify the difference between one-way, two-way, and nested anova. The main value of the hidden nominal variable is that it lets me make the blanket statement that any time you have two or more measurements from a single individual (organism, experimental trial, location, etc.), the identity of that individual is a nominal variable if you only have one measurement from an individual, the individual is not a nominal variable. For that reason, I'll call it a "hidden" nominal variable. I'm not aware that anyone else considers this nominal variable to be part of correlation and regression, and it's not something you need to know the value of-you could indicate that a food intake measurement and weight measurement came from the same rat by putting both numbers on the same line, without ever giving the rat a name. There's also one nominal variable that keeps the two measurements together in pairs, such as the name of an individual organism, experimental trial, or location. Use correlation/linear regression when you have two measurement variables, such as food intake and weight, drug dosage and blood pressure, air temperature and metabolic rate, etc. ![]() For most purposes, just knowing that bigger amphipods have significantly more eggs (the hypothesis test) would be more interesting than knowing the equation of the line, but it depends on the goals of your experiment. ![]()
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