(multi-testing problem and all.)Īnd regarding the AIC : the lower the better seems more like it. Mind the "" around significantly, as the significance here cannot be interpreted as most people think. So what to do with it? Interprete it in exactly that way: it expresses in a way if the model without that term is "significantly" different from the model with that term. As long as you only have continuous variables, this table is exactly equivalent to summary(lm1), as the F-values are just those T-values squared. Please note the Community Wiki answer below and add to it if you see fit, to clarify this output.ĭrop1 gives you a comparison of models based on the AIC criterion, and when using the option test="F" you add a "type II ANOVA" to it, as explained in the help files. Looking at the output above, I want to throw away the "Examination" variable and focus on the "Education" variable, is interpretation this correct?Īlso, the AIC value, lower is better, yes?Įd. ![]() What does all of this mean? I'm assuming that the "stars" help in deciding which input variables are to be kept. ![]() ![]() These two commands should get you some output: In R, the drop1command outputs something neat.
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