The F value is used to perform what's called an F-test and from it is derived the Pr(F) value, which describes how likely (or Probable = Pr) that F value is. If you require further details, you will have to turn to dead trees with words on them (i.e., books). These are a measure of how much the predicted value of the dependent (or output) variable varies from the true value for each data point in the set (or more colloquially: each "line" in the data table).ĪIC is the Akaike information criterion which is generally regarded as "too complex to explain" but is, in short, a measure of the goodness of fit of an estimated statistical model. In short this is a measure of the amount that each individual value deviates from the overall mean of those values. The Sum of Sq column refers to the sum of squares (or more precisely sum of squared deviations). Your model should reflect your hypothesis and not the other way around.įor reference, these are the values that are included in the table:ĭf refers to Degrees of freedom, "the number of degrees of freedom is the number of values in the final calculation of a statistic that are free to vary." So again, do not base yourself on the p-values. Stepwise regression is incredibly tricky, jeopardizing your p-values in a most profound manner. I'm trying not to be rude, but if you don't understand what is explained in the help files there, you shouldn't be using the function in the first place. For the anova() function, it's rather obvious that type I and type II SS are not the same. For AIC(), that information is given on the help pages of extractAIC(). Mind you, the calculation of both AIC and the F statistic are different from the R functions AIC(lm1) resp. So the best model from that output would be the one without the variable examination. AIC is a value that goes for the model, not for the variable. (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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