Hi all,
I am having trouble understanding how to interpret the standard error of
the regression (aka residual standard error, in R lingo). In section
notes, it says that we can analyze the meaning of our standard error of
the regression in comparison with the range, standard deviation, min and
max of the dependent variable. But how are we comparing these? Are we
looking for values below, similar to or above these values? I feel like
there is a conceptual link that I am having trouble making here.......
Also, I am not sure in problem three how to "examine and interpret" (Fox,
96) the intercept. I understand the information the slop is giving us but
what does the intercept tell us about the relationship between x and y
suggested by the regression line?
Thanks,
Becky
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Greetings,
I think you can interpret the standard error of the regression as how
closely do the points in the graph come to the line, or the standard error
of the residuals. Another way to think about it is to remember how the
standard error of the regression is calculated. Essentially, the
residuals of the y values are used to calculate the value, so if the
residuals are small, this will yield a small standard error, and vice
versa. In regards to the intercept of the graph, I think you only need to
interpret the intercept in the graph when it makes sense to. For example,
it sort of makes sense to interpret the intercept in 7 because you have a
range of values and 0 is significant (identifying a conservative justice).
Therefore, you can know the amount of liberal dialogue in the decisions of
the most conservative justices through the intercept. This is about as
far as I have gotten with things.
Regards,
Sheldon