Date: Dec 11, 2012 5:35 PM Author: Gary Subject: Re: Multiple regression with all dummy variables On Wednesday, 12 December 2012 00:22:38 UTC+2, Gary wrote:

> On Tuesday, 11 December 2012 20:20:48 UTC+2, paul wrote:

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> > Does a multiple regression with all dummy (indicator) variables make

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> > sense? I work at a state university tutoring various basic subjects

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> > including college algebra, first semester calculus, and a two-semester

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> > "Statistics for Business and Economics" sequence. In recent years my

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> > students have been taught that an alternative to using the ANOVA

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> > technique is to run a multiple regression analysis using all dummy

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> > variables. A recent example given as a study guide for the final exam

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> > was a comparison of used-car prices by color (white, black, blue, or

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> > silver.) Both ANOVA and a multiple regression (with black as the

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> > excluded category) reject the null hypothesis that there is no

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> > difference in prices by color. But the students are then told that the

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> > multiple regression gives more information since we can conclude from

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> > the t-tests on individual coefficients that silver cars sell for more

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> > than the base case (black.) I thought you needed at least one measured

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> > (scalar?) variable among the explanatory variables -- it makes no

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> > sense to do a scatter plot on just a dummy variable, so what on earth

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> > is this "line" (or surface) you are getting from the regression?

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> > So, is having at least one measured explanatory variable a basic

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> > requirement for regression? Has anyone proven that the individual

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> > coefficients on an all-dummy variable regression have no meaning?

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> > Perhaps they follow a well-defined distribution, which might not be

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> > Student's t. Any easy on-line sources? I did not see anything in basic

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> > article on regression in wikipedia.

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> > I'll mention that previously students were taught that, according to

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> > the Central Limit Theorem, if you are doing hypothesis testing on a

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> > mean and you have more than 30 or 40 data points, it's OK to assume

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> > your test statistic is normally rather than t-distributed. They've

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> > abandoned that nonsense, but I'm sceptical about these all-dummy

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> > regressions.

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> > Thanks for any help!

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> I think you can find some of the argument in

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> Cohen, J. (1968). Multiple regression as a general data-analytic system. Psychological Bulletin, 70, 426-443.

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> Also Cohen's famous textbook.

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> Lance

I can't find a PDF of the article but here is an account of its content:

http://garfield.library.upenn.edu/classics1982/A1982PB23900001.pdf

Lance