gov1000-list November 2004

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23 participants
83 discussions

by Kevin Quinn

Hi Everyone, The Ornstein data problem on problem set 8 requires you to deal with rhs variables that are categorical-- what are called "factors" in R. The way to deal with such variables is to create a series of dummy variables that allow the expected value of y to shift across the different categories of the categorical variable. Suppose you have a categorical variable X that has 3 categories (A, B, and C). Assuming there is an intercept in the model, the idea is to use 2 dummy variables-- one of which is equal to 1 whenever X = B (0 otherwise) and another dummy variable that is equal to 1 whenever X = C (0 otherwise). The coefficient on the dummy for B tells you how much higher the mean of y is in category B than the baseline category (here X = A) holding the other independent variables in the model constant. The coefficient on the C dummy can be interpreted similarly. In general if your X has k categories you will use k-1 dummy variables. One of the *really* nice things about R is that if a categorical variable is coded as a factor R will automatically create the appropriate dummy variables for you. Take a look at the following highly contrived example: > x <- factor(c("Pat", "Pat", "Pat", "Pat", "Sam", "Sam", "Sam", "Sam", "Chris", "Chris", "Chris", "Chris")) > y <- c(5, 5, 5, 5, 12, 12, 12, 12, 30, 30, 30, 30) > data.frame(y,x) y x 1 5 Pat 2 5 Pat 3 5 Pat 4 5 Pat 5 12 Sam 6 12 Sam 7 12 Sam 8 12 Sam 9 30 Chris 10 30 Chris 11 30 Chris 12 30 Chris > lm.out <- lm(y~x) > summary(lm.out) Call: lm(formula = y ~ x) Residuals: Min 1Q Median 3Q Max -1.634e-14 7.456e-31 1.140e-30 1.534e-30 1.717e-14 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.000e+01 4.163e-15 7.206e+15 <2e-16 *** xPat -2.500e+01 5.888e-15 -4.246e+15 <2e-16 *** xSam -1.800e+01 5.888e-15 -3.057e+15 <2e-16 *** --- Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 Residual standard error: 8.327e-15 on 9 degrees of freedom Multiple R-Squared: 1, Adjusted R-squared: 1 F-statistic: 9.596e+30 on 2 and 9 DF, p-value: < 2.2e-16 Our results tell us that the average level of y is 25 units lower for Pat than for Chris, and 18 units lower for Sam than for Chris. We can verify this by looking at the original data. Alison will also talk about dummy variables a bit today in section. Hope this helps. Best, Kevin ------------------------------------------------------ Kevin Quinn Assistant Professor Department of Government and Center for Basic Research in the Social Sciences 34 Kirkland Street Harvard University Cambridge, MA 02138

19 years, 4 months

PS 8

by Kevin Quinn

Hi Everyone, Problem set 8 is now available on the course website. Best, Kevin ------------------------------------------------------ Kevin Quinn Assistant Professor Department of Government and Center for Basic Research in the Social Sciences 34 Kirkland Street Harvard University Cambridge, MA 02138

19 years, 5 months

revised slides

by Kevin Quinn

Hi Everyone, I have substantially revised the slides for today's lecture. They are now available on the course website. Best, Kevin ------------------------------------------------------ Kevin Quinn Assistant Professor Department of Government and Center for Basic Research in the Social Sciences 34 Kirkland Street Harvard University Cambridge, MA 02138

19 years, 5 months

Font size in Latex

by Rebecca Marie Nelson

How can I change it? What are my options? I have a table that does not fit, so I wanted to make the font smaller.... I consulted Not-so-short and tried the: \small etc commands and they didn't work. Any ideas? Thanks! Becky :)

19 years, 5 months

problem 3?

by David Ni

Im not sure if Im the only one, but Im not quite sure how to do problem 3. any tips? Thanks! Cheers, Dave

19 years, 5 months

Mail Delivery (failure gov1000-list@fas.harvard.edu)

by help＠winzip.com

19 years, 5 months

Mail Delivery (failure gov1000-list@fas.harvard.edu)

by gov1000-list＠fas.harvard.edu

19 years, 5 months

What's so special about linear.hypothesis?

by Lucy Clare Barnes

>From the homework so far, it seems that we haven't HAD to do anything in linear.hypothesis that we couldn't have done in anova. is this right? if so, what is it that linear.hypothesis CAN do that anova can't, and if not, where have I gone drastically wrong on the problem set? thanks Lucy

19 years, 5 months

7d please

by Matthew Walter Landauer

a question - are we testing whether Black and Hisp are simultaneously equal to zero, or whether each one might be equal to zero? does the second half of 7d refer to the two tests made in question 7 all together (7b/c and one from 7d) or the two tests from 7d (assuming the second interpretation of my first question?). Clearly the answer to my second question relies on the answer to my first. Confusingly, and syntactically quite shockingly poor as to the way this was written, matt

19 years, 5 months

Random Latex question....

by Andy Harris

I'm trying to format a document where I'd like the page number to be in the upper right hand corner, prefaced by my name. Does anyone know an easy way to do this? andy

19 years, 5 months

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gov1000-list November 2004