This is a multi-part message in MIME format. --------------872E9E81E16BDD891ECC5EB6 Content-Type: text/plain; charset=us-ascii Content-Transfer-Encoding: 7bit We need good individual-level demographic and election data to learn more about who votes for each major U.S. political party. Barring that, it is still interesting to examine county-level data. The U.S. City and County Databook (fisher.lib.virginia.edu/ccdb) combines U.S. census data with election data. For the 1992 U.S. presidential election I used S-Plus to develop a multiple regression model with percent voting democratic (Clinton) as the dependent variable and the following independent variables: population density (cubic spline in log(density+1)) % population change % age >= 65 years serious crimes per 100,000 % farm population % white % voting turnout median family income and % with bachelors degree of those age>=25 years (restricted tensor spline interaction surface) Nonlinearity was allowed for all variables except crime rate, by using restricted cubic regression splines. The two attached graphs show (1) the relation between the % college degree and % voting democratic, holding all other variables to their median over the 3141 U.S. counties (including income; 0.95 confidence bands are included), and (2) the simultaneous relationship between college, income, and % democratic. The attached graphs are in jpeg format so the resolution isn't great but they should be easy to view. Anyone wanting a copy of the S-Plus transport-format dataset can get it next Tuesday from hesweb1.med.virginia.edu/biostat/s/data/counties.sdd. -- Frank E Harrell Jr Prof. of Biostatistics & Statistics Div. of Biostatistics & Epidem. Dept. of Health Evaluation Sciences U. Virginia School of Medicine http://hesweb1.med.virginia.edu/biostat --------------872E9E81E16BDD891ECC5EB6 Content-Type: image/jpeg; name="college1.jpg" Content-Transfer-Encoding: base64 Content-Disposition: inline; filename="college1.jpg" /9j/4AAQSkZJRgABAQEAAQABAAD//gA6IEltYWdlIGdlbmVyYXRlZCBieSBBbGFkZGluIEdo b3N0c2NyaXB0IChkZXZpY2U9cG5tcmF3KQr/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkS Ew8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/wAALCADZATABAREA /8QAGwABAAIDAQEAAAAAAAAAAAAAAAMGAQQFAgf/xABAEAACAQMDAgQCCAQEBQQDAAABAgMA BBEFEiEGMRMUIkEVUSMyQlZhcZXjRXWBwgdSYpEWM1NjoUNyscGD0fD/2gAIAQEAAD8A+/0p SlKUpSlKUpSlKUpSlKUpSlKUpSlKUpSlK1ry/trDy/mJNnmJlgj9JOXbsOO3apppRDC8rK7B ASQilmP5Ack17pWg2r2yOivHdKXYKpNtJjJDH5fJD+WR8xWk3V2jIG33EiFYmlIaFwQoiEp9 u+xgcf070m6u0SATGS7IEIkZz4T/AGFRm9vlIn+/4V7bqvR073R+vIn/ACm7pOLdvb2kIH/n tzWI+q9GluIoEuiZJXEaDwm5JkeMe3+aNx/T8a823V+iXZhEN2WMxVU+icZLRPKPb/JG5/pj uRUb9a6CljJeNeMIIwWZvBfgC3FyeMf9Jg39cd+KluOrNGtWkWa6KmN5I2+iY4aMoGHb2Mi/ 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