SCategCont2D-2-16 -...

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Wednesday February 16, 2011 Last time: Mosaic, Association Today: Agreement, Odds Ratios, Continuous 2-Dim Next Time: Lab Announcements: HW 5 due Friday in lab; 2 copies for exam feedback First exam: Feb 25th; Scenarios posted this Friday 2/18 Review Summary Rebecca Nugent, Department of Statistics, Carnegie Mellon University, 2011 1
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Association Plot: graph that indicates deviations from independence in the contigency table library(graphics) assocplot(data.table,main="Hair and Eye Color of Statistics students") Agreement Plot (VCD): for use when comparing two sets of (possibly ranked) decisions; table must be of equal dimensions. library(vcd) test<-matrix(c(1,0,0,0,1,0,0,0,1),ncol=3) agreementplot(test) data.table<-t(MSPatients[,,1]) agreementplot(data.table) Rebecca Nugent, Department of Statistics, Carnegie Mellon University, 2011 2
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Odds Ratios: measures the association in a 2x2 contingency table UCBAdmissions x<-UCBAdmissions[,,6]
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Unformatted text preview: OR&lt;-(x[1,1]*x[2,2])/(x[1,2]*x[2,1]) Four Fold Display: displays the odds ratio/association in 2x2(x k) contingency tables library(graphics) fourfoldplot(UCBAdmissions[,,6],main=&quot;Department F&quot;) fourfoldplot(UCBAdmissions,col=c(2,4)) Rebecca Nugent, Department of Statistics, Carnegie Mellon University, 2011 3 Continuous 2-Dim Structure: Examine variables individually: Examine variables together: What do we want to know? Measuring Linear Association: cor(x1,x2) When is this not useful? Scatterplot: two-dimensional plot of the two variables; can see grouping and type of relationship library(MASS);attach(birthwt) plot(age,bwt,pch=16,xlab=&quot;Years&quot;,ylab=&quot;Grams&quot;,main=&quot;Mothers Age vs. Childs Birthweight&quot;) ##to see different pchs plot(1:16,1:16,pch=1:16,cex=2); plot(1:16,1:16,col=1:16,cex=2,pch=16) Rebecca Nugent, Department of Statistics, Carnegie Mellon University, 2011 4...
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SCategCont2D-2-16 -...

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