Created
September 17, 2010 11:50
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unm <- read.csv("enrollment.csv") | |
plot(unm$unem,unm$enroll,xlab="Unemployment (%)",ylab="Enrollment") | |
# | |
# We could use our commands, but there are built in tools | |
# | |
res <- lm(enroll~unem,data=unm) | |
names(res) | |
res$coef | |
res$fitted | |
res$resid | |
summary(res) | |
# | |
# We will discuss some of this output more in detail on Monday | |
# | |
plot(unm$unem,unm$enroll,xlab="Unemployment (%)",ylab="Enrollment") | |
abline(res) | |
confint(res) | |
confint(res,level=.90) | |
# | |
# Predicted values based on model | |
# | |
xh <- data.frame(unem=7) | |
predict(res,newdata=xh) | |
xh <- data.frame(unem=c(6,7,8,9,10)) | |
predict(res,newdata=xh) | |
predict(res,newdata=xh,se.fit=T) | |
predict(res,newdata=xh,interval="confidence",level=.95) | |
predict(res,newdata=xh,interval="prediction",level=.95) | |
# | |
# Can we graph this in a useful fashion? | |
# | |
attach(unm) # Break apart the file so we have easy access to data | |
plot(unem,enroll,xlab="Unemployment (%)",ylab="Enrollment",pch=20) | |
abline(res,lwd=2) | |
newData <- data.frame(unem=seq(min(unem),max(unem),by=(max(unem)-min(unem))/45)) | |
conf.lim <- predict(res,newData,interval="confidence") | |
pred.lim <- predict(res,newData,interval="prediction") | |
matlines(newData$unem,conf.lim[,-1],col="red",lty=2) | |
matlines(newData$unem,pred.lim[,-1],col="green",lty=3) | |
legend(8.5,7000,legend=c("Fitted Line","Confidence Bands","Prediction Bands"), | |
lty=c(1,2,3),col=c("black","red","green")) | |
read.csv("stream.csv") |
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