Feb 09, 2017
Review of Chapter 3
Played with the advertising data.
d <- read.csv("Advertising.csv")
plot(Sales ~ TV, d)
m <- lm(Sales ~ TV, d)
summary(m)
abline(m)
matlines(predict(m, newdata=data.frame(TV=0:300), interval="p"))
d$x <- log(d$TV)
d$y <- log(d$Sales)
m <- lm(y ~ x, d)
summary(m)
plot(y ~ x, d)
matlines(log(0:300), predict(m, newdata=data.frame(x=log(0:300)), interval="p"))
plot(Sales ~ TV, d)
matlines(0:300, exp(predict(m, newdata=data.frame(x=log(0:300)), interval="p")))Talked about diagnosing model problems.
plot(m)
But also, 4-plots and 6-plots as seen in the NIST handbook.
Next Time
- Thursday, March 9, 2017
Be ready to discuss Chapter 3 of AnIntroductionToStatisticalLearning