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datapp <- read.csv("data_pib.csv",header=T)
datap2 <- datapp[datapp$sex<99,]
datap <- datap2[datap2$skin<99,]
attach(datap)
plot(age,skin)
plot(length,skin)

tumor.table <- table(skin,age)
tumor.table
ages <- as.real(colnames(tumor.table))

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x <- c(1,2,3,4,5,15)
y <- c(3.1,5.9,9.3,11.8,15.1,1)
plot(x,y)
res <- lm(y~x)
summary(res)
abline(res)

res1 <- lm(y~x,subset=c(-6))
summary(res1)
yhat.6.6 <- predict(res1,data.frame(x=15))

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data1 <- read.csv("data_set_1.csv")
library(MASS)
library(car)
data1a <- within(data1,Y2<-exp(Y))
res <- stepAIC(lm(Y~(X1+X2+X3+X4+X5+X6)^2+I(X1^2)+I(X2^2)+I(X3^2)+I(X4^2)+I(X5^2)+I(X6^2),data=data1),k=log(2010))
plot(res,1)



data2 <- read.csv("data_set_2.csv")

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x <- c(1,2,3,4,5,15)
y <- c(3.1,5.9,9.3,11.8,15.1,1)
plot(x,y)
res <- lm(y~x)
summary(res)
abline(res)

res1 <- lm(y~x,subset=c(-6))
summary(res1)
yhat.6.6 <- predict(res1,data.frame(x=15))

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data <- read.csv("manhours.csv",header=T)
library(MASS)
library(car)
library(leaps)
attach(data)
pairs(data)

# The data concern the manpower and workload for US Navy Bachelor Officers' Quarters
# Estimate manpower needs for manning Bachelor Officers Quarters. 
# Site: Site id

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data <- read.csv("salary.csv")
attach(data)
# Analysis
#
# Different intercept, same slope
res1 <- lm(Salary~YSdeg+Sex)
summary(res1)
# Different slope, same intercept
res2 <- lm(Salary~YSdeg+Sex:YSdeg)
summary(res2)

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#
# Modeling Interactions
#
library(faraway)
data(savings)
attach(savings)

summary(lm(sr~pop15+ddpi))
summary(lm(sr~pop15:ddpi))
summary(lm(sr~pop15+ddpi+pop15:ddpi))

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#
# To add better variable names to data read in from a text file
#   The names are assigned in numerical order
#
data <- read.table("CH06PR18.txt")
names(data) <- c("rental.rate","age","exp.tax","vacancy","sq.foot")
#
# To drop the 13 and 73 observation from a regression
#
res <- lm(Y~X1+X2+X3,subset=-c(13,73))

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library(faraway)
data(savings)
attach(savings)
summary(savings)
help(savings)
pairs(savings)
#
# A better 'pairs' plot
#res <- lm(sr~pop15)
summary(res)

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#
# What Polynomial?
#
data <- read.csv("sparrow.csv")
attach(data)
plot(ppha,pairs,xlab="Pedestrians per ha per min",ylab="Breeding pairs per ha")

res3 <- lm(pairs~ppha+I(ppha^2)+I(ppha^3))
summary(res3)
res3b <- lm(pairs~poly(ppha,3))