Figura 1.1

Digrama de dispersión para la data Tallas

Tallas.data <- read.table(file = "http://tarwi.lamolina.edu.pe/~clopez/Regresion/Tallas.txt", header = TRUE)
attach(Tallas.data)
plot(MTalla, HTalla, xlab = "X = Talla madre", ylab = "Y = Talla hija", xlim = c(55, 75), ylim = c(55, 75))

Figura 1.2

La data Forbes

Forbes.data <- read.table(file = "http://tarwi.lamolina.edu.pe/~clopez/Regresion/Forbes.txt", header = T)
attach(Forbes.data)
par(mfrow = c(2, 2))
# Gráfico (a)
plot(Presion ~ Temperatura, xlab = "Temperatura (a)", ylab = "Presion")
Forbes.m1 <- lm(Presion ~ Temperatura)
abline(Forbes.m1)
# Gráfico (b)
plot(Temperatura, residuals(Forbes.m1), xlab = "Temperatura (b)", ylab = "Residuales")
abline(h = 0, lty = 2)
# Gráfico (c)
plot(Temperatura, log10(Presion), xlab = "Temperatura (c)", ylab = "log(Presion)")
Forbes.m2 <- lm(log10(Presion) ~ Temperatura)
abline(Forbes.m2)
# Gráfico (d)
plot(Temperatura, residuals(Forbes.m2), xlab = "Temperatura (d)", ylab = "Residuales")
abline(h = 0, lty = 2)

Figura 1.3

La data Tallas

plot(MTalla, HTalla, xlab = "X = Talla madre", ylab = "Y = Talla hija", xlim = c(55, 75), ylim = c(55, 75))
abline(a = 0, b = 1, col = "blue")
Tallas.m1 <- lm(HTalla ~ MTalla)
abline(Tallas.m1)

Figura 1.4

La data Anscombe

Anscombe.data <- read.table(file = "http://tarwi.lamolina.edu.pe/~clopez/Regresion/Anscombe.txt", header = T)
par(mfrow = c(2, 2))
# Gráfico (a)
plot(y1 ~ x1, data = Anscombe.data, xlab = "x1 (a)", ylab = "y1", xlim = c(4, 15), ylim = c(4, 12))
Anscombe.m1 <- lm(y1 ~ x1, data = Anscombe.data)
abline(Anscombe.m1)
# Gráfico (b)
plot(y2 ~ x1, data = Anscombe.data, xlab = "x1 (b)", ylab = "y2", xlim = c(5, 15), ylim = c(4, 10))
Anscombre.m2 <- lm(y2 ~ x1, data = Anscombe.data)
abline(Anscombre.m2)
# Gráfico (c)
plot(y3 ~ x1, data = Anscombe.data, xlab = "x1 (c)", ylab = "y3", xlim = c(4, 14), ylim = c(4, 14))
Anscombe.m3 <- lm(y3 ~ x1, data = Anscombe.data)
abline(Anscombe.m3)
# Gráfico (d)
plot(y4 ~ x2, data = Anscombe.data, xlab = "x2 (d)", ylab = "y4", xlim = c(6, 20), ylim = c(4, 14))
Anscombe.m4 <- lm(y4 ~ x2, data = Anscombe.data)
abline(Anscombe.m4)

Figura 1.5

La data Tallas con el suavizador loess

plot(MTalla, HTalla, xlab = "X = Talla madre", ylab = "Y = Talla hija", xlim = c(55, 75), ylim = c(55, 75))
abline(Tallas.m1)
lines(lowess(MTalla, HTalla, f = 6/10, iter = 1), lty = 2, col = "red")

Figura 1.6

Matrices de dispersión

Gasolina2001.data <- read.table(file = "http://tarwi.lamolina.edu.pe/~clopez/Regresion/Gasolina2001.txt", header = T)
attach(Gasolina2001.data)
TasaLic <- 1000*Licencias/Poblacion
TasaComb <- 1000*Combustible/Poblacion
Ingreso <- Ingreso/1000
logMillas <- log2(Millas)
pairs(TasaComb ~ Impuesto + TasaLic + Ingreso + logMillas, gap = 0.4, cex.labels = 1.2)