ozone <- read.table("../donnees/ozone.txt", header = T, sep = ";")
library("scatterplot3d")
scatterplot3d(ozone[,"T12"],ozone[,"Vx"],ozone[,"O3"],
type="h",pch=16, box=FALSE, xlab="T12", ylab="Vx", zlab="O3")
2 La régression linéaire multiple
La concentration en ozone
regmulti <- lm(O3~T12+Vx, data = ozone)
summary(regmulti)
Call:
lm(formula = O3 ~ T12 + Vx, data = ozone)
Residuals:
Min 1Q Median 3Q Max
-42.984 -10.152 -2.407 11.710 34.494
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 35.4530 10.7446 3.300 0.00185 **
T12 2.5380 0.5151 4.927 1.08e-05 ***
Vx 0.8736 0.1772 4.931 1.06e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 16.82 on 47 degrees of freedom
Multiple R-squared: 0.5249, Adjusted R-squared: 0.5047
F-statistic: 25.96 on 2 and 47 DF, p-value: 2.541e-08La hauteur des eucalyptus
eucalypt <- read.table("../donnees/eucalyptus.txt", header = T, sep = ";")
plot(ht~circ, data = eucalypt, xlab = "circ", ylab = "ht")
regmult <- lm(ht ~ circ + I(sqrt(circ)), data = eucalypt)
resume.mult <- summary(regmult)
resume.mult
Call:
lm(formula = ht ~ circ + I(sqrt(circ)), data = eucalypt)
Residuals:
Min 1Q Median 3Q Max
-4.1881 -0.6881 0.0427 0.7927 3.7481
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -24.35200 2.61444 -9.314 <2e-16 ***
circ -0.48295 0.05793 -8.336 <2e-16 ***
I(sqrt(circ)) 9.98689 0.78033 12.798 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.136 on 1426 degrees of freedom
Multiple R-squared: 0.7922, Adjusted R-squared: 0.7919
F-statistic: 2718 on 2 and 1426 DF, p-value: < 2.2e-16plot(ht ~ circ, data = eucalypt, pch = "+", col = "grey60")
grille <- data.frame(circ = seq(min(eucalypt[,"circ"]),max(eucalypt[,"circ"]), length = 100))
lines(grille[,"circ"], predict(regmult, grille))