V<-c(9.25,-6.15,-4.92,11.57,-6.23,2.76,10.15,13.5,21.27,13.79)
N12<-c(5,7,6,5,2,7,4,6,1,4)
 T12<-c(23.8,16.3,27.2,7.1,25.1,27.5,19.4,19.8,32.2,20.7)
 O3<-c(115.4,76.8,113.8,81.6,115.4,125,83.6,75.2,136.8,102.8)
 mean(T12)
 mean(O3)
 sum(T12)
 sum(O3)
 B<-sum(O3)
 A<-sum(T12)
 T12*O3
sum(T12*O3)
sum(T12^2)
T12^2
(sum(T12))^2
 b1<-(sum(T12*O3)-(sum(T12)*sum(O3)/length(T12)))/(sum(T12^2)-(sum(T12)^2)/length(T12))
 b1
 b0<-mean(O3)-(b1*mean(T12))
b0
##################################################### les données
T12<-c(23.8,16.3,27.2,7.1,25.1,27.5,19.4,19.8,32.2,20.7)
O3<-c(115.4,76.8,113.8,81.6,115.4,125,83.6,75.2,136.8,102.8)

################################## Estimation du coefficient de corrélation ro
cor(O3,T12,use="complete.obs")# calcul du coefficient de corrélation r
cor.test(O3,T12,use="complete.obs")# la corrélation est elle significative?
				   # inervalle de confiance autour de ro
				   # r	
################################## effectuer la regression de la variable dépendante (à expliquer) ici O3
				## à la variable indépendante(explicative) ici T12 	
a<-lm(O3~T12)
summary(a)
#####################graphe
plot(O3~T12)
abline(lm(O3~T12),col="blue")
################################## Etude des résidus
summary(a)
###############Prévision
T12val <- 56
 predict(a,data.frame(T12=T12val),interval="prediction")

##################REGRESSION MULTIPLE
#################LES DONNEES
T12<-c(23.8,16.3,27.2,7.1,25.1,27.5,19.4,19.8,32.2,20.7)
V<-c(9.25,-6.15,-4.92,11.57,-6.23,2.76,10.15,13.5,21.27,13.79)
N12<-c(5,7,6,5,2,7,4,6,1,4)
 O3<-c(115.4,76.8,113.8,81.6,115.4,125,83.6,75.2,136.8,102.8)
################# modelisation 
a<-lm(O3~T12+V+N12)
summary(a)
################# A PARTIR D'UN JEU DE DONNEES
donnees<-read.csv2("ozone.csv")
a<-lm(maxO3~T12+Vx12+Ne12,data=donnees)
summary(a)