# *******************************************************************
# 1. Definição da trilha de dados
# *******************************************************************
setwd("C:\\dados\\")


# *******************************************************************
# 2. Leitura dos dados
# *******************************************************************
X<-read.table("c:\\dados\\plano1med.dat",header=F)
# Listar dados
X


# *******************************************************************
# 3. Estatísticas descritivas
# *******************************************************************
summary(X)


# *******************************************************************
# 4. Estatísticas gerais: média, desvio-padrão, variância, n e erro padrão da média
# *******************************************************************
variavel<-(X[,3])
mean(variavel)
sd(variavel)
var(variavel)
length(variavel)
sd(variavel)/sqrt(length(variavel))


# *******************************************************************
# 5. correlação entre duas variáveis
# *******************************************************************
variavelx<-(X[,3])
variavely<-(X[,4])
plot(variavelx,variavely,xlab="X",ylab="Y")
cor.test(variavelx,variavely,method="pearson",alternative="two.sided")
cor.test(variavelx,variavely,method="spearman",alternative="two.sided")


# *******************************************************************
# 6. correlação entre todos os pares de variáveis
# *******************************************************************
fim<-ncol(X)-1
for(i in 1:fim)
{
variavelx<-(X[,i])
inicio<-(i+1)
for(j in inicio:ncol(X))
{
variavely<-(X[,j])
print(c(i,j))
valor<-cor.test(variavelx,variavely,method="pearson",alternative="two.sided")
print(valor)
}
}


# *******************************************************************
# 7. saída da matriz de correlação
# *******************************************************************
correl<-matrix(rep(NA),ncol(X),ncol(X))
for(i in 1:ncol(X))
{
variavelx<-(X[,i])
for(j in 1:ncol(X))
{
variavely<-(X[,j])
valor<-cor.test(variavelx,variavely,method="pearson",alternative="two.sided")
correl[i,j]<-(valor$estimate)
}
}
print(correl)