RClase02

datos=c(6,3,7,4,5,3)
A=matrix(data=datos,nrow=3,ncol=2,byrow=FALSE)
print(A*3)

##      [,1] [,2]
## [1,]   18   12
## [2,]    9   15
## [3,]   21    9

matA<-matrix(data=c("a","b","c","d","e","f"),nrow=3,ncol=2,byrow=TRUE)
rownames(matA)<-c("Fila1","Fila2","Fila3")
colnames(matA)<-c("Col1","Col2")
print(matA)

##       Col1 Col2
## Fila1 "a"  "b" 
## Fila2 "c"  "d" 
## Fila3 "e"  "f"

class(matA)

## [1] "matrix" "array"

print(is.matrix(matA))

## [1] TRUE

Fórmulas estadísticas

x<-c(4,2,7,8,4,6,5,8,3)
media<-mean(x)
desvSTD<-sd(x)
varianza<-var(x)
print(media)

## [1] 5.222222

print(desvSTD)

## [1] 2.166667

print(varianza)

## [1] 4.694444

x<-c(0,1,2,3,4,5)
y<-c(2.5,1.4,1.98,3.1,3.8,5.4)
formula1<-formula(y~x)
modelo<-lm(formula1)
summary(modelo)

## 
## Call:
## lm(formula = formula1)
## 
## Residuals:
##      1      2      3      4      5      6 
##  1.100 -0.652 -0.724 -0.256 -0.208  0.740 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   1.4000     0.6072   2.306   0.0824 .
## x             0.6520     0.2006   3.251   0.0313 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.839 on 4 degrees of freedom
## Multiple R-squared:  0.7254, Adjusted R-squared:  0.6568 
## F-statistic: 10.57 on 1 and 4 DF,  p-value: 0.03135

plot(x,y)
abline(0.35238,0.93771,col="blue")