datos=c(5,6,7,8,9,10)
matrix(data=datos,nrow=3,ncol=2)
##      [,1] [,2]
## [1,]    5    8
## [2,]    6    9
## [3,]    7   10
datos=c(5,6,7,8,9,10)
A=matrix(data=datos,nrow=3,ncol=2, byrow=TRUE)
print(A*3)
##      [,1] [,2]
## [1,]   15   18
## [2,]   21   24
## [3,]   27   30
MatA<-matrix(data = c("a", "b", "c", "d", "e", "f"), nrow = 3, ncol = 2)
rownames(MatA)<-c("Fila1","Fila2","Fila3")
colnames(MatA)<-c("Col1","Col2")
print(MatA)
##       Col1 Col2
## Fila1 "a"  "d" 
## Fila2 "b"  "e" 
## Fila3 "c"  "f"
 ##      [,1] [,2]
 ## [1,] "a"  "d"
 ## [2,] "b"  "e"
 ## [3,] "c"  "f"
class(MatA)
## [1] "matrix" "array"
print(is.matrix(MatA))
## [1] TRUE
mat1 <- matrix(data = 1:6, nrow = 2, ncol = 3, byrow = TRUE)
 rownames(mat1)
## NULL
 ## NULL
 colnames(mat1)
## NULL
 ## NULL
rownames(mat1) <- c("Row 1", "Row 2")
 colnames(mat1) <- c("Col 1", "Col 2", "Col 3")
 mat1
##       Col 1 Col 2 Col 3
## Row 1     1     2     3
## Row 2     4     5     6
 ##       Col 1 Col 2 Col 3
 ## Row 1     1     2     3
 ## Row 2     4     5     6

##Fórmulas Estadísticas

x<-c(5,6,7,3,4,2,6,9)
media<-mean(x)
desvSTD<-sd(x)
varianza<-var(x)
print(media)
## [1] 5.25
print(desvSTD)
## [1] 2.251983
print(varianza)
## [1] 5.071429
x<-c(0,1,2,3,4,5)
y<-c(2.5,1.4,1.98,3.1,3.8,5.4)
formualal<-formula(y~x)
modelo<-lm(formualal)
summary(modelo)
## 
## Call:
## lm(formula = formualal)
## 
## 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( 1.4000 ,0.6520, col="pink")