Probabilidad

Probabilidad: Estudio de azar y la incertidumbre en cualquier situation en la cual varios posibles sucesos pueden ocurrir.

Es un valor entre 0 (imposible) y 1 (seguro).

Ejemplo: La probabilidad de que llueva hoy es de 0.70 (70%).

Experimento: Cualquier accion cuyo resultado esta sujeto a la incertidumbre.

Ejemplo: Lanzar una moneda al aire.

Experimento: lanzar un dado

#install.packages("dice")
library(dice)
## Loading required package: gtools
#install.packages("gtools")
library(gtools)

#install.packages("MASS")
library(MASS)

Probabilidad de obtener un 5

#probabilidad de obtener un 5 
un_seis <- getEventProb (nrolls = 1, ndicePerRoll = 1, nsidesPerDie = 6, eventList = list(6))
un_seis
## [1] 0.1666667
fractions(un_seis)
## [1] 1/6

Probabilidad de sumar un 5 al lanzar 2 dados

un_cinco <- getEventProb(nrolls = 2, ndicePerRoll = 1, nsidesPerDie = 6, eventList = list(5))
un_cinco
## [1] 0.3055556
fractions(un_cinco)
## [1] 11/36

Probabilidad de obtener un 5 en dos lanzamientos consecutivos

dos_cinco <- getEventProb(nrolls = 2, ndicePerRoll = 1, nsidesPerDie = 6, eventList = list(5,5))
dos_cinco
## [1] 0.02777778
fractions(dos_cinco)
## [1] 1/36

¿Que número es mas probable de alcanzar al lanzar dos dados?

sumar_dos <- getEventProb(nrolls = 1, ndicePerRoll = 2, nsidesPerDie = 6, eventList = list(2))
sumar_tres <- getEventProb(nrolls = 1, ndicePerRoll = 2, nsidesPerDie = 6, eventList = list(3))
sumar_cuatro <- getEventProb(nrolls = 1, ndicePerRoll = 2, nsidesPerDie = 6, eventList = list(4))
sumar_cinco <- getEventProb(nrolls = 1, ndicePerRoll = 2, nsidesPerDie = 6, eventList = list(5))
sumar_seis <- getEventProb(nrolls = 1, ndicePerRoll = 2, nsidesPerDie = 6, eventList = list(6))
sumar_siete <- getEventProb(nrolls = 1, ndicePerRoll = 2, nsidesPerDie = 6, eventList = list(7))
sumar_ocho <- getEventProb(nrolls = 1, ndicePerRoll = 2, nsidesPerDie = 6, eventList = list(8))
sumar_nueve <- getEventProb(nrolls = 1, ndicePerRoll = 2, nsidesPerDie = 6, eventList = list(9))
sumar_diez <- getEventProb(nrolls = 1, ndicePerRoll = 2, nsidesPerDie = 6, eventList = list(10))
sumar_once <- getEventProb(nrolls = 1, ndicePerRoll = 2, nsidesPerDie = 6, eventList = list(11))
sumar_doce <- getEventProb(nrolls = 1, ndicePerRoll = 2, nsidesPerDie = 6, eventList = list(12))
suma <- c(2,3,4,5,6,7,8,9,10,11,12)
probabilidad <- c(sumar_dos,sumar_tres, sumar_cuatro, sumar_cinco, sumar_seis, sumar_siete, sumar_ocho, sumar_nueve, sumar_diez, sumar_once, sumar_doce)
tabla <- cbind(suma, probabilidad) 
barplot(probabilidad, names.arg =suma, main = "Probabilidad", xlab = "suma de 2 dados", col = "Tomato" )

#EXPERIMENTO : MANO DE POKER

#install.packages("tidyverse")
library(purrr)

#crear baraja inglesa
numero <- c (2,3,4,5,6,7,8,9,"D", "J", "Q", "K", "A")
numeros <- rep(numero, 4)
palo <- c ("T", "C", "P", "D")
palos <-rep(palo, 13)
baraja <- data.frame(numeros, palos)

#crear el mazo de barajas
mazo <- apply(format(baraja),1, paste, collapse="")
mazo
##    1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
## "2T" "3C" "4P" "5D" "6T" "7C" "8P" "9D" "DT" "JC" "QP" "KD" "AT" "2C" "3P" "4D" 
##   17   18   19   20   21   22   23   24   25   26   27   28   29   30   31   32 
## "5T" "6C" "7P" "8D" "9T" "DC" "JP" "QD" "KT" "AC" "2P" "3D" "4T" "5C" "6P" "7D" 
##   33   34   35   36   37   38   39   40   41   42   43   44   45   46   47   48 
## "8T" "9C" "DP" "JD" "QT" "KC" "AP" "2D" "3T" "4C" "5P" "6D" "7T" "8C" "9P" "DD" 
##   49   50   51   52 
## "JT" "QC" "KP" "AD"
#crear la mano de cartas
mano <- function(n) sample(mazo, n, rep=FALSE)
mi_mano <- mano (5)
mi_mano
##   47   12   52    6   27 
## "9P" "KD" "AD" "7C" "2P"

Conclusiones

Es interesante como podemos jugar con los números y conocer las probabilidades de obtener como pudimos ver en el ejemplo un 5 al lanzar un dado y despúes en la baraja la infinidad de posibilidades de que te salga una cosa u otra entre las posibilidades y caracterisiticas que tiene la baraja inglesa.

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