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 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 numero 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
##   52   40   30   51   19 
## "AD" "2D" "5C" "KP" "7P"
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