{"id":2155,"date":"2021-03-08T23:39:00","date_gmt":"2021-03-08T23:39:00","guid":{"rendered":"https:\/\/ricovictor.com\/?p=2155"},"modified":"2026-01-29T21:52:52","modified_gmt":"2026-01-29T21:52:52","slug":"distribucion-binomial-en-r","status":"publish","type":"post","link":"https:\/\/ricovictor.com\/index.php\/2021\/03\/08\/distribucion-binomial-en-r\/","title":{"rendered":"Distribuci\u00f3n Binomial en R"},"content":{"rendered":"\t\t
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Muchas veces cuando estamos estudiando estad\u00edstica o probabilidad escuchamos que una determinada variable se distribuye c\u00f3mo un modelo normal o binomial, student<\/strong> etc . \u00c9sto es lo que quiere decir es que ese evento o estudio tiene un comportamiento que se puede asemejar a un determinado modelo te\u00f3rico.<\/div>
\u00a0<\/div><\/div>
Es decir, la distribuci\u00f3n de probabilidad de una variable aleatoria modeliza un experimento aleatorio, describiendo te\u00f3ricamente la forma en que var\u00edan los resultados de dicho experimento<\/strong>. En probabilidad tenemos una gran cantidad de modelos que nos sirven cuando queremos hacer nuestros estudios y aplicaciones pr\u00e1cticas.<\/div>
\u00a0<\/div><\/div>
En este v\u00eddeo vamos a ver una de esas distribuciones, llamada Binomial<\/strong>. \u00c9sta se caracteriza por ser de tipo discreto, y mide el n\u00famero de \u00e9xitos en una secuencia de \u00abn\u00bb ensayos independientes, con una probabilidad fija \u00abp\u00bb de ocurrencia de \u00e9xitos entre los ensayos. Usar\u00e9 las funciones de R studio para hacer los ejemplos.<\/div>
\u00a0<\/div><\/div>

Tambi\u00e9n abajo puedes copiar el c\u00f3digo<\/strong> usado en el v\u00eddeo.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Codigo R<\/h2>


### modelo teorico<\/p>

\u00a0<\/p>

# la distribuci\u00f3n binomial mide el n\u00famero de \u00e9xitos en una
# determinada secuencia de \u00abn\u00bb ensayos indepedientes,
# con una probabilidad fija de ocurrenica (p)<\/p>


# Funci\u00f3n Binomial<\/p>

# X- B(n,p)<\/p>

#p= exito
#1-p= fracaso<\/p>


dbinom(x, size, prob) # funcion masa binomial<\/p>

pbinom(q, size, prob) #funci\u00f3n de distribuci\u00f3n binomial<\/p>

qbinom(p, size, prob) # cuantiles<\/p>

rbinom(n, size, prob) ## n\u00fameros aleatorios<\/p>

###<\/p>


## P(x)= 0<\/p>


## ** De cada 10 veces que Real Madrid juega con el Barca ,
#el Madrid gana 7 veces<\/p>

#n=10
#p=0.7<\/p>

#\u00bfCu\u00e1l es la probabilidad de que gane 1 vez?<\/p>

dbinom(1, 10, 0.7)<\/p>

#\u00bfY de ganen los dos las mismas veces ?<\/p>

dbinom(5, 10, 0.7)<\/p>

#\u00bfY que gane entre 1 y 3 veces incluidos los dos?<\/p>

p_1 <-dbinom(1, 10, 0.7)
p_2<- dbinom(2, 10, 0.7)
p_3 <- dbinom(3, 10, 0.7)<\/p>

p_1 + p_2 + p_3<\/p>

x<=3<\/p>


pbinom(3, 10, 0.7)<\/p>


#Cual es la probabilidad de que barca gane al menos 4 veces?<\/p>

#p(x)< 6<\/p>

pbinom(5, 10, 0.7)<\/p>

\u00a0<\/p>

####<\/p>

# EJEMPLO 2<\/p>


## Se ha comprobado que la probabilidad de
#que se funda la l\u00e1mpara de un televisor en un mes es 0.02.
#Si el televisor tiene 5 a\u00f1os<\/p>

#60mese =5a\u00f1os<\/p>

#\u263b ninguna rotura<\/p>

dbinom(0, 60, 0.02)<\/p>


#exactamente haya 5 roturas<\/p>

# P[X=5]<\/p>

dbinom(5, 60, 0.02)<\/p>


#al menos haya 5 roturas<\/p>

# P[X \u2265 5]<\/p>

# P[X \u2265 5] = 1 \u2013 P[X \u2264 4].<\/p>

1- pbinom(4, 60, 0.02)<\/p>


# haya entre 5 y 25 roturas (ambos inclusive)<\/p>

# P[5 \u2264 X \u2264 25]<\/p>

# P[5 \u2264 X \u2264 25] = P[X \u2264 25] \u2013 P[ X \u2264 5] = F(25) \u2013 F(4)<\/p>


pbinom(c(4, 25), 60, 0.02)<\/p>

1.0000000 – 0.99297547<\/p>


#g) Calcular el valor de la variable tal que deja a su derecha
# el 32% de las observaciones<\/p>

#1-32=68<\/p>

qbinom(0.32, 60, 0.02)<\/p>


#h) Generar una muestra de 30 valores aleatorios de esta distribuci\u00f3n.<\/p>

rbinom(100, 60, 0.02)<\/p>

#################<\/p>

## estuidar retabilidad divisa<\/p>

#mercado sube o baja<\/p>

# 0.2 — sube o baja<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\tAprende Arbitraje Estad\u00edstico<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\tCurso GRATIS Multicointegraci\u00f3n en FOREX<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Muchas veces cuando estamos estudiando estad\u00edstica o probabilidad escuchamos que una determinada variable se distribuye c\u00f3mo un modelo normal o binomial, student etc . \u00c9sto es lo que quiere decir es que ese evento o estudio tiene un comportamiento que se puede asemejar a un determinado modelo te\u00f3rico.\u00a0Es decir, la distribuci\u00f3n de probabilidad de una …<\/p>\n

Distribuci\u00f3n Binomial en R<\/span> Leer m\u00e1s »<\/a><\/p>\n","protected":false},"author":1,"featured_media":2157,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"footnotes":""},"categories":[2],"tags":[],"yoast_head":"\nDistribuci\u00f3n Binomial en R - V\u00edctor A. 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