Determinar y simular muestreos
Con un conjunto de datos utilizar mecanismos de programación para determinar muestreos mediante técnicas de aleatorio simple, aleatorio sistemático, aleatorio estratificado y por conglomerados.
El propósito de la estadística inferencial consiste en determinar y conocer el comportamiento sobre una población a partir de una muestra.
Una muestra es una porción, una proporción o parte de la población de interés. En muchos casos, el muestreo resulta más accesible y sencillo que el estudio de toda la población. (Lind et al., 2015).
Por otra parte la importancia del muestreo como lo menciona (Anderson et al., 2008) es cuestión de minimizar costo de trabajo, recopilar información de una muestra es sustancialmente menor, que hacerlo de una población completa; especialmente cuando se deben realizar entrevistas personales para recopilar la información.
Finamente, los métodos de muestreo aleatorio y sin sesgos son muy importantes para realizar inferencias estadísticas válidas (Lind et al., 2015).
Una muestra aleatoria simple de tamaño n de una población finita de tamaño N es una muestra seleccionada de manera que cada posible muestra de tamaño n tenga la misma probabilidad de ser seleccionada (Anderson et al., 2008).
De un cojunto de N elementos de una población, un muestreo aleatorio simple sería una especíe de rifa o tómbola para elegir de de entre los N total de población una cantidad de n número de la muestra.
Se selecciona un punto aleatorio de inicio y posteriormente se elige cada k-ésimo miembro de la población (Lind et al., 2015).
Suele emplearse como alternativa al muestreo aleatorio simple, en especial cuando las poblaciones son grandes se lleva mucho tiempo tomar una muestra aleatoria simple en la que primero hay que hallar un número aleatorio y después contar o buscar en el marco el elemento correspondiente (Anderson et al., 2008).
El primer elemento se elige aleatoriamente, lo que permite suponer que una muestra sistemática tiene las propiedades de una muestra aleatoria simple. Esta suposición suele ser correcta cuando el marco es un ordenamiento aleatorio de los elementos de la población (Anderson et al., 2008)
Muestreo aleatorio estratificado Cuando una población se divide en grupos a partir de ciertas características, el muestreo aleatorio estratificado garantiza que cada grupo o estrato se encuentre representado en la muestra (Lind et al., 2015).
(Anderson et al., 2008) describe el muestreo aleatorio estratificado en donde los elementos de la población primero se dividen en grupos, a los que se les llama estratos, de manera que cada elemento pertenezca a uno y sólo un estrato. La base para la formación de los estratos, que puede ser departamento, edad, tipo de industria, enre otros, está a discreción de la persona que diseña la muestra.
Por otra parte, para asegurar que la muestra sea una representación imparcial de las N observaciones, se debe determinar la frecuencia relativa y a partir de ahí generar las cantidad de muestra de cada estrato. (Lind et al., 2015).
Muestreo por conglomerados La población se divide en conglomerados a partir de los límites naturales geográficos u otra clase. A continuación, estos se seleccionan al azar y se toma una muestra de forma aleatoria con elementos de cada grupo (Lind et al., 2015).
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(mosaic)## Warning: package 'mosaic' was built under R version 4.0.3## Registered S3 method overwritten by 'mosaic':
## method from
## fortify.SpatialPolygonsDataFrame ggplot2##
## The 'mosaic' package masks several functions from core packages in order to add
## additional features. The original behavior of these functions should not be affected by this.##
## Attaching package: 'mosaic'## The following object is masked from 'package:Matrix':
##
## mean## The following object is masked from 'package:ggplot2':
##
## stat## The following objects are masked from 'package:dplyr':
##
## count, do, tally## The following objects are masked from 'package:stats':
##
## binom.test, cor, cor.test, cov, fivenum, IQR, median, prop.test,
## quantile, sd, t.test, var## The following objects are masked from 'package:base':
##
## max, mean, min, prod, range, sample, sumlibrary(readr)## Warning: package 'readr' was built under R version 4.0.3library(ggplot2) # Para gráficos
library(knitr) # Para formateo de datos
library(fdth) # Para tablas de frecuencias## Warning: package 'fdth' was built under R version 4.0.3##
## Attaching package: 'fdth'## The following objects are masked from 'package:mosaic':
##
## sd, var## The following objects are masked from 'package:stats':
##
## sd, varSe carga un conjunto de 100 nombres de personas con sus atributo de género y la actividad deportiva o cultura que practican, Cargando un datos llamando a una función que construye los datos. El argumento encoding significa que acepte acentos en los datos.
source("https://raw.githubusercontent.com/rpizarrog/probabilidad-y-estad-stica/master/construir%20datos%20y%20funciones%20caso%209.r", encoding = "UTF-8")
kable(head(personas, 10), caption = "Los primeros diez registros de nombres en el conjunto dedatos")| nombres | generos | ajedrez | beisbol | tiro.arco | pesas | futbol | softbol | atletismo | folklorico | tahitiano | teatro | rondalla | pantomima |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| JUAN | M | NO | NO | NO | SI | NO | SI | NO | NO | NO | NO | NO | SI |
| JOSÉ LUIS | M | NO | NO | NO | NO | NO | NO | NO | SI | NO | NO | NO | NO |
| JOSÉ | M | NO | SI | NO | SI | NO | NO | NO | NO | NO | NO | SI | SI |
| MARÍA GUADALUPE | F | NO | SI | NO | NO | NO | NO | NO | NO | NO | NO | SI | SI |
| FRANCISCO | M | NO | NO | NO | NO | NO | NO | SI | NO | NO | NO | NO | NO |
| GUADALUPE | F | NO | NO | NO | NO | NO | NO | NO | NO | NO | NO | NO | NO |
| MARÍA | F | NO | SI | NO | NO | SI | NO | NO | NO | NO | NO | NO | NO |
| JUANA | F | NO | NO | NO | NO | SI | NO | NO | SI | NO | NO | NO | NO |
| ANTONIO | M | NO | NO | NO | NO | NO | NO | NO | NO | NO | NO | NO | NO |
| JESÚS | M | NO | NO | SI | NO | NO | SI | NO | NO | SI | NO | NO | NO |
kable(tail(personas, 10), caption = "Las útimos diez registros de nombres en el conjunto de datos")| nombres | generos | ajedrez | beisbol | tiro.arco | pesas | futbol | softbol | atletismo | folklorico | tahitiano | teatro | rondalla | pantomima | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 91 | ANDREA | F | NO | NO | NO | NO | NO | NO | NO | NO | NO | NO | NO | SI |
| 92 | ISABEL | F | NO | NO | NO | NO | NO | NO | NO | NO | NO | NO | NO | NO |
| 93 | MARÍA TERESA | F | NO | SI | NO | NO | SI | NO | NO | SI | NO | NO | NO | NO |
| 94 | IRMA | F | SI | SI | NO | NO | NO | NO | NO | NO | NO | NO | NO | NO |
| 95 | CARMEN | F | NO | NO | NO | NO | NO | NO | NO | NO | NO | NO | NO | NO |
| 96 | LUCÍA | F | NO | SI | NO | SI | NO | NO | NO | SI | NO | NO | SI | SI |
| 97 | ADRIANA | F | NO | NO | NO | NO | NO | NO | SI | NO | NO | NO | NO | NO |
| 98 | AGUSTÍN | M | NO | SI | NO | NO | NO | NO | NO | NO | SI | NO | NO | NO |
| 99 | MARÍA DE LA LUZ | F | NO | NO | NO | NO | NO | NO | SI | NO | NO | NO | NO | NO |
| 100 | GUSTAVO | M | NO | NO | NO | NO | NO | NO | NO | SI | NO | NO | NO | NO |
Se cargan os datos de alumnos inscritos en una Institución de educación superior en el semetre septiembre 2020 a enero 2021, con los atributos siguientes: No de control (modificado y no real), Número Conesucutivo de alumno Semestre que cursa Créditos aprobados Carga académica que cursa Promedio aritmético Carrera
alumnos <- alumnos <- read_csv("https://raw.githubusercontent.com/rpizarrog/probabilidad-y-estad-stica/master/datos/promedios%20alumnos/datos%20alumnos%20promedios%20SEP%202020.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## `No. Control` = col_double(),
## Alumno = col_double(),
## Semestre = col_double(),
## `Cr. Apr.` = col_double(),
## Carga = col_double(),
## Promedio = col_double(),
## Carrera = col_character()
## )kable(head(alumnos, 10), caption = "Los primeros diez registros de alumnos")| No. Control | Alumno | Semestre | Cr. Apr. | Carga | Promedio | Carrera |
|---|---|---|---|---|---|---|
| 20190001 | 1 | 11 | 198 | 19 | 80.21 | SISTEMAS |
| 20190002 | 2 | 11 | 235 | 10 | 84.33 | SISTEMAS |
| 20190003 | 3 | 9 | 235 | 10 | 95.25 | SISTEMAS |
| 20190004 | 4 | 9 | 226 | 19 | 95.00 | SISTEMAS |
| 20190005 | 5 | 10 | 231 | 14 | 82.32 | SISTEMAS |
| 20190006 | 6 | 9 | 212 | 23 | 95.02 | SISTEMAS |
| 20190007 | 7 | 12 | 221 | 10 | 79.06 | SISTEMAS |
| 20190008 | 8 | 9 | 226 | 9 | 92.47 | SISTEMAS |
| 20190009 | 9 | 9 | 231 | 4 | 91.08 | SISTEMAS |
| 20190010 | 10 | 11 | 222 | 13 | 80.42 | SISTEMAS |
kable(tail(alumnos, 10), caption = "Las útimos diez registros de alumnos")| No. Control | Alumno | Semestre | Cr. Apr. | Carga | Promedio | Carrera |
|---|---|---|---|---|---|---|
| 20195920 | 5920 | 7 | 169 | 23 | 89.14 | ADMINISTRACION |
| 20195921 | 5921 | 5 | 109 | 26 | 87.83 | ADMINISTRACION |
| 20195922 | 5922 | 3 | 55 | 29 | 92.83 | ADMINISTRACION |
| 20195923 | 5923 | 2 | 23 | 23 | 88.60 | ADMINISTRACION |
| 20195924 | 5924 | 2 | 27 | 28 | 92.83 | ADMINISTRACION |
| 20195925 | 5925 | 7 | 94 | 13 | 80.95 | ADMINISTRACION |
| 20195926 | 5926 | 5 | 103 | 32 | 92.68 | ADMINISTRACION |
| 20195927 | 5927 | 4 | 79 | 34 | 86.18 | ADMINISTRACION |
| 20195928 | 5928 | 5 | 108 | 32 | 90.48 | ADMINISTRACION |
| 20195929 | 5929 | 7 | 169 | 32 | 92.33 | ADMINISTRACION |
Hay que encuestar a diez personas de 100 para hacerles alguna entrevist, ¿a quienes? Con el conjunto de datos seleccionar 10 personas aleatoriamente con al funcón sample()
N <- nrow(personas)
n <- 10
muestra <- sample(personas$nombres, n)
kable(muestra, caption = "La muestra de personas")| x |
|---|
| FRANCISCA |
| GUSTAVO |
| JORGE |
| PATRICIA |
| PEDRO |
| LUCÍA |
| ELIZABETH |
| JESÚS |
| ALEJANDRO |
| RAFAEL |
N <- nrow(alumnos)
n <- 100
muestra <- sample(N, n) # Genera los números
kable(alumnos[muestra, ], caption = "La muestra de alumnos")| No. Control | Alumno | Semestre | Cr. Apr. | Carga | Promedio | Carrera |
|---|---|---|---|---|---|---|
| 20194973 | 4973 | 6 | 133 | 33 | 85.54 | GESTION EMPRESARIAL |
| 20195866 | 5866 | 1 | NA | 27 | 0.00 | ADMINISTRACION |
| 20191513 | 1513 | 6 | 67 | 34 | 78.60 | BIOQUIMICA |
| 20194076 | 4076 | 7 | 144 | 32 | 88.52 | MECATRONICA |
| 20192521 | 2521 | 9 | 222 | 23 | 86.40 | ELECTRONICA |
| 20195624 | 5624 | 3 | 55 | 29 | 96.67 | ADMINISTRACION |
| 20194985 | 4985 | 4 | 55 | 29 | 80.42 | GESTION EMPRESARIAL |
| 20195075 | 5075 | 5 | 116 | 32 | 87.71 | GESTION EMPRESARIAL |
| 20195041 | 5041 | 7 | 140 | 35 | 82.27 | GESTION EMPRESARIAL |
| 20190395 | 395 | 1 | NA | 27 | 0.00 | SISTEMAS |
| 20193795 | 3795 | 4 | 66 | 29 | 86.47 | MECATRONICA |
| 20195683 | 5683 | 1 | NA | 27 | 0.00 | ADMINISTRACION |
| 20191577 | 1577 | 9 | 165 | 16 | 78.86 | CIVIL |
| 20192306 | 2306 | 5 | 89 | 27 | 86.33 | ELECTRICA |
| 20193510 | 3510 | 3 | 41 | 24 | 76.80 | MECANICA |
| 20191415 | 1415 | 6 | 123 | 29 | 82.48 | BIOQUIMICA |
| 20190830 | 830 | 5 | 97 | 26 | 93.50 | ARQUITECTURA |
| 20190200 | 200 | 7 | 107 | 17 | 79.26 | SISTEMAS |
| 20195484 | 5484 | 11 | 257 | 5 | 87.44 | ADMINISTRACION |
| 20190025 | 25 | 11 | 230 | 15 | 84.02 | SISTEMAS |
| 20192596 | 2596 | 3 | 52 | 25 | 92.67 | ELECTRONICA |
| 20193863 | 3863 | 1 | NA | 25 | 0.00 | MECATRONICA |
| 20190074 | 74 | 10 | 230 | 15 | 83.94 | SISTEMAS |
| 20191933 | 1933 | 1 | NA | 27 | 0.00 | CIVIL |
| 20191691 | 1691 | 4 | 75 | 32 | 84.19 | CIVIL |
| 20192587 | 2587 | 5 | 90 | 20 | 83.50 | ELECTRONICA |
| 20190886 | 886 | 1 | NA | 26 | 0.00 | ARQUITECTURA |
| 20194827 | 4827 | 7 | 150 | 25 | 88.75 | GESTION EMPRESARIAL |
| 20194756 | 4756 | 9 | 230 | 15 | 91.77 | GESTION EMPRESARIAL |
| 20190663 | 663 | 7 | 151 | 23 | 85.22 | ARQUITECTURA |
| 20192503 | 2503 | 10 | 202 | 23 | 81.25 | ELECTRONICA |
| 20194892 | 4892 | 1 | NA | 27 | 0.00 | GESTION EMPRESARIAL |
| 20194549 | 4549 | 6 | 133 | 23 | 83.25 | QUIMICA |
| 20190308 | 308 | 4 | 83 | 29 | 91.00 | SISTEMAS |
| 20192139 | 2139 | 6 | 143 | 30 | 84.77 | CIVIL |
| 20191319 | 1319 | 7 | 124 | 34 | 83.15 | BIOQUIMICA |
| 20195755 | 5755 | 4 | 84 | 29 | 87.44 | ADMINISTRACION |
| 20195925 | 5925 | 7 | 94 | 13 | 80.95 | ADMINISTRACION |
| 20193632 | 3632 | 1 | NA | 26 | 0.00 | MECANICA |
| 20193546 | 3546 | 3 | 48 | 22 | 78.64 | MECANICA |
| 20191619 | 1619 | 9 | 225 | 10 | 84.85 | CIVIL |
| 20191632 | 1632 | 9 | 159 | 15 | 80.15 | CIVIL |
| 20194890 | 4890 | 7 | 170 | 35 | 87.44 | GESTION EMPRESARIAL |
| 20192090 | 2090 | 4 | 78 | 33 | 83.59 | CIVIL |
| 20191764 | 1764 | 1 | NA | 27 | 0.00 | CIVIL |
| 20190612 | 612 | 1 | NA | 26 | 0.00 | ARQUITECTURA |
| 20191097 | 1097 | 7 | 139 | 24 | 84.62 | ARQUITECTURA |
| 20190796 | 796 | 7 | 116 | 34 | 81.12 | ARQUITECTURA |
| 20190240 | 240 | 2 | 27 | 28 | 92.33 | SISTEMAS |
| 20191202 | 1202 | 1 | NA | 23 | 0.00 | BIOQUIMICA |
| 20194673 | 4673 | 12 | 219 | 16 | 89.93 | GESTION EMPRESARIAL |
| 20195370 | 5370 | 5 | 41 | 4 | 81.44 | INFORMATICA |
| 20191901 | 1901 | 5 | 117 | 31 | 87.08 | CIVIL |
| 20193696 | 3696 | 11 | 231 | 4 | 83.33 | MECATRONICA |
| 20193370 | 3370 | 11 | 225 | 10 | 81.86 | MECANICA |
| 20191197 | 1197 | 3 | 57 | 27 | 82.54 | BIOQUIMICA |
| 20193032 | 3032 | 3 | 55 | 29 | 89.00 | INDUSTRIAL |
| 20194867 | 4867 | 1 | NA | 27 | 0.00 | GESTION EMPRESARIAL |
| 20191967 | 1967 | 1 | NA | 27 | 0.00 | CIVIL |
| 20193638 | 3638 | 7 | 170 | 27 | 86.59 | MECANICA |
| 20190934 | 934 | 7 | 170 | 28 | 88.58 | ARQUITECTURA |
| 20194100 | 4100 | 9 | 225 | 5 | 87.96 | QUIMICA |
| 20195193 | 5193 | 6 | 138 | 33 | 86.21 | GESTION EMPRESARIAL |
| 20195450 | 5450 | 10 | 262 | 10 | 88.60 | ADMINISTRACION |
| 20191067 | 1067 | 1 | NA | 26 | 0.00 | ARQUITECTURA |
| 20193404 | 3404 | 10 | 172 | 18 | 81.13 | MECANICA |
| 20194217 | 4217 | 12 | 225 | 10 | 78.46 | QUIMICA |
| 20191449 | 1449 | 1 | NA | 23 | 0.00 | BIOQUIMICA |
| 20192720 | 2720 | 9 | 202 | 24 | 82.28 | INDUSTRIAL |
| 20195151 | 5151 | 1 | NA | 27 | 0.00 | GESTION EMPRESARIAL |
| 20193110 | 3110 | 1 | NA | 27 | 0.00 | INDUSTRIAL |
| 20191051 | 1051 | 6 | 127 | 24 | 88.19 | ARQUITECTURA |
| 20194783 | 4783 | 1 | NA | 27 | 0.00 | GESTION EMPRESARIAL |
| 20195643 | 5643 | 2 | 27 | 28 | 92.67 | ADMINISTRACION |
| 20194482 | 4482 | 2 | 25 | 30 | 82.00 | QUIMICA |
| 20194046 | 4046 | 1 | NA | 25 | 0.00 | MECATRONICA |
| 20192183 | 2183 | 2 | 27 | 30 | 83.50 | CIVIL |
| 20190659 | 659 | 1 | NA | 26 | 0.00 | ARQUITECTURA |
| 20195318 | 5318 | 1 | NA | 26 | 0.00 | TIC |
| 20192101 | 2101 | 2 | 23 | 25 | 80.80 | CIVIL |
| 20191758 | 1758 | 4 | 80 | 34 | 85.94 | CIVIL |
| 20192297 | 2297 | 5 | 94 | 33 | 84.77 | ELECTRICA |
| 20193793 | 3793 | 7 | 128 | 31 | 84.46 | MECATRONICA |
| 20190822 | 822 | 3 | 48 | 32 | 90.45 | ARQUITECTURA |
| 20190352 | 352 | 8 | 176 | 32 | 80.47 | SISTEMAS |
| 20193467 | 3467 | 3 | 42 | 32 | 82.30 | MECANICA |
| 20190443 | 443 | 7 | 160 | 34 | 90.34 | SISTEMAS |
| 20190241 | 241 | 5 | 112 | 25 | 91.63 | SISTEMAS |
| 20194569 | 4569 | 3 | 51 | 30 | 88.64 | QUIMICA |
| 20193456 | 3456 | 6 | 89 | 32 | 78.30 | MECANICA |
| 20195534 | 5534 | 8 | 177 | 34 | 86.89 | ADMINISTRACION |
| 20193666 | 3666 | 12 | 190 | 5 | 78.35 | MECATRONICA |
| 20192155 | 2155 | 2 | 22 | 26 | 93.40 | CIVIL |
| 20193527 | 3527 | 1 | NA | 26 | 0.00 | MECANICA |
| 20191607 | 1607 | 10 | 231 | 4 | 83.15 | CIVIL |
| 20194038 | 4038 | 5 | 105 | 24 | 88.57 | MECATRONICA |
| 20190090 | 90 | 4 | 49 | 32 | 82.64 | SISTEMAS |
| 20195706 | 5706 | 4 | 84 | 30 | 86.94 | ADMINISTRACION |
| 20190058 | 58 | 9 | 200 | 25 | 83.66 | SISTEMAS |
| 20190724 | 724 | 4 | 70 | 28 | 87.56 | ARQUITECTURA |
Con el conjunto de datos personas, iniciar en un valor aletorio e identificar los siguientes de 10 en 10 hasta tener diez personas.
N <- nrow(personas)
n = 10
saltos <- round(N / n, 0)
inicio <- round(sample(N, 1) / n, 0)
#inicio
cuales <- seq(from = inicio, to =N, by= saltos)
kable(personas[cuales, ], caption = "La muestra sistematizada de personas")| nombres | generos | ajedrez | beisbol | tiro.arco | pesas | futbol | softbol | atletismo | folklorico | tahitiano | teatro | rondalla | pantomima | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | JESÚS | M | NO | NO | SI | NO | NO | SI | NO | NO | SI | NO | NO | NO |
| 20 | DANIEL | M | NO | NO | NO | NO | NO | NO | SI | NO | NO | NO | NO | NO |
| 30 | DAVID | M | NO | NO | NO | NO | NO | NO | NO | NO | NO | NO | NO | NO |
| 40 | MARÍA ELENA | M | NO | NO | NO | NO | NO | NO | SI | SI | NO | NO | NO | NO |
| 50 | ALBERTO | M | NO | NO | NO | NO | NO | NO | SI | NO | NO | NO | NO | NO |
| 60 | ROSA MARÍA | F | NO | NO | NO | NO | NO | SI | NO | SI | NO | NO | NO | NO |
| 70 | GABRIEL | M | SI | NO | SI | NO | NO | SI | NO | NO | NO | NO | NO | NO |
| 80 | MARÍA LUISA | F | SI | NO | NO | NO | NO | NO | NO | NO | NO | NO | NO | NO |
| 90 | ARACELI | M | NO | NO | NO | NO | NO | NO | NO | NO | NO | NO | SI | NO |
| 100 | GUSTAVO | M | NO | NO | NO | NO | NO | NO | NO | SI | NO | NO | NO | NO |
N <- nrow(alumnos)
n = 100
saltos <- round(N / n, 0)
inicio <- round(sample(N, 1) / n, 0)
cuales <- seq(from = inicio, to =N, by= saltos)
kable(alumnos[cuales, ], caption = "La muestra de alumnos")| No. Control | Alumno | Semestre | Cr. Apr. | Carga | Promedio | Carrera |
|---|---|---|---|---|---|---|
| 20190057 | 57 | 9 | 226 | 4 | 89.10 | SISTEMAS |
| 20190116 | 116 | 7 | 165 | 34 | 93.67 | SISTEMAS |
| 20190175 | 175 | 3 | 50 | 33 | 90.91 | SISTEMAS |
| 20190234 | 234 | 7 | 105 | 22 | 84.00 | SISTEMAS |
| 20190293 | 293 | 4 | 83 | 33 | 86.28 | SISTEMAS |
| 20190352 | 352 | 8 | 176 | 32 | 80.47 | SISTEMAS |
| 20190411 | 411 | 7 | 165 | 34 | 82.78 | SISTEMAS |
| 20190470 | 470 | 9 | 198 | 29 | 83.33 | ARQUITECTURA |
| 20190529 | 529 | 10 | 172 | 12 | 79.97 | ARQUITECTURA |
| 20190588 | 588 | 4 | 80 | 30 | 90.28 | ARQUITECTURA |
| 20190647 | 647 | 6 | 124 | 26 | 83.85 | ARQUITECTURA |
| 20190706 | 706 | 1 | NA | 26 | 0.00 | ARQUITECTURA |
| 20190765 | 765 | 1 | NA | 26 | 0.00 | ARQUITECTURA |
| 20190824 | 824 | 6 | 132 | 30 | 82.96 | ARQUITECTURA |
| 20190883 | 883 | 6 | 91 | 30 | 85.53 | ARQUITECTURA |
| 20190942 | 942 | 5 | 88 | 30 | 83.32 | ARQUITECTURA |
| 20191001 | 1001 | 3 | 52 | 24 | 90.50 | ARQUITECTURA |
| 20191060 | 1060 | 1 | NA | 26 | 0.00 | ARQUITECTURA |
| 20191119 | 1119 | 1 | NA | 26 | 0.00 | ARQUITECTURA |
| 20191178 | 1178 | 9 | 140 | 23 | 82.81 | BIOQUIMICA |
| 20191237 | 1237 | 5 | 79 | 31 | 81.78 | BIOQUIMICA |
| 20191296 | 1296 | 8 | 95 | 28 | 76.81 | BIOQUIMICA |
| 20191355 | 1355 | 1 | NA | 23 | 0.00 | BIOQUIMICA |
| 20191414 | 1414 | 1 | NA | 23 | 0.00 | BIOQUIMICA |
| 20191473 | 1473 | 2 | 18 | 29 | 82.60 | BIOQUIMICA |
| 20191532 | 1532 | 3 | 47 | 25 | 87.09 | BIOQUIMICA |
| 20191591 | 1591 | 10 | 225 | 15 | 80.28 | CIVIL |
| 20191650 | 1650 | 9 | 235 | 10 | 91.00 | CIVIL |
| 20191709 | 1709 | 5 | 67 | 8 | 82.71 | CIVIL |
| 20191768 | 1768 | 6 | 139 | 30 | 85.21 | CIVIL |
| 20191827 | 1827 | 1 | NA | 27 | 0.00 | CIVIL |
| 20191886 | 1886 | 4 | 51 | 31 | 78.83 | CIVIL |
| 20191945 | 1945 | 3 | 55 | 30 | 87.33 | CIVIL |
| 20192004 | 2004 | 4 | 78 | 18 | 81.06 | CIVIL |
| 20192063 | 2063 | 5 | 121 | 31 | 87.12 | CIVIL |
| 20192122 | 2122 | 2 | 27 | 26 | 80.17 | CIVIL |
| 20192181 | 2181 | 1 | NA | 27 | 0.00 | CIVIL |
| 20192240 | 2240 | 9 | 221 | 14 | 92.94 | ELECTRICA |
| 20192299 | 2299 | 7 | 160 | 31 | 88.08 | ELECTRICA |
| 20192358 | 2358 | 7 | 98 | 9 | 81.04 | ELECTRICA |
| 20192417 | 2417 | 3 | 56 | 26 | 92.00 | ELECTRICA |
| 20192476 | 2476 | 3 | 51 | 28 | 85.92 | ELECTRICA |
| 20192535 | 2535 | 6 | 104 | 24 | 82.96 | ELECTRONICA |
| 20192594 | 2594 | 1 | NA | 25 | 0.00 | ELECTRONICA |
| 20192653 | 2653 | 5 | 105 | 28 | 95.17 | ELECTRONICA |
| 20192712 | 2712 | 11 | 235 | 10 | 80.68 | INDUSTRIAL |
| 20192771 | 2771 | 4 | 75 | 32 | 80.59 | INDUSTRIAL |
| 20192830 | 2830 | 8 | 174 | 36 | 81.22 | INDUSTRIAL |
| 20192889 | 2889 | 5 | 112 | 30 | 90.72 | INDUSTRIAL |
| 20192948 | 2948 | 6 | 120 | 26 | 79.30 | INDUSTRIAL |
| 20193007 | 3007 | 6 | 142 | 25 | 83.56 | INDUSTRIAL |
| 20193066 | 3066 | 7 | 149 | 25 | 87.74 | INDUSTRIAL |
| 20193125 | 3125 | 3 | 55 | 27 | 84.08 | INDUSTRIAL |
| 20193184 | 3184 | 6 | 139 | 28 | 84.48 | INDUSTRIAL |
| 20193243 | 3243 | 3 | 51 | 29 | 86.83 | INDUSTRIAL |
| 20193302 | 3302 | 5 | 95 | 27 | 81.18 | INDUSTRIAL |
| 20193361 | 3361 | 5 | 87 | 31 | 84.70 | INDUSTRIAL |
| 20193420 | 3420 | 7 | 132 | 27 | 83.52 | MECANICA |
| 20193479 | 3479 | 7 | 142 | 35 | 80.45 | MECANICA |
| 20193538 | 3538 | 5 | 108 | 29 | 84.88 | MECANICA |
| 20193597 | 3597 | 5 | 103 | 34 | 81.17 | MECANICA |
| 20193656 | 3656 | 6 | 113 | 29 | 79.72 | MECANICA |
| 20193715 | 3715 | 10 | 178 | 8 | 79.81 | MECATRONICA |
| 20193774 | 3774 | 7 | 159 | 30 | 87.76 | MECATRONICA |
| 20193833 | 3833 | 7 | 151 | 31 | 82.44 | MECATRONICA |
| 20193892 | 3892 | 6 | 76 | 20 | 81.18 | MECATRONICA |
| 20193951 | 3951 | 6 | 47 | 4 | 82.09 | MECATRONICA |
| 20194010 | 4010 | 1 | NA | 25 | 0.00 | MECATRONICA |
| 20194069 | 4069 | 5 | 105 | 24 | 86.74 | MECATRONICA |
| 20194128 | 4128 | 11 | 161 | 32 | 81.21 | QUIMICA |
| 20194187 | 4187 | 5 | 109 | 25 | 87.22 | QUIMICA |
| 20194246 | 4246 | 9 | 230 | 5 | 85.70 | QUIMICA |
| 20194305 | 4305 | 2 | 11 | 25 | 91.67 | QUIMICA |
| 20194364 | 4364 | 4 | 86 | 28 | 88.50 | QUIMICA |
| 20194423 | 4423 | 9 | 215 | 20 | 83.36 | QUIMICA |
| 20194482 | 4482 | 2 | 25 | 30 | 82.00 | QUIMICA |
| 20194541 | 4541 | 5 | 88 | 29 | 84.84 | QUIMICA |
| 20194600 | 4600 | 9 | 204 | 20 | 82.31 | QUIMICA |
| 20194659 | 4659 | 7 | 162 | 30 | 88.71 | QUIMICA |
| 20194718 | 4718 | 10 | 225 | 10 | 85.17 | GESTION EMPRESARIAL |
| 20194777 | 4777 | 5 | 107 | 33 | 87.87 | GESTION EMPRESARIAL |
| 20194836 | 4836 | 1 | NA | 27 | 0.00 | GESTION EMPRESARIAL |
| 20194895 | 4895 | 3 | 53 | 29 | 87.92 | GESTION EMPRESARIAL |
| 20194954 | 4954 | 2 | 22 | 26 | 91.20 | GESTION EMPRESARIAL |
| 20195013 | 5013 | 2 | 27 | 27 | 84.50 | GESTION EMPRESARIAL |
| 20195072 | 5072 | 3 | 54 | 28 | 93.08 | GESTION EMPRESARIAL |
| 20195131 | 5131 | 3 | 54 | 28 | 90.75 | GESTION EMPRESARIAL |
| 20195190 | 5190 | 3 | 45 | 33 | 85.10 | GESTION EMPRESARIAL |
| 20195249 | 5249 | 2 | 22 | 27 | 92.40 | GESTION EMPRESARIAL |
| 20195308 | 5308 | 1 | NA | 26 | 0.00 | TIC |
| 20195367 | 5367 | 7 | 85 | 18 | 82.58 | INFORMATICA |
| 20195426 | 5426 | 7 | 156 | 33 | 90.29 | INFORMATICA |
| 20195485 | 5485 | 9 | 262 | 10 | 92.09 | ADMINISTRACION |
| 20195544 | 5544 | 5 | 89 | 28 | 85.63 | ADMINISTRACION |
| 20195603 | 5603 | 1 | NA | 27 | 0.00 | ADMINISTRACION |
| 20195662 | 5662 | 1 | NA | 27 | 0.00 | ADMINISTRACION |
| 20195721 | 5721 | 8 | 180 | 34 | 85.00 | ADMINISTRACION |
| 20195780 | 5780 | 4 | 84 | 33 | 89.94 | ADMINISTRACION |
| 20195839 | 5839 | 6 | 140 | 28 | 91.93 | ADMINISTRACION |
| 20195898 | 5898 | 2 | 23 | 28 | 87.80 | ADMINISTRACION |
Con el conjunto de datos de personas se trata de encontrar 10 , pero que sea representativa de acuerdo y conforme al género femenino y masculino. ¿Cuál es la frecuencia relativa del género femenino? ¿Cuál es la frecuencia relativa del género masculino? Ambas frecuencias multiplicar por el tamaño de la muestra para garantizar imparcialidad en la muestra.
N <- nrow(personas)
n <- 10
femeninos <- filter(personas, generos=='F')
masculinos <- filter(personas, generos=='M')
frfem <- nrow(femeninos) / N
frmas <- nrow(masculinos) / N
frfem ## [1] 0.42frmas ## [1] 0.58muestraFem <- sample(femeninos, n * frfem)
kable(muestraFem, caption = "La muestra de personas Femenino")| nombres | generos | ajedrez | beisbol | tiro.arco | pesas | futbol | softbol | atletismo | folklorico | tahitiano | teatro | rondalla | pantomima | orig.id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | GUADALUPE | F | NO | NO | NO | NO | NO | NO | NO | NO | NO | NO | NO | NO | 2 |
| 15 | TERESA | F | NO | NO | NO | NO | NO | NO | NO | SI | NO | NO | NO | NO | 15 |
| 14 | FRANCISCA | F | NO | NO | SI | NO | NO | NO | SI | NO | NO | NO | NO | NO | 14 |
| 7 | JAVIER | F | NO | NO | NO | NO | NO | SI | NO | NO | NO | NO | SI | NO | 7 |
muestraMas <- sample(masculinos, n * frmas)
kable(muestraMas, caption = "La muestra de personas Masculino")| nombres | generos | ajedrez | beisbol | tiro.arco | pesas | futbol | softbol | atletismo | folklorico | tahitiano | teatro | rondalla | pantomima | orig.id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 30 | MARIO | M | NO | NO | SI | SI | NO | NO | NO | NO | NO | NO | NO | NO | 30 |
| 52 | JOSÉ GUADALUPE | M | NO | NO | NO | NO | NO | SI | NO | NO | NO | NO | NO | SI | 52 |
| 7 | MIGUEL ÁNGEL | M | NO | NO | NO | NO | NO | NO | NO | NO | SI | NO | NO | NO | 7 |
| 58 | GUSTAVO | M | NO | NO | NO | NO | NO | NO | NO | SI | NO | NO | NO | NO | 58 |
| 34 | LUIS | M | NO | NO | NO | NO | NO | NO | NO | NO | SI | NO | NO | SI | 34 |
N <- nrow(alumnos)
n <- 100
tabla_frec <- data.frame(fdt_cat(alumnos$Carrera))
tabla_frec$muestra <- round(tabla_frec$rf * n, 0)
kable(tabla_frec, caption = "Tabla de frecuencia de alumnos")| Category | f | rf | rf… | cf | cf… | muestra |
|---|---|---|---|---|---|---|
| INDUSTRIAL | 707 | 0.1192444 | 11.924439 | 707 | 11.92444 | 12 |
| ARQUITECTURA | 675 | 0.1138472 | 11.384719 | 1382 | 23.30916 | 11 |
| CIVIL | 648 | 0.1092933 | 10.929330 | 2030 | 34.23849 | 11 |
| GESTION EMPRESARIAL | 585 | 0.0986676 | 9.866757 | 2615 | 44.10525 | 10 |
| QUIMICA | 568 | 0.0958003 | 9.580030 | 3183 | 53.68528 | 10 |
| ADMINISTRACION | 497 | 0.0838253 | 8.382527 | 3680 | 62.06780 | 8 |
| SISTEMAS | 452 | 0.0762355 | 7.623545 | 4132 | 69.69135 | 8 |
| BIOQUIMICA | 441 | 0.0743802 | 7.438016 | 4573 | 77.12936 | 7 |
| MECATRONICA | 432 | 0.0728622 | 7.286220 | 5005 | 84.41558 | 7 |
| MECANICA | 301 | 0.0507674 | 5.076741 | 5306 | 89.49233 | 5 |
| ELECTRICA | 280 | 0.0472255 | 4.722550 | 5586 | 94.21488 | 5 |
| ELECTRONICA | 161 | 0.0271547 | 2.715466 | 5747 | 96.93034 | 3 |
| INFORMATICA | 101 | 0.0170349 | 1.703491 | 5848 | 98.63383 | 2 |
| TIC | 81 | 0.0136617 | 1.366166 | 5929 | 100.00000 | 1 |
N <- nrow(alumnos)
n <- 100
sistemas <- filter(alumnos, Carrera =='SISTEMAS')
civil <- filter(alumnos, Carrera == 'CIVIL')
frsistemas <- nrow(sistemas) / N
frcivil <- nrow(civil) / N
frsistemas## [1] 0.07623545frcivil## [1] 0.1092933muestrasistemas <- sample(sistemas, round(n * frsistemas, 0))
kable(muestrasistemas, caption = "La muestra de alumnos de Sistemas")| No. Control | Alumno | Semestre | Cr. Apr. | Carga | Promedio | Carrera | orig.id |
|---|---|---|---|---|---|---|---|
| 20190279 | 279 | 8 | 177 | 31 | 88.82 | SISTEMAS | 279 |
| 20190127 | 127 | 4 | 68 | 34 | 80.53 | SISTEMAS | 127 |
| 20190048 | 48 | 9 | 212 | 4 | 91.28 | SISTEMAS | 48 |
| 20190104 | 104 | 3 | 50 | 33 | 86.55 | SISTEMAS | 104 |
| 20190452 | 452 | 2 | 27 | 28 | 84.50 | SISTEMAS | 452 |
| 20190226 | 226 | 6 | 128 | 32 | 83.18 | SISTEMAS | 226 |
| 20190184 | 184 | 5 | 116 | 26 | 92.64 | SISTEMAS | 184 |
| 20190356 | 356 | 3 | 55 | 28 | 91.67 | SISTEMAS | 356 |
muestracivil <- sample(civil, round(n * frcivil, 0))
kable(muestracivil, caption = "La muestra de alumnos de Civil")| No. Control | Alumno | Semestre | Cr. Apr. | Carga | Promedio | Carrera | orig.id |
|---|---|---|---|---|---|---|---|
| 20191740 | 1740 | 5 | 113 | 30 | 88.63 | CIVIL | 172 |
| 20192009 | 2009 | 4 | 82 | 31 | 82.71 | CIVIL | 441 |
| 20191578 | 1578 | 10 | 205 | 25 | 81.95 | CIVIL | 10 |
| 20191905 | 1905 | 7 | 154 | 32 | 82.64 | CIVIL | 337 |
| 20191984 | 1984 | 6 | 133 | 30 | 86.79 | CIVIL | 416 |
| 20191731 | 1731 | 8 | 187 | 25 | 86.03 | CIVIL | 163 |
| 20191798 | 1798 | 6 | 116 | 34 | 84.04 | CIVIL | 230 |
| 20191829 | 1829 | 6 | 97 | 28 | 79.57 | CIVIL | 261 |
| 20192158 | 2158 | 2 | 27 | 30 | 93.17 | CIVIL | 590 |
| 20192056 | 2056 | 8 | 172 | 21 | 88.53 | CIVIL | 488 |
| 20191587 | 1587 | 10 | 216 | 14 | 78.87 | CIVIL | 19 |
Al conjunto de datos alumnos agregar tres columnas. Primero cargar datos de localidades de Durango
N <- nrow(alumnos)
n <- 100
locdurangomx <- read.csv("https://raw.githubusercontent.com/rpizarrog/probabilidad-y-estad-stica/master/datos/locdurangomx.csv", encoding = "UTF-8")localidades50 <- locdurangomx[sample(nrow(locdurangomx), 5), ]
# localidades50alumlocalidades <- sample(localidades50, N, replace = TRUE)
alumnos$localidad <- alumlocalidades$Nom_Loc
alumnos$latitud <- alumlocalidades$Lat_Decimal
alumnos$longitud <- alumlocalidades$Lon_Decimal
kable(head(alumnos, 10), caption = "Los primeros diez registros de alumnos")| No. Control | Alumno | Semestre | Cr. Apr. | Carga | Promedio | Carrera | localidad | latitud | longitud |
|---|---|---|---|---|---|---|---|---|---|
| 20190001 | 1 | 11 | 198 | 19 | 80.21 | SISTEMAS | El Pavo Real | 23.96133 | -104.5428 |
| 20190002 | 2 | 11 | 235 | 10 | 84.33 | SISTEMAS | La Carreta del Fuerte (Predios Rústicos Navíos) | 23.92002 | -105.0540 |
| 20190003 | 3 | 9 | 235 | 10 | 95.25 | SISTEMAS | El Pavo Real | 23.96133 | -104.5428 |
| 20190004 | 4 | 9 | 226 | 19 | 95.00 | SISTEMAS | Rancho Triple R | 24.15531 | -104.5130 |
| 20190005 | 5 | 10 | 231 | 14 | 82.32 | SISTEMAS | Rancho Triple R | 24.15531 | -104.5130 |
| 20190006 | 6 | 9 | 212 | 23 | 95.02 | SISTEMAS | El Mezteño | 23.75246 | -105.0537 |
| 20190007 | 7 | 12 | 221 | 10 | 79.06 | SISTEMAS | El Mezteño | 23.75246 | -105.0537 |
| 20190008 | 8 | 9 | 226 | 9 | 92.47 | SISTEMAS | Rancho Triple R | 24.15531 | -104.5130 |
| 20190009 | 9 | 9 | 231 | 4 | 91.08 | SISTEMAS | La Carreta del Fuerte (Predios Rústicos Navíos) | 23.92002 | -105.0540 |
| 20190010 | 10 | 11 | 222 | 13 | 80.42 | SISTEMAS | El Pavo Real | 23.96133 | -104.5428 |
kable(tail(alumnos, 10), caption = "Las útimos diez registros de alumnos")| No. Control | Alumno | Semestre | Cr. Apr. | Carga | Promedio | Carrera | localidad | latitud | longitud |
|---|---|---|---|---|---|---|---|---|---|
| 20195920 | 5920 | 7 | 169 | 23 | 89.14 | ADMINISTRACION | El Mezteño | 23.75246 | -105.0537 |
| 20195921 | 5921 | 5 | 109 | 26 | 87.83 | ADMINISTRACION | El Pavo Real | 23.96133 | -104.5428 |
| 20195922 | 5922 | 3 | 55 | 29 | 92.83 | ADMINISTRACION | La Carreta del Fuerte (Predios Rústicos Navíos) | 23.92002 | -105.0540 |
| 20195923 | 5923 | 2 | 23 | 23 | 88.60 | ADMINISTRACION | Santiago Bayacora | 23.89576 | -104.6144 |
| 20195924 | 5924 | 2 | 27 | 28 | 92.83 | ADMINISTRACION | La Carreta del Fuerte (Predios Rústicos Navíos) | 23.92002 | -105.0540 |
| 20195925 | 5925 | 7 | 94 | 13 | 80.95 | ADMINISTRACION | Santiago Bayacora | 23.89576 | -104.6144 |
| 20195926 | 5926 | 5 | 103 | 32 | 92.68 | ADMINISTRACION | El Mezteño | 23.75246 | -105.0537 |
| 20195927 | 5927 | 4 | 79 | 34 | 86.18 | ADMINISTRACION | El Pavo Real | 23.96133 | -104.5428 |
| 20195928 | 5928 | 5 | 108 | 32 | 90.48 | ADMINISTRACION | La Carreta del Fuerte (Predios Rústicos Navíos) | 23.92002 | -105.0540 |
| 20195929 | 5929 | 7 | 169 | 32 | 92.33 | ADMINISTRACION | Rancho Triple R | 24.15531 | -104.5130 |
N <- nrow(alumnos)
n <- 100
tabla_frec <- data.frame(fdt_cat(alumnos$localidad))
tabla_frec$muestra <- round(tabla_frec$rf * n, 0)
kable(tabla_frec, caption = "Tabla de frecuencia de alumnos por localidad")| Category | f | rf | rf… | cf | cf… | muestra |
|---|---|---|---|---|---|---|
| El Mezteño | 1216 | 0.2050936 | 20.50936 | 1216 | 20.50936 | 21 |
| Santiago Bayacora | 1208 | 0.2037443 | 20.37443 | 2424 | 40.88379 | 20 |
| La Carreta del Fuerte (Predios Rústicos Navíos) | 1181 | 0.1991904 | 19.91904 | 3605 | 60.80283 | 20 |
| Rancho Triple R | 1175 | 0.1981784 | 19.81784 | 4780 | 80.62068 | 20 |
| El Pavo Real | 1149 | 0.1937932 | 19.37932 | 5929 | 100.00000 | 19 |
N <- nrow(alumnos)
n <- 100
loc1 <- filter(alumnos, localidad == tabla_frec$Category[1])
loc2 <- filter(alumnos, localidad == tabla_frec$Category[2])
loc3 <- filter(alumnos, localidad == tabla_frec$Category[3])
loc4 <- filter(alumnos, localidad == tabla_frec$Category[4])
loc5 <- filter(alumnos, localidad == tabla_frec$Category[5])
frloc1 <- nrow(loc1) / N
frloc2 <- nrow(loc2) / N
frloc3 <- nrow(loc3) / N
frloc4 <- nrow(loc4) / N
frloc5 <- nrow(loc5) / N
muestraloc1 <- sample(loc1, round(n * frloc1, 0))
kable(muestraloc1, caption = paste("La muestra de alumnos de Localidad ",tabla_frec$Category[1] ))| No. Control | Alumno | Semestre | Cr. Apr. | Carga | Promedio | Carrera | localidad | latitud | longitud | orig.id |
|---|---|---|---|---|---|---|---|---|---|---|
| 20191274 | 1274 | 3 | 57 | 27 | 85.08 | BIOQUIMICA | El Mezteño | 23.75246 | -105.0537 | 249 |
| 20191507 | 1507 | 4 | 82 | 28 | 83.11 | BIOQUIMICA | El Mezteño | 23.75246 | -105.0537 | 292 |
| 20192350 | 2350 | 7 | 160 | 31 | 92.33 | ELECTRICA | El Mezteño | 23.75246 | -105.0537 | 454 |
| 20191075 | 1075 | 6 | 142 | 24 | 87.33 | ARQUITECTURA | El Mezteño | 23.75246 | -105.0537 | 211 |
| 20193752 | 3752 | 7 | 164 | 22 | 88.23 | MECATRONICA | El Mezteño | 23.75246 | -105.0537 | 775 |
| 20192811 | 2811 | 4 | 57 | 36 | 79.23 | INDUSTRIAL | El Mezteño | 23.75246 | -105.0537 | 560 |
| 20194200 | 4200 | 8 | 145 | 16 | 84.65 | QUIMICA | El Mezteño | 23.75246 | -105.0537 | 871 |
| 20190817 | 817 | 3 | 52 | 28 | 91.33 | ARQUITECTURA | El Mezteño | 23.75246 | -105.0537 | 161 |
| 20193397 | 3397 | 9 | 178 | 23 | 82.05 | MECANICA | El Mezteño | 23.75246 | -105.0537 | 701 |
| 20195404 | 5404 | 1 | NA | 27 | 0.00 | INFORMATICA | El Mezteño | 23.75246 | -105.0537 | 1106 |
| 20192824 | 2824 | 3 | 61 | 22 | 81.57 | INDUSTRIAL | El Mezteño | 23.75246 | -105.0537 | 564 |
| 20193250 | 3250 | 3 | 51 | 25 | 85.92 | INDUSTRIAL | El Mezteño | 23.75246 | -105.0537 | 665 |
| 20193654 | 3654 | 7 | 163 | 26 | 84.43 | MECANICA | El Mezteño | 23.75246 | -105.0537 | 752 |
| 20192013 | 2013 | 7 | 155 | 36 | 81.73 | CIVIL | El Mezteño | 23.75246 | -105.0537 | 384 |
| 20191000 | 1000 | 1 | NA | 26 | 0.00 | ARQUITECTURA | El Mezteño | 23.75246 | -105.0537 | 196 |
| 20195073 | 5073 | 1 | NA | 27 | 0.00 | GESTION EMPRESARIAL | El Mezteño | 23.75246 | -105.0537 | 1048 |
| 20191335 | 1335 | 1 | NA | 23 | 0.00 | BIOQUIMICA | El Mezteño | 23.75246 | -105.0537 | 260 |
| 20191946 | 1946 | 5 | 112 | 29 | 88.50 | CIVIL | El Mezteño | 23.75246 | -105.0537 | 372 |
| 20191307 | 1307 | 7 | 105 | 28 | 79.30 | BIOQUIMICA | El Mezteño | 23.75246 | -105.0537 | 256 |
| 20193875 | 3875 | 4 | 67 | 23 | 79.07 | MECATRONICA | El Mezteño | 23.75246 | -105.0537 | 800 |
| 20190528 | 528 | 9 | 215 | 16 | 88.59 | ARQUITECTURA | El Mezteño | 23.75246 | -105.0537 | 103 |
muestraloc2 <- sample(loc2, round(n * frloc2, 0))
kable(muestraloc1, caption = paste("La muestra de alumnos de Localidad ",tabla_frec$Category[2] ))| No. Control | Alumno | Semestre | Cr. Apr. | Carga | Promedio | Carrera | localidad | latitud | longitud | orig.id |
|---|---|---|---|---|---|---|---|---|---|---|
| 20191274 | 1274 | 3 | 57 | 27 | 85.08 | BIOQUIMICA | El Mezteño | 23.75246 | -105.0537 | 249 |
| 20191507 | 1507 | 4 | 82 | 28 | 83.11 | BIOQUIMICA | El Mezteño | 23.75246 | -105.0537 | 292 |
| 20192350 | 2350 | 7 | 160 | 31 | 92.33 | ELECTRICA | El Mezteño | 23.75246 | -105.0537 | 454 |
| 20191075 | 1075 | 6 | 142 | 24 | 87.33 | ARQUITECTURA | El Mezteño | 23.75246 | -105.0537 | 211 |
| 20193752 | 3752 | 7 | 164 | 22 | 88.23 | MECATRONICA | El Mezteño | 23.75246 | -105.0537 | 775 |
| 20192811 | 2811 | 4 | 57 | 36 | 79.23 | INDUSTRIAL | El Mezteño | 23.75246 | -105.0537 | 560 |
| 20194200 | 4200 | 8 | 145 | 16 | 84.65 | QUIMICA | El Mezteño | 23.75246 | -105.0537 | 871 |
| 20190817 | 817 | 3 | 52 | 28 | 91.33 | ARQUITECTURA | El Mezteño | 23.75246 | -105.0537 | 161 |
| 20193397 | 3397 | 9 | 178 | 23 | 82.05 | MECANICA | El Mezteño | 23.75246 | -105.0537 | 701 |
| 20195404 | 5404 | 1 | NA | 27 | 0.00 | INFORMATICA | El Mezteño | 23.75246 | -105.0537 | 1106 |
| 20192824 | 2824 | 3 | 61 | 22 | 81.57 | INDUSTRIAL | El Mezteño | 23.75246 | -105.0537 | 564 |
| 20193250 | 3250 | 3 | 51 | 25 | 85.92 | INDUSTRIAL | El Mezteño | 23.75246 | -105.0537 | 665 |
| 20193654 | 3654 | 7 | 163 | 26 | 84.43 | MECANICA | El Mezteño | 23.75246 | -105.0537 | 752 |
| 20192013 | 2013 | 7 | 155 | 36 | 81.73 | CIVIL | El Mezteño | 23.75246 | -105.0537 | 384 |
| 20191000 | 1000 | 1 | NA | 26 | 0.00 | ARQUITECTURA | El Mezteño | 23.75246 | -105.0537 | 196 |
| 20195073 | 5073 | 1 | NA | 27 | 0.00 | GESTION EMPRESARIAL | El Mezteño | 23.75246 | -105.0537 | 1048 |
| 20191335 | 1335 | 1 | NA | 23 | 0.00 | BIOQUIMICA | El Mezteño | 23.75246 | -105.0537 | 260 |
| 20191946 | 1946 | 5 | 112 | 29 | 88.50 | CIVIL | El Mezteño | 23.75246 | -105.0537 | 372 |
| 20191307 | 1307 | 7 | 105 | 28 | 79.30 | BIOQUIMICA | El Mezteño | 23.75246 | -105.0537 | 256 |
| 20193875 | 3875 | 4 | 67 | 23 | 79.07 | MECATRONICA | El Mezteño | 23.75246 | -105.0537 | 800 |
| 20190528 | 528 | 9 | 215 | 16 | 88.59 | ARQUITECTURA | El Mezteño | 23.75246 | -105.0537 | 103 |
muestraloc3 <- sample(loc3, round(n * frloc3, 0))
kable(muestraloc1, caption = paste("La muestra de alumnos de Localidad ",tabla_frec$Category[3] ))| No. Control | Alumno | Semestre | Cr. Apr. | Carga | Promedio | Carrera | localidad | latitud | longitud | orig.id |
|---|---|---|---|---|---|---|---|---|---|---|
| 20191274 | 1274 | 3 | 57 | 27 | 85.08 | BIOQUIMICA | El Mezteño | 23.75246 | -105.0537 | 249 |
| 20191507 | 1507 | 4 | 82 | 28 | 83.11 | BIOQUIMICA | El Mezteño | 23.75246 | -105.0537 | 292 |
| 20192350 | 2350 | 7 | 160 | 31 | 92.33 | ELECTRICA | El Mezteño | 23.75246 | -105.0537 | 454 |
| 20191075 | 1075 | 6 | 142 | 24 | 87.33 | ARQUITECTURA | El Mezteño | 23.75246 | -105.0537 | 211 |
| 20193752 | 3752 | 7 | 164 | 22 | 88.23 | MECATRONICA | El Mezteño | 23.75246 | -105.0537 | 775 |
| 20192811 | 2811 | 4 | 57 | 36 | 79.23 | INDUSTRIAL | El Mezteño | 23.75246 | -105.0537 | 560 |
| 20194200 | 4200 | 8 | 145 | 16 | 84.65 | QUIMICA | El Mezteño | 23.75246 | -105.0537 | 871 |
| 20190817 | 817 | 3 | 52 | 28 | 91.33 | ARQUITECTURA | El Mezteño | 23.75246 | -105.0537 | 161 |
| 20193397 | 3397 | 9 | 178 | 23 | 82.05 | MECANICA | El Mezteño | 23.75246 | -105.0537 | 701 |
| 20195404 | 5404 | 1 | NA | 27 | 0.00 | INFORMATICA | El Mezteño | 23.75246 | -105.0537 | 1106 |
| 20192824 | 2824 | 3 | 61 | 22 | 81.57 | INDUSTRIAL | El Mezteño | 23.75246 | -105.0537 | 564 |
| 20193250 | 3250 | 3 | 51 | 25 | 85.92 | INDUSTRIAL | El Mezteño | 23.75246 | -105.0537 | 665 |
| 20193654 | 3654 | 7 | 163 | 26 | 84.43 | MECANICA | El Mezteño | 23.75246 | -105.0537 | 752 |
| 20192013 | 2013 | 7 | 155 | 36 | 81.73 | CIVIL | El Mezteño | 23.75246 | -105.0537 | 384 |
| 20191000 | 1000 | 1 | NA | 26 | 0.00 | ARQUITECTURA | El Mezteño | 23.75246 | -105.0537 | 196 |
| 20195073 | 5073 | 1 | NA | 27 | 0.00 | GESTION EMPRESARIAL | El Mezteño | 23.75246 | -105.0537 | 1048 |
| 20191335 | 1335 | 1 | NA | 23 | 0.00 | BIOQUIMICA | El Mezteño | 23.75246 | -105.0537 | 260 |
| 20191946 | 1946 | 5 | 112 | 29 | 88.50 | CIVIL | El Mezteño | 23.75246 | -105.0537 | 372 |
| 20191307 | 1307 | 7 | 105 | 28 | 79.30 | BIOQUIMICA | El Mezteño | 23.75246 | -105.0537 | 256 |
| 20193875 | 3875 | 4 | 67 | 23 | 79.07 | MECATRONICA | El Mezteño | 23.75246 | -105.0537 | 800 |
| 20190528 | 528 | 9 | 215 | 16 | 88.59 | ARQUITECTURA | El Mezteño | 23.75246 | -105.0537 | 103 |
muestraloc4 <- sample(loc4, round(n * frloc4, 0))
kable(muestraloc1, caption = paste("La muestra de alumnos de Localidad ",tabla_frec$Category[4] ))| No. Control | Alumno | Semestre | Cr. Apr. | Carga | Promedio | Carrera | localidad | latitud | longitud | orig.id |
|---|---|---|---|---|---|---|---|---|---|---|
| 20191274 | 1274 | 3 | 57 | 27 | 85.08 | BIOQUIMICA | El Mezteño | 23.75246 | -105.0537 | 249 |
| 20191507 | 1507 | 4 | 82 | 28 | 83.11 | BIOQUIMICA | El Mezteño | 23.75246 | -105.0537 | 292 |
| 20192350 | 2350 | 7 | 160 | 31 | 92.33 | ELECTRICA | El Mezteño | 23.75246 | -105.0537 | 454 |
| 20191075 | 1075 | 6 | 142 | 24 | 87.33 | ARQUITECTURA | El Mezteño | 23.75246 | -105.0537 | 211 |
| 20193752 | 3752 | 7 | 164 | 22 | 88.23 | MECATRONICA | El Mezteño | 23.75246 | -105.0537 | 775 |
| 20192811 | 2811 | 4 | 57 | 36 | 79.23 | INDUSTRIAL | El Mezteño | 23.75246 | -105.0537 | 560 |
| 20194200 | 4200 | 8 | 145 | 16 | 84.65 | QUIMICA | El Mezteño | 23.75246 | -105.0537 | 871 |
| 20190817 | 817 | 3 | 52 | 28 | 91.33 | ARQUITECTURA | El Mezteño | 23.75246 | -105.0537 | 161 |
| 20193397 | 3397 | 9 | 178 | 23 | 82.05 | MECANICA | El Mezteño | 23.75246 | -105.0537 | 701 |
| 20195404 | 5404 | 1 | NA | 27 | 0.00 | INFORMATICA | El Mezteño | 23.75246 | -105.0537 | 1106 |
| 20192824 | 2824 | 3 | 61 | 22 | 81.57 | INDUSTRIAL | El Mezteño | 23.75246 | -105.0537 | 564 |
| 20193250 | 3250 | 3 | 51 | 25 | 85.92 | INDUSTRIAL | El Mezteño | 23.75246 | -105.0537 | 665 |
| 20193654 | 3654 | 7 | 163 | 26 | 84.43 | MECANICA | El Mezteño | 23.75246 | -105.0537 | 752 |
| 20192013 | 2013 | 7 | 155 | 36 | 81.73 | CIVIL | El Mezteño | 23.75246 | -105.0537 | 384 |
| 20191000 | 1000 | 1 | NA | 26 | 0.00 | ARQUITECTURA | El Mezteño | 23.75246 | -105.0537 | 196 |
| 20195073 | 5073 | 1 | NA | 27 | 0.00 | GESTION EMPRESARIAL | El Mezteño | 23.75246 | -105.0537 | 1048 |
| 20191335 | 1335 | 1 | NA | 23 | 0.00 | BIOQUIMICA | El Mezteño | 23.75246 | -105.0537 | 260 |
| 20191946 | 1946 | 5 | 112 | 29 | 88.50 | CIVIL | El Mezteño | 23.75246 | -105.0537 | 372 |
| 20191307 | 1307 | 7 | 105 | 28 | 79.30 | BIOQUIMICA | El Mezteño | 23.75246 | -105.0537 | 256 |
| 20193875 | 3875 | 4 | 67 | 23 | 79.07 | MECATRONICA | El Mezteño | 23.75246 | -105.0537 | 800 |
| 20190528 | 528 | 9 | 215 | 16 | 88.59 | ARQUITECTURA | El Mezteño | 23.75246 | -105.0537 | 103 |
muestraloc5 <- sample(loc5, round(n * frloc5, 0))
kable(muestraloc1, caption = paste("La muestra de alumnos de Localidad ",tabla_frec$Category[5] ))| No. Control | Alumno | Semestre | Cr. Apr. | Carga | Promedio | Carrera | localidad | latitud | longitud | orig.id |
|---|---|---|---|---|---|---|---|---|---|---|
| 20191274 | 1274 | 3 | 57 | 27 | 85.08 | BIOQUIMICA | El Mezteño | 23.75246 | -105.0537 | 249 |
| 20191507 | 1507 | 4 | 82 | 28 | 83.11 | BIOQUIMICA | El Mezteño | 23.75246 | -105.0537 | 292 |
| 20192350 | 2350 | 7 | 160 | 31 | 92.33 | ELECTRICA | El Mezteño | 23.75246 | -105.0537 | 454 |
| 20191075 | 1075 | 6 | 142 | 24 | 87.33 | ARQUITECTURA | El Mezteño | 23.75246 | -105.0537 | 211 |
| 20193752 | 3752 | 7 | 164 | 22 | 88.23 | MECATRONICA | El Mezteño | 23.75246 | -105.0537 | 775 |
| 20192811 | 2811 | 4 | 57 | 36 | 79.23 | INDUSTRIAL | El Mezteño | 23.75246 | -105.0537 | 560 |
| 20194200 | 4200 | 8 | 145 | 16 | 84.65 | QUIMICA | El Mezteño | 23.75246 | -105.0537 | 871 |
| 20190817 | 817 | 3 | 52 | 28 | 91.33 | ARQUITECTURA | El Mezteño | 23.75246 | -105.0537 | 161 |
| 20193397 | 3397 | 9 | 178 | 23 | 82.05 | MECANICA | El Mezteño | 23.75246 | -105.0537 | 701 |
| 20195404 | 5404 | 1 | NA | 27 | 0.00 | INFORMATICA | El Mezteño | 23.75246 | -105.0537 | 1106 |
| 20192824 | 2824 | 3 | 61 | 22 | 81.57 | INDUSTRIAL | El Mezteño | 23.75246 | -105.0537 | 564 |
| 20193250 | 3250 | 3 | 51 | 25 | 85.92 | INDUSTRIAL | El Mezteño | 23.75246 | -105.0537 | 665 |
| 20193654 | 3654 | 7 | 163 | 26 | 84.43 | MECANICA | El Mezteño | 23.75246 | -105.0537 | 752 |
| 20192013 | 2013 | 7 | 155 | 36 | 81.73 | CIVIL | El Mezteño | 23.75246 | -105.0537 | 384 |
| 20191000 | 1000 | 1 | NA | 26 | 0.00 | ARQUITECTURA | El Mezteño | 23.75246 | -105.0537 | 196 |
| 20195073 | 5073 | 1 | NA | 27 | 0.00 | GESTION EMPRESARIAL | El Mezteño | 23.75246 | -105.0537 | 1048 |
| 20191335 | 1335 | 1 | NA | 23 | 0.00 | BIOQUIMICA | El Mezteño | 23.75246 | -105.0537 | 260 |
| 20191946 | 1946 | 5 | 112 | 29 | 88.50 | CIVIL | El Mezteño | 23.75246 | -105.0537 | 372 |
| 20191307 | 1307 | 7 | 105 | 28 | 79.30 | BIOQUIMICA | El Mezteño | 23.75246 | -105.0537 | 256 |
| 20193875 | 3875 | 4 | 67 | 23 | 79.07 | MECATRONICA | El Mezteño | 23.75246 | -105.0537 | 800 |
| 20190528 | 528 | 9 | 215 | 16 | 88.59 | ARQUITECTURA | El Mezteño | 23.75246 | -105.0537 | 103 |
#install.packages("leaflet")
library(leaflet)## Warning: package 'leaflet' was built under R version 4.0.3map<-leaflet() %>%
addTiles() %>%
addMarkers(lat=localidades50$Lat_Decimal[1],lng=localidades50$Lon_Decimal[1] ,popup=paste(localidades50$Nom_Loc[1], " ", tabla_frec$muestra[1])) %>%
addMarkers(lat=localidades50$Lat_Decimal[2],lng=localidades50$Lon_Decimal[2] ,popup=paste(localidades50$Nom_Loc[2], " ", tabla_frec$muestra[2])) %>%
addMarkers(lat=localidades50$Lat_Decimal[3],lng=localidades50$Lon_Decimal[3] ,popup=paste(localidades50$Nom_Loc[3], " ", tabla_frec$muestra[3])) %>%
addMarkers(lat=localidades50$Lat_Decimal[4],lng=localidades50$Lon_Decimal[4] ,popup=paste(localidades50$Nom_Loc[4], " ", tabla_frec$muestra[4])) %>%
addMarkers(lat=localidades50$Lat_Decimal[5],lng=localidades50$Lon_Decimal[5] ,popup=paste(localidades50$Nom_Loc[5], " ", tabla_frec$muestra[5]))
# Mostrar el mapa
mapAnderson, D. R., Sweeney, D. J., & Williams, T. A. (2008). Estadística para administración y economía (10th ed.). Cengage Learning,
Lind, D., Marchal, W., & Wathen, S. (2015). Estadística aplicada a los negocios y la economía (Decimo Sexta). McGraw-Hill.