Temario corte 1

Introducción a R y R Studio Configuración y entorno de trabajo.

  1. 1.1.¿Qué es un objeto en R?

En R, un objeto es una estructura fundamental que almacena datos, resultados o funciones. Todo en R es un objeto, desde un simple número hasta un complejo modelo estadístico, entre estos datos como vectores, matrices,data frames, listas; funciones con operacines predefinidas o creadas por el usuario; o resultados octenidos de modelos estadisticos, graficas,etc.

se usa el operador de asignacion <- 0 =

1. 2.¿Cómo se crea y se manipula un objeto?

un objeto es una informacion cualitativa y cuantitativa se introduce asi :

#crear un vector númerico
vector_a<- c(20,51,84,6,0,3)

#crear un data frame 
a_df <- data.frame(
  estudiante= c("Ana","Bruno","carla","Daniel","Enrique"),
  n_hermanos= c(1,13,6,0,3)
)
# crear una lista
lista_a<- list(vector=vector_a, df=a_df)

#crear una lista 
a_funcion <- function(x){x^2}

print(vector_a)
## [1] 20 51 84  6  0  3
print(a_df)
##   estudiante n_hermanos
## 1        Ana          1
## 2      Bruno         13
## 3      carla          6
## 4     Daniel          0
## 5    Enrique          3
print(lista_a)
## $vector
## [1] 20 51 84  6  0  3
## 
## $df
##   estudiante n_hermanos
## 1        Ana          1
## 2      Bruno         13
## 3      carla          6
## 4     Daniel          0
## 5    Enrique          3
print(a_funcion)
## function (x) 
## {
##     x^2
## }

Manipulacion de Objetos

1.2.1. Acceder a elementos

#vector 
vector_a[3]   #tercer elemento
## [1] 84
#Data frame
a_df$estudiante         # Columna 'estudiante'
## [1] "Ana"     "Bruno"   "carla"   "Daniel"  "Enrique"
a_df[1, ]               # Primera fila
estudiante n_hermanos
Ana 1 
a_df[, "n_hermanos"]  # Columna por nombre
## [1]  1 13  6  0  3
# Lista
lista_a$df        # Elemento llamado 'df'
estudiante n_hermanos
Ana 1
Bruno 13
carla 6
Daniel 0
Enrique 3 
lista_a[[1]]      # Primer elemento (sin nombre)
## [1] 20 51 84  6  0  3

1.2.2. Modificar objetos:

# Cambiar un valor en un vector
vector_a[2] <- 10

# Agregar columna a un data frame
a_df$ciudad <- c("Madrid", "la argentina", "Valencia","florencia","neiva")  #debe tener la misma cantidad de elementos que la data ej: 5

# Eliminar un elemento de una lista
lista_a$vector <- NULL

print(vector_a)
## [1] 20 10 84  6  0  3
print(a_df)
##   estudiante n_hermanos       ciudad
## 1        Ana          1       Madrid
## 2      Bruno         13 la argentina
## 3      carla          6     Valencia
## 4     Daniel          0    florencia
## 5    Enrique          3        neiva
print(lista_a)
## $df
##   estudiante n_hermanos
## 1        Ana          1
## 2      Bruno         13
## 3      carla          6
## 4     Daniel          0
## 5    Enrique          3

1.2.3. Operaciones comunes:

# Aritméticas
sum(vector_a)
## [1] 123
mean(a_df$n_hermanos)
## [1] 4.6
# Funciones aplicadas
lapply(lista_a, length)  # Longitud de cada elemento
## $df
## [1] 2
# Transformaciones
a_df$hermanos_mayor_5 <- ifelse(a_df$n_hermanos > 5, "Sí", "No")

print(a_df)
##   estudiante n_hermanos       ciudad hermanos_mayor_5
## 1        Ana          1       Madrid               No
## 2      Bruno         13 la argentina               Sí
## 3      carla          6     Valencia               Sí
## 4     Daniel          0    florencia               No
## 5    Enrique          3        neiva               No

1.2.4 verificacio y coversión

# Verificar tipo de objeto
class(vector_a)    # "numeric"
## [1] "numeric"
is.data.frame(a_df) # TRUE
## [1] TRUE
# Convertir entre tipos
vector_a_lista <- as.list(vector_a)
print(vector_a)
## [1] 20 10 84  6  0  3
print(is.data.frame(a_df))
## [1] TRUE
print(vector_a_lista)
## [[1]]
## [1] 20
## 
## [[2]]
## [1] 10
## 
## [[3]]
## [1] 84
## 
## [[4]]
## [1] 6
## 
## [[5]]
## [1] 0
## 
## [[6]]
## [1] 3

2. ¿Cuál es la diferencia entre una variable y un objeto?

En R, los términos variable y objeto están estrechamente relacionados pero tienen diferencias conceptuales importantes:

2.1. Objeto

cualquier dato almacenado en la memoria de R ademas puede ser manipulado por funciones. ejemplo: vectores, data frame,lista, funciones, modelos estadisticos, gráficos. -NATURALEZA: Estructura de datos en memoria. -TIPO: Tiene clase (numeric, list, etc.) -MUTABILIDAD: Contenido puede cambiar. -EJEMPLO: c(1, 2, 3) (vector).

# Todos estos son objetos:
objeto1 <- 42                 # Número
objeto2 <- "Hola"             # Cadena de texto
print(objeto1)
## [1] 42
print(objeto2)
## [1] "Hola"

2.2. Variable Un Nombre (simbolo) que hace referencia a un objeto en memoria, es una “etiqueta” que apunta a un objeto. es el alias del objeto, puede reasignarse para apuntara a diferentes objetos.

-NATURALEZA:Nombre que referencia un objeto. -TIPO: No tiene tipo (solo referencia). -MUTUABILIDAD: puede reasignarse a otro objeto. -Ejemplo: mi_vector<- c(1,2,3)

# 'x' es una variable que apunta a un objeto (el número 10)
x <- 10  

a_function<- function(x){x^2}
print(a_funcion(x))
## [1] 100
# Ahora 'x' apunta a un nuevo objeto (texto)
x <- "nuevo texto"  
print(x)
## [1] "nuevo texto"

3. ¿Qué es un script en R?

Un script en R es un archivo de texto (con extensión .R) que contiene un conjunto de comandos, funciones y comentarios escritos en lenguaje R, diseñados para ejecutarse secuencialmente. A diferencia de trabajar directamente en la consola interactiva, los scripts permiten: - Reproducir análisis de manera exacta. - Organizar código enpaos lógicos. - gardar y Compartir el fluo de trabajo.

# Script: mi_primer_script.R
# Autor: Tu Nombre
# Fecha: 2025-05-16
# Descripción: Análisis de datos de ventas

# 1. Cargar datos
library(readxl)
## Warning: package 'readxl' was built under R version 4.4.3
clase5 <- read_excel("~/R studio 2025-1/clase5.xlsx")
## Warning: Expecting numeric in C11 / R11C3: got a date
## Warning: Expecting numeric in C12 / R12C3: got a date
## Warning: Expecting numeric in C13 / R13C3: got a date
View(clase5)
print(clase5)
## # A tibble: 18 × 5
##    nombre     edad promedio semestre `esta nivelado si o no`
##    <chr>     <dbl>    <dbl>    <dbl> <chr>                  
##  1 Maura        20     3.5         7 si                     
##  2 Camila       21     3.5         8 no                     
##  3 Johan S.     21     3.5         7 no                     
##  4 elkin        18     3.3         3 no                     
##  5 alejandro    17     3.3         3 no                     
##  6 Yandry       19     3.92        7 si                     
##  7 Nicolas      20     3.74        7 si                     
##  8 Dirley       19     3.6         7 no                     
##  9 william      24     3.5        10 no                     
## 10 daniela      18 45691           7 no                     
## 11 steven       22 45719           6 no                     
## 12 yuliana      20 45719           8 no                     
## 13 karen        19     3.5         7 no                     
## 14 jose         21     3.4         7 no                     
## 15 santiago     20     3.5         5 no                     
## 16 johan        19     3.9         5 si                     
## 17 santiago     20     3.7         6 no                     
## 18 daniela      19     3.7         5 si
# 2. Calcular estadísticas
media_promedio <- mean(clase5$promedio)
desviacion <- sd(clase5$semestre)

print(media_promedio)
## [1] 7621.253
print(desviacion)
## [1] 1.719743
# 3. Visualizar
hist(clase5$promedio, main = "Distribución de promedios", col = "steelblue")

4. ¿Qué son los comentarios en R y cómo se utilizan?

los comentarios en R son textos que deben iniciar la lina con “#” para que no generen conflicto con el cod.

5. Explica la diferencia entre un archivo .R y un archivo .RMarkdown.

el archivo.R es el cod del para ejecutar dentro del programa de Rstudio,se usa para automatizar procesos, desarrollar funciones personalizadas, ejecutar analisis en la consola de R, y el archivo.Rmd es un presentation formal donde combina codigo y texto formateado en latex, permite generar informes dunamicos como, web, diapositivas, y dashboar.

6. ¿Cómo se puede ejecutar solo una parte de un script en RStudio?

Se selecciona la parte que se desea ejecutar, y luego le da la opcion “Run” o tecla Enter.

7. ¿Cuál es la función de la pestaña “Environment” en RStudio?

Es un entorno que almacena las variables, tablas, objetos, listas,etc. que estan cargadis en la memoria de archivo que este manejado en R.Muestra una lista en tiempo real de todos los objetos (datos, variables, funciones, modelos, etc.) que has creado o cargado.

8. Explica la diferencia entre la función print() y la ejecución directa de un objeto en la consola.

la funcion Print() da la visualisacion de una variable o objeto para el archivo.Rmarkdown, y la consola podemos pedirle que nos muestre el objeto o variable o tabla, oara saber que esta guardado con ese nombre.

9. ¿Cuál es la diferencia entre los operadores =, y, <- en R?

la diferencia entre = y <- es

x <- 10  # Asigna el valor 10 a la variable 'x'
mean(x = c(1, 2, 3))  # Correcto en argumentos de función
## [1] 2
x = 10  # Asignación válida, pero menos preferida

__Tipos de objetos, funciones más comunes, operaciones lógicas_

\

  1. Crea un vector con los nombres de cinco países e imprime el tercero y el quinto elemento.
america<-c("Colombia","Bolivia","argentina","Ecuador","Peru")
print(america[c(3,5)])
## [1] "argentina" "Peru"
  1. ¿Cuál es la diferencia entre un vector y un data.frame en R? \ un vector es un aestructura que almacena elementos del mismo tipo(numerico,caracter,logico), miestras que el data.frame es una estructura bidimencional(filas y columnas), similar a una tabla de excel o base de datos.
#vector

vector_char<-c("a","b","c") #vector de cararcteres

#tabla de datos

dg<-data.frame(
  pais=c("colombia","argentina"),
  poblacion=c(51, 44) #En millones 
  )
  1. ¿Qué resultado devuelve la siguiente operación lógica en R? (5 >= 3) & (4 == 2 + 2) | (7 < 1) (==, !=, >, <, >=, <=)
#(5 >= 3) & (4 == 2 + 2) | (7 < 1)
cantidad1 <- 5
cantidad2 <- 3
cantidad3 <- 4
cantidad4 <- 2
cantidad5 <- 2
cantidad6 <- 7
cantidad7 <- 1

comparacion_mayorigual <- cantidad1 >= cantidad2
print(comparacion_mayorigual)
## [1] TRUE
comparacion_igual <- cantidad3 == (cantidad4 + cantidad5)
print(comparacion_igual)
## [1] TRUE
comparacion_menorque<-cantidad6 < cantidad7
print(comparacion_menorque)
## [1] FALSE
  1. ¿Cómo se puede verificar si un objeto en R es de tipo numeric?\\

en la consola puede escribir class (x), x siendo el objeto que desea conocer.

#usar class(x)
class(cantidad1)
## [1] "numeric"
  1. Importa un archivo xlsx llamado “ventas.csv” y guárdalo en un dataframe llamado ventas_data. (que contenga datos ficticios: fecha_venta, articulo, valor del articulo) \
library(readr)
## Warning: package 'readr' was built under R version 4.4.3
venta <- read_csv("~/venta.csv")
## Rows: 24 Columns: 1
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): fecha_venta;articulo; valor_articulo
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
View(venta)


install.packages("readr")  # Si no lo tienes instalado
## Warning: package 'readr' is in use and will not be installed
library(readr)

datos <- read_csv2("~/venta.csv")  # read_csv2 está diseñado para separadores ;
## ℹ Using "','" as decimal and "'.'" as grouping mark. Use `read_delim()` for more control.
## Rows: 24 Columns: 3── Column specification ────────────────────────────────────────────────────────
## Delimiter: ";"
## chr (2): fecha_venta, articulo
## dbl (1): valor_articulo
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
  1. ¿Cómo se importa un archivo Excel en R utilizando el paquete readxl? primero se va a las obciones de FILE segundo IMPORT DATASET terero FROM READXL cuarto seleccionar el archio cvs Quinto copiar el cod para colocar en el scrip sexto importar septimo colocar el cod en el scrip

\\ 7.¿Cómo se pueden renombrar las columnas de un dataframe en R? Cambia el nombre de la primer columna por nombres de clientes ficticios.

#tomamos la tabla de ventas y vamos a cmabiar los nombres.
names(venta)<-c("fecha","objeto","valor") #asignar nombres nuevos
## Warning: The `value` argument of `names<-()` must have the same length as `x` as of
## tibble 3.0.0.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
View(venta)
  1. Crea un dataframe llamado empresa con 3 columnas: “Rol en la compañia” (carácter), “Horas de trabajo” (numérico) y “Permisos en el mes” (numérico). Añade cinco filas con datos ficticios.
empresa <- data.frame(
  rol = c("jefe_administrativo", "supervisor", "almacenista", "secretario", "servicios_g", "jefe_cartera", "juridico", "jefe_fondos", "aux_fondos", "Aux_contable", "fiscal", "informatico",
          "Juridico", "", "juridico", "cartera", "cartera", "secretario", "sercetrio", "celador"),
  Hora_trabajo = c(25, 34, 28, 22, 45, 30, 33, 26, 40, 19, 38, 31, 29, 24, 35, 27, 39, 32, 21, 23),
  Permisos = c(4, 1, 2, 3, 0, 2, 0, 0, 0, 2, 3,0,2, 0, 0, 0, 2, 3,0,0),
  vahiculo = c("moto","moto","bus","bus","carro","moto","moto","bus","bus","carro","moto","moto","bus","bus","carro","moto","moto","bus","bus","carro"),
  numerode_hijos = c(0, 1 ,1 ,2 ,1 ,4 ,0 ,1 ,1 ,1 ,0 ,1 ,1 ,2 ,1 ,4 ,0 ,1 ,1 ,1),
  edad = c(21,22,23,40,65,21,65,25,45,40,21,22,23,40,65,21,65,25,45,40),
  rendimiento1_5 = c(5,4,5,2,3,5,4,5,2,3,5,4,5,2,3,5,4,5,2,3),
  presentacion = c("buena","excelente","regular","excelente","regular","buena","excelente","regular","excelente","regular","buena","excelente","regular","excelente","regular","buena","excelente","regular","excelente","regular")
)

Temario corte 2

ESTADISTICA DESCRIPTIVA

Media = promedio = mean() Mediana = median() Moda = Dato que mas se repite = mode() Desviacion estandar = sd() Varianza = var() Rango = range() summary(data) ncol() main: Para ponertitulo en un grafico

EJERCICIO

nombre<-c("Maura","Camila","Johan","elkin","Alejandro","yandry","Nicolas","Dirley","Willian","Daniela","Steven","Yuliana","Karen","Jose","Santiago","Johan","Santaigo","Daniela")
edad<-c(20,21,21,18,17,19,20,19,18,22,20,19,21,20,19,20,19,20)
promedio<-c(3.5,3.5,3.5,3.3,3.3,3.9,3.7,3.6,3.5,3.2,3.3,3.3,3.5,3.4,3.5,3.9,3.7,3.7)
semestre<-c(7,8,7,3,3,7,7,7,10,7,6,8,7,7,5,5,6,5)
nivelado<-c("si","no","no","no","si","si","no","no","no","no","no","no","no","no","si","no","si","no")
dt<-data.frame(nombre,edad,promedio,semestre,nivelado)

summary(dt)
##     nombre               edad          promedio        semestre     
##  Length:18          Min.   :17.00   Min.   :3.200   Min.   : 3.000  
##  Class :character   1st Qu.:19.00   1st Qu.:3.325   1st Qu.: 5.250  
##  Mode  :character   Median :20.00   Median :3.500   Median : 7.000  
##                     Mean   :19.61   Mean   :3.517   Mean   : 6.389  
##                     3rd Qu.:20.00   3rd Qu.:3.675   3rd Qu.: 7.000  
##                     Max.   :22.00   Max.   :3.900   Max.   :10.000  
##    nivelado        
##  Length:18         
##  Class :character  
##  Mode  :character  
##                    
##                    
##

Visualizacion de datos: Agrupados y no agrupados

Tipos de graficos

Histograma: Muestra la distribucion de una variable numerica dividiendola en intervalos. Analiza la frecuencia de valores dentro de rangos especificos.

edades <- c(18, 22, 21, 25, 20, 22, 24, 26, 28, 22, 23)
hist(edades,
     main = "Distribución de edades",
     xlab = "Edad",
     col = "skyblue",
     border = "black")

Diagrama de caja o de bigotes(boxplot): Visualiza los cuartiles, mediana, y los valores atipicos.Compara distribuciones y detecta outliers.

edades <- c(18, 22, 21, 25, 20, 22, 24, 26, 28, 22, 23, 40)
boxplot(edades,
        main = "Boxplot de edades",
        ylab = "Edad",
        col = "pink")

Grafico de pastel o diagrama de tortas o grafico de sectores (pie chart): Representa proporciones de un total den sectores circulares. visualiza porcentajes o proporciones.

categorias <- c("Aprobados", "Reprobados", "Retirados")
valores <- c(50, 30, 20)
pie(valores, labels = categorias,
    main = "Resultados de los estudiantes",
    col = c("green", "red", "yellow"))

Barras apiladas: Muestra partes de un todo por categorias.Compara la composicion dentro de cada grupo.

ventas <- matrix(c(10, 15, 20, 8, 12, 18), nrow = 2, byrow = TRUE)
colnames(ventas) <- c("Ene", "Feb", "Mar")
rownames(ventas) <- c("Producto A", "Producto B")
barplot(ventas,
        beside = FALSE,
        main = "Ventas mensuales por producto",
        col = c("blue", "red"),
        legend = rownames(ventas))

Cargar una data de un sitio web

url <- 'https://raw.githubusercontent.com/fhernanb/datos/master/babies.txt'

Datosweb<- read.table(url, header=TRUE, sep='\t')
Datosweb
bwt gestation parity age height weight smoke
120 284 First born 27 62 100 Not
113 282 First born 33 64 135 Not
128 279 First born 28 64 115 Yes
123 NA First born 36 69 190 Not
108 282 First born 23 67 125 Yes
136 286 First born 25 62 93 Not
138 244 First born 33 62 178 Not
132 245 First born 23 65 140 Not
120 289 First born 25 62 125 Not
143 299 First born 30 66 136 Yes
140 351 First born 27 68 120 Not
144 282 First born 32 64 124 Yes
141 279 First born 23 63 128 Yes
110 281 First born 36 61 99 Yes
114 273 First born 30 63 154 Not
115 285 First born 38 63 130 Not
92 255 First born 25 65 125 Yes
115 261 First born 33 60 125 Yes
144 261 First born 33 68 170 Not
119 288 First born 43 66 142 Yes
105 270 First born 22 56 93 Not
115 274 First born 27 67 175 Yes
137 287 First born 25 66 145 Not
122 276 First born 30 68 182 Not
131 294 First born 23 65 122 Not
103 261 First born 27 65 112 Yes
146 280 First born 26 58 106 Not
114 266 First born 20 65 175 Yes
125 292 First born 32 65 125 Not
114 274 First born 28 66 132 Yes
122 270 First born 26 61 105 Not
93 278 First born 34 61 146 Not
130 268 First born 30 66 123 Not
119 275 First born 23 60 105 Not
113 281 First born 24 65 120 Not
134 283 First born 22 67 130 Not
107 279 First born 24 63 115 Not
134 288 First born 23 63 92 Yes
122 267 First born 27 65 101 Yes
128 282 First born 31 65 NA Not
129 293 First born 30 61 160 Not
110 278 First born 23 63 177 Not
138 302 First born 26 NA NA Yes
111 270 First born 27 61 119 Not
87 248 First born 37 65 130 Yes
143 274 First born 27 63 110 Yes
155 294 First born 32 66 150 Not
110 272 First born 25 60 90 Not
122 275 First born 26 66 147 Not
145 291 First born 26 63 119 Yes
115 258 First born 26 62 130 Not
108 283 First born 31 65 148 Yes
102 282 First born 28 61 110 Not
143 286 First born 31 64 126 Not
146 267 First born 30 67 132 Not
124 275 First born 22 60 130 Not
124 278 First born 26 70 145 Yes
145 257 First born 33 65 140 Not
106 273 First born 28 60 116 Not
75 232 First born 33 61 110 Not
107 273 First born 24 61 96 Not
124 288 First born 22 67 118 Not
122 280 First born 23 65 125 Yes
101 245 First born 23 63 130 Yes
128 283 First born 28 63 125 Yes
104 282 First born 36 65 115 Yes
97 246 First born 37 63 150 Not
137 274 First born 26 69 137 Yes
103 273 First born 31 63 170 Yes
142 276 First born 38 63 170 Not
130 289 First born 27 66 130 Not
156 292 First born 26 63 118 Not
133 284 First born 25 66 125 Yes
120 274 First born 24 62 120 Not
91 270 First born 24 60 149 Yes
127 274 First born 21 62 110 Not
153 286 First born 26 63 107 Yes
121 276 First born 39 63 130 Not
120 277 First born 27 63 126 Not
99 272 First born 27 62 103 Yes
149 293 First born 35 65 116 Not
129 280 First born 23 64 104 Not
139 292 First born 25 68 135 Not
114 274 First born 33 67 148 Yes
138 287 First born 30 66 145 Not
129 274 First born 29 71 NA Yes
138 294 First born 32 65 117 Not
131 296 First born 37 63 143 Not
125 305 First born 22 70 196 Yes
114 NA First born 24 67 113 Yes
128 281 First born 33 59 117 Not
134 268 First born 28 62 112 Not
114 271 First born 27 60 104 Not
92 NA First born 31 67 130 Not
85 278 First born 23 61 103 Yes
135 282 First born 22 64 100 Not
87 255 First born 28 61 100 Yes
125 302 First born 37 62 162 Not
128 NA First born 35 62 110 Not
105 254 First born 29 64 137 Not
120 279 First born 27 60 121 Yes
119 274 First born 33 64 120 Not
116 286 First born 24 61 NA Not
107 280 First born 36 65 117 Yes
119 273 First born 24 61 108 Yes
133 279 First born 37 66 140 Not
155 287 First born 33 66 143 Not
126 273 First born 22 65 150 Not
129 303 First born 27 64 125 Not
137 274 First born 29 65 154 Not
103 269 First born 26 65 NA Yes
125 302 First born 28 65 125 Not
91 255 First born 19 67 136 Yes
134 293 First born 21 65 NA Not
95 279 First born 22 66 145 Yes
118 276 First born 29 64 114 Not
141 278 First born 33 66 109 Yes
131 283 First born 25 67 215 Not
121 264 First born 32 66 145 Not
100 243 First born 39 65 170 Yes
131 288 First born 24 61 103 Not
118 284 First born 26 66 133 Not
152 288 First born 35 67 130 Not
121 284 First born 34 69 155 Not
117 276 First born 31 69 150 Not
115 283 First born 25 61 150 Yes
112 277 First born 23 65 110 Not
94 267 First born 30 62 120 Yes
109 272 First born 35 66 154 Not
132 225 First born 28 67 148 Not
117 278 First born 25 62 103 Not
101 266 First born 20 67 110 Yes
112 294 First born 25 64 125 Yes
128 283 First born 24 60 100 Not
128 279 First born 25 66 147 Yes
117 258 First born 31 64 120 Not
134 278 First born 24 69 135 Not
127 284 First born 28 65 145 Not
93 269 First born 21 65 104 Yes
122 275 First born 27 65 165 Not
100 265 First born 39 62 107 Yes
147 293 First born 32 65 123 Not
120 299 First born 25 65 110 Not
144 277 First born 30 63 127 Not
105 268 First born 32 61 115 Yes
136 276 First born 23 66 155 Not
102 262 First born 24 63 125 Not
160 300 First born 29 71 175 Yes
113 275 First born 24 68 140 Yes
126 282 First born 38 66 250 Not
126 271 First born 29 68 148 Not
115 278 First born 29 61 128 Not
127 336 First born 29 NA NA Not
119 284 First born 20 66 132 Not
129 NA First born 23 NA NA Yes
123 318 First born 21 64 152 Not
118 282 First born 22 68 135 Yes
133 287 First born 24 60 104 Yes
105 281 First born 39 61 NA Not
134 290 First born 22 60 121 Not
144 288 First born 21 67 111 Not
111 273 First born 43 62 138 Not
125 262 First born 36 66 190 Not
135 296 First born 30 63 123 Not
134 289 First born 22 63 125 Not
116 289 First born 22 65 160 Yes
129 291 First born 29 69 123 Not
113 301 First born 26 67 105 Yes
131 295 First born 23 65 123 Yes
126 293 First born 29 59 110 NA
121 272 First born 22 62 109 Not
121 271 First born 25 68 118 Yes
138 287 First born 24 65 115 Not
136 278 First born 23 61 105 Not
120 279 First born 30 66 131 Not
122 278 First born 31 72 155 Yes
134 267 First born 30 66 170 Not
101 280 First born 25 65 123 Yes
112 288 First born 32 62 125 Not
132 290 First born 25 64 120 Not
136 285 First born 23 62 175 Not
113 277 First born 23 65 192 Yes
96 271 First born 23 64 116 Not
124 277 First born 29 63 220 Not
113 306 First born 21 62 150 Not
131 286 First born 34 NA NA Yes
137 258 First born 25 63 117 Not
133 268 First born 24 61 93 Not
107 244 First born 20 58 97 Not
96 265 First born 28 59 135 Yes
142 278 First born 35 66 136 Yes
136 275 First born 22 63 110 Not
75 239 First born 26 63 124 Yes
125 302 First born 32 61 NA Yes
104 295 First born 26 65 155 Yes
130 274 First born 30 63 150 Not
90 290 First born 22 63 168 Not
118 276 First born 22 66 147 Yes
123 320 First born 22 66 117 Not
137 291 First born 34 61 110 Not
101 268 First born 19 63 140 Not
142 275 First born 25 64 132 Not
98 282 First born 20 63 97 Yes
124 283 First born 23 63 112 Not
151 310 First born 21 65 NA Not
109 281 First born 23 61 105 Not
150 285 First born 22 61 110 Yes
119 282 First born 26 68 150 Yes
131 280 First born 38 65 125 Not
101 272 First born 29 63 150 Yes
113 246 First born 19 62 138 Yes
127 270 First born 25 62 150 Not
97 260 First born 23 61 99 Yes
117 282 First born 28 64 115 Not
150 290 First born 21 65 125 Not
85 234 First born 33 67 130 Not
128 288 First born 27 70 145 Not
105 233 First born 34 61 130 Not
90 269 First born 26 67 125 NA
115 274 First born 22 65 130 Yes
107 290 First born 28 62 135 Not
121 275 First born 24 63 121 Yes
119 286 First born 20 64 180 Not
117 275 First born 20 64 145 Yes
134 264 First born 26 68 136 Not
117 288 First born 35 65 142 Not
115 268 First born 28 66 128 Not
110 254 First born 23 63 120 Yes
130 282 First born 21 62 106 Yes
140 274 First born 23 63 106 Yes
111 284 First born 22 NA NA Yes
93 249 First born 33 66 117 Not
154 292 First born 42 65 116 Yes
125 290 First born 19 64 127 Not
93 318 First born 31 66 135 Not
122 277 First born 33 63 135 Yes
129 267 First born 22 63 160 Not
126 276 First born 23 63 120 Not
85 274 First born 24 68 155 Not
173 293 First born 30 63 110 Not
144 329 First born 22 65 190 Yes
114 278 First born 25 65 140 Yes
111 NA First born 27 63 105 Yes
154 287 First born 27 65 125 Yes
150 274 First born 25 67 117 Yes
111 278 First born 21 62 125 Not
126 277 First born 32 66 128 Not
122 261 First born 28 65 124 Not
141 282 First born 24 68 169 Not
142 274 First born 24 63 125 Not
99 262 First born 38 59 110 Yes
113 286 First born 23 63 105 Not
149 282 First born 21 61 110 Not
117 328 First born 29 65 125 Yes
130 274 First born 26 64 185 NA
106 275 First born 31 65 142 NA
128 290 First born 22 64 118 Not
125 286 First born 21 64 139 Not
114 290 First born 30 66 160 Not
130 285 First born 23 63 128 Yes
116 148 First born 28 66 135 Not
81 256 First born 30 64 148 Yes
124 287 First born 27 62 105 Yes
125 292 First born 22 65 122 Not
110 262 First born 25 66 140 Not
125 279 First born 23 63 104 Yes
138 294 First born 40 64 125 Not
142 284 First born 39 66 132 Not
115 278 First born 23 60 102 Yes
102 280 First born 38 67 140 Not
140 294 First born 25 61 103 Not
133 276 Unknown 22 63 119 Not
127 290 First born 35 66 165 Not
104 274 Unknown 20 62 115 Yes
119 275 First born 42 67 156 Yes
152 301 First born 29 65 150 Not
123 284 Unknown 20 65 120 Yes
143 273 First born 19 66 135 Not
131 308 First born 40 65 160 Not
141 319 Unknown 20 67 140 Yes
129 277 First born 30 66 142 Yes
113 282 Unknown 36 59 140 Not
119 292 First born 33 62 118 Yes
109 295 Unknown 23 63 103 Yes
104 280 Unknown 27 68 146 Yes
131 282 Unknown 21 66 126 Not
110 293 Unknown 28 64 135 Yes
148 279 First born 27 71 189 Not
137 283 Unknown 20 65 157 Not
117 283 First born 27 63 108 Not
115 302 Unknown 22 67 135 Not
98 280 First born 35 64 122 Yes
136 303 Unknown 20 68 148 Yes
121 276 Unknown 23 71 152 Yes
132 285 Unknown 25 63 140 Not
91 264 First born 36 60 100 Yes
119 294 First born 34 59 105 Not
85 273 First born 26 60 105 Yes
106 271 Unknown 26 61 110 Yes
132 284 First born 29 64 122 Not
80 266 Unknown 25 62 125 Not
109 286 First born 24 64 125 Yes
111 306 First born 27 61 102 Not
143 292 Unknown 21 65 125 Not
136 290 First born 26 66 135 Not
110 285 Unknown 19 64 130 Not
98 257 First born 29 66 130 Yes
108 305 Unknown 24 65 112 Not
101 295 First born 18 62 145 Yes
71 281 First born 32 60 117 Yes
124 292 First born 29 68 176 Yes
93 256 First born 34 66 NA Yes
106 276 First born 30 66 130 Not
101 278 First born 25 62 112 Yes
100 277 First born 31 62 100 Yes
104 269 First born 35 63 110 Yes
117 270 First born 24 67 135 Yes
117 267 First born 29 65 120 Yes
149 279 First born 25 67 135 Not
135 284 First born 25 66 123 Not
110 283 Unknown 21 66 129 Not
121 276 First born 31 67 130 Not
142 285 Unknown 24 66 136 Not
104 260 First born 33 64 145 Not
138 296 First born 34 66 120 Not
112 278 Unknown 21 63 120 Not
117 293 First born 39 60 120 Yes
109 282 First born 25 62 106 Yes
131 266 Unknown 28 67 135 Not
120 273 First born 29 64 130 Yes
116 270 First born 29 63 132 Not
140 290 First born 23 65 110 Not
103 273 Unknown 22 64 110 Yes
120 279 Unknown 23 67 135 Not
139 260 Unknown 32 64 127 Not
123 254 First born 26 62 130 Yes
104 280 Unknown 23 64 107 Yes
131 283 First born 31 NA NA Not
111 270 First born 22 59 103 Not
122 277 First born 32 63 157 Yes
116 271 Unknown 30 67 144 Yes
129 277 First born 27 68 130 Yes
133 292 First born 30 65 112 Yes
110 277 First born 25 61 130 Not
105 276 First born 22 67 130 Not
93 246 First born 37 65 130 Not
122 281 First born 42 63 103 Yes
133 293 First born 23 64 110 Yes
130 296 Unknown 22 66 117 Yes
104 307 First born 24 59 122 Not
106 278 First born 31 65 110 Yes
120 281 First born 33 63 113 Not
121 284 First born 27 63 NA Yes
118 276 Unknown 18 63 128 Not
140 290 Unknown 19 67 132 Yes
114 268 First born 22 64 104 Not
116 280 First born 40 62 159 Not
129 284 First born 24 64 115 Not
120 286 First born 22 62 115 Yes
127 281 First born 24 63 112 Yes
107 278 Unknown 27 NA 135 Not
71 234 First born 32 64 110 Yes
88 274 First born 30 66 130 Not
107 300 First born 19 NA NA Yes
122 286 First born 23 64 145 Not
106 302 Unknown 19 66 147 Not
135 285 First born 30 66 130 Not
107 290 First born 26 63 112 Not
129 294 First born 32 62 170 Yes
126 274 First born 39 62 122 Not
116 293 Unknown 26 64 125 Not
124 294 First born 26 62 122 Not
123 281 First born 23 68 136 Not
145 315 First born 39 67 143 Yes
102 278 First born 27 67 135 Yes
129 293 First born 30 65 130 Yes
98 276 Unknown 22 61 121 Not
110 272 First born 28 60 108 Not
135 282 First born 24 67 128 Yes
101 278 Unknown 20 62 105 Not
96 266 First born 26 65 125 Not
104 276 Unknown 18 60 109 Yes
100 249 First born 24 67 100 Not
154 292 First born 40 66 145 Not
127 293 First born 31 67 137 Not
126 288 First born 31 62 150 Not
126 282 Unknown 23 66 115 Yes
127 279 First born 26 67 155 Yes
98 275 First born 25 65 112 Yes
127 288 Unknown 21 66 130 Not
129 299 First born 22 68 145 Not
131 292 Unknown 22 64 124 Yes
132 289 Unknown 19 66 145 Not
127 280 First born 27 62 118 Not
99 313 Unknown 34 59 100 Yes
115 290 First born 30 64 140 Yes
145 290 Unknown 24 67 125 Not
102 249 Unknown 23 67 134 Yes
136 299 First born 29 64 115 Not
121 286 Unknown NA NA NA Not
121 282 First born 22 66 133 Not
120 286 First born 25 62 105 Not
118 261 First born 26 60 104 Not
127 304 Unknown 26 62 105 Not
132 281 Unknown 24 63 117 Not
102 258 Unknown 22 65 135 Not
143 279 First born 39 65 129 Yes
118 277 First born 25 62 120 Not
102 286 Unknown 22 64 140 Not
163 280 First born 35 69 139 Not
132 294 First born 32 64 116 Not
116 276 First born 33 61 180 Not
138 288 Unknown 19 66 124 Not
139 279 First born 20 64 143 Not
132 298 Unknown 23 61 137 Not
87 282 First born 27 63 104 Yes
131 297 First born 30 67 132 Not
130 282 First born 26 67 147 Yes
123 290 First born 28 66 107 Yes
115 276 Unknown 18 63 110 Not
116 272 First born 27 64 130 Yes
119 286 Unknown 20 67 130 Not
125 279 Unknown 19 67 135 Not
144 282 First born 33 66 155 Yes
123 269 First born 26 67 132 Not
120 276 First born 23 66 114 Not
140 251 First born 28 63 210 Not
120 271 Unknown 17 64 142 Yes
116 272 First born NA 63 138 Yes
120 289 Unknown 31 59 102 Not
146 280 First born 23 61 145 Not
112 283 Unknown 21 62 102 Yes
115 269 First born 30 62 115 NA
132 278 First born 20 64 150 Yes
146 263 First born 39 53 110 Yes
122 275 First born 30 68 140 Not
128 292 First born 32 66 130 Not
119 277 First born 24 63 120 Yes
135 278 First born 27 66 148 Not
116 315 First born 26 NA NA Not
129 235 First born 24 66 135 Not
116 293 Unknown 28 62 108 Not
100 275 First born 27 64 111 Yes
118 280 First born 27 NA NA Yes
138 257 First born 38 67 138 Not
123 282 First born 22 65 130 Not
113 288 Unknown 21 61 120 Not
129 280 Unknown 24 65 140 Yes
122 280 First born 24 67 127 Yes
132 281 Unknown 21 67 140 Not
120 269 Unknown 40 63 130 Not
114 283 Unknown 20 65 115 Not
130 280 First born 29 66 135 Not
117 286 First born 32 66 127 Yes
142 285 First born 33 63 124 Not
144 273 First born 27 62 118 Yes
127 262 Unknown 32 64 125 Not
115 270 First born 25 67 165 Yes
85 258 First born 41 67 137 Not
99 274 First born 28 66 118 Yes
123 323 Unknown 17 64 140 Not
112 281 Unknown 23 61 150 Not
68 223 First born 32 66 149 Yes
102 283 Unknown 19 65 127 Yes
109 273 First born 37 65 138 Yes
102 267 Unknown 25 60 93 Yes
99 275 First born 23 61 125 Yes
78 256 Unknown 29 65 123 Not
128 284 Unknown 19 66 111 Yes
107 303 Unknown 25 67 133 Not
136 295 First born 23 64 147 Not
101 278 First born 27 61 99 Yes
100 275 Unknown 25 64 125 Not
109 272 First born 41 66 154 Yes
117 281 Unknown 21 70 141 Yes
88 252 Unknown 21 60 115 Yes
95 270 First born 35 65 135 Yes
119 280 Unknown 25 61 NA Yes
123 272 First born 28 NA NA Not
127 291 Unknown 24 66 135 Yes
107 293 First born 20 65 155 Yes
124 291 First born 26 66 NA Not
126 262 First born 37 66 135 Yes
98 278 First born 27 63 110 Yes
96 241 First born 23 64 130 Yes
104 282 First born 24 63 144 Not
133 273 Unknown 33 63 135 Not
93 267 First born 25 63 135 Yes
101 280 Unknown 24 65 123 Yes
118 277 First born 21 64 155 Not
130 289 First born 21 61 130 Yes
125 288 First born 22 63 128 Yes
140 291 Unknown 19 65 122 Not
115 290 Unknown 19 65 118 Not
130 293 First born 26 63 123 Not
114 277 Unknown 31 64 125 Not
105 278 First born 21 64 120 Not
101 289 Unknown 31 60 125 Not
132 286 First born 26 67 122 Yes
112 252 First born 37 64 162 Not
69 232 First born 31 59 103 Yes
114 264 First born 26 63 110 Yes
123 267 First born 29 63 111 Yes
129 284 Unknown 20 66 130 Yes
114 283 Unknown 15 64 117 Yes
115 290 First born 31 62 95 Not
98 272 Unknown 35 64 129 Not
128 283 First born 27 67 126 Not
119 279 Unknown 20 NA NA Yes
119 271 First born 28 64 175 Yes
154 288 First born 25 65 147 Not
127 247 Unknown 21 63 140 Not
131 263 First born 29 64 180 Yes
129 288 First born 28 59 102 Not
114 286 Unknown 22 64 116 Yes
110 280 First born 29 62 110 Yes
103 268 First born 31 64 150 Yes
117 287 First born 20 65 115 Yes
138 282 First born 25 64 120 Not
126 280 First born 24 66 147 Yes
124 271 First born 23 66 145 Not
111 284 First born 34 62 110 Not
132 282 First born 28 67 200 Yes
103 240 First born 26 65 140 Not
158 285 First born 28 62 130 Not
146 277 First born 32 NA NA Not
101 286 Unknown 21 64 117 Yes
132 290 First born 26 66 125 Not
114 293 Unknown 20 66 180 Yes
71 277 First born 40 69 135 Not
116 282 First born 19 64 120 Not
108 271 First born 19 60 109 Yes
123 298 First born 25 64 113 Yes
129 289 First born 37 63 132 Not
134 282 First born 24 62 110 Not
113 298 First born 30 60 124 Yes
123 277 Unknown 20 65 160 Not
147 277 First born 30 68 160 Not
121 270 Unknown 20 62 103 Not
125 284 Unknown 19 67 130 Not
115 277 Unknown 25 66 128 Not
101 289 First born 27 59 96 Not
93 271 First born 30 65 127 Yes
109 275 First born 33 66 120 Not
115 276 Unknown 23 60 106 Not
130 293 Unknown 23 65 122 Yes
123 278 First born 21 61 89 Not
111 300 First born 20 64 108 Yes
97 279 Unknown 24 64 138 Yes
122 292 Unknown 25 65 125 Not
124 300 First born 28 63 95 Not
129 276 First born 26 66 145 Not
124 290 First born 26 59 140 Not
107 280 First born 20 60 107 Yes
142 273 Unknown 22 62 125 Not
129 287 Unknown 29 66 135 Not
174 281 First born 37 67 155 Not
105 264 First born 30 65 105 Yes
103 291 Unknown 26 63 102 Not
124 285 Unknown 27 63 114 Not
105 265 First born 43 65 124 Not
133 275 First born 36 65 137 Yes
161 302 Unknown 22 70 170 Yes
105 260 First born 23 64 197 Not
108 281 First born 41 66 171 Not
153 297 First born 27 66 145 Not
133 280 Unknown 25 61 130 Not
115 269 First born 41 63 165 Yes
127 254 First born 27 67 146 Yes
128 271 First born 41 65 135 Yes
117 265 First born 40 68 134 Yes
123 274 First born 23 66 135 Not
119 288 Unknown 22 64 132 Yes
141 284 Unknown 17 64 105 Not
91 260 First born 26 62 110 Yes
116 291 First born 29 65 133 Yes
116 255 First born 24 65 132 Not
121 273 First born 32 64 112 Not
111 274 First born 36 67 159 Not
102 257 First born 25 66 135 Not
118 283 First born 24 65 150 Not
126 294 Unknown 22 65 125 Yes
98 286 First born 31 62 105 Yes
131 288 Unknown 28 65 125 Not
115 278 First born 21 60 113 Not
103 281 Unknown 22 59 98 Yes
147 301 First born 26 65 130 Not
123 308 Unknown 19 65 135 Not
125 283 First born 22 65 119 Not
117 270 First born 30 67 130 Yes
99 268 First born 29 71 150 Not
115 283 First born 31 66 127 Yes
116 265 First born 36 63 120 Not
118 297 First born 35 68 140 Yes
170 303 Unknown 21 64 129 Not
104 270 First born 25 61 110 Not
108 269 Unknown 20 62 114 Not
144 289 Unknown 17 69 130 Yes
99 250 Unknown 26 66 115 Not
97 263 Unknown 25 63 107 Not
142 284 First born 37 68 155 NA
85 270 Unknown 19 63 118 Yes
130 285 Unknown 24 66 126 Yes
117 275 First born 22 62 115 Yes
109 302 First born 24 64 110 Not
147 285 First born 24 64 137 Not
105 281 Unknown 23 64 115 Not
135 278 Unknown 27 68 139 Yes
115 273 Unknown 23 67 215 Yes
123 280 First born 23 65 140 Yes
105 274 Unknown 26 61 100 Not
154 271 First born 36 69 160 Yes
110 276 First born 25 63 107 Yes
119 285 Unknown 26 62 108 Not
103 292 Unknown 28 62 132 Not
117 272 First born 25 64 116 Not
120 289 First born 23 69 165 Not
145 278 First born 24 62 109 Not
104 271 First born 20 62 98 Yes
123 268 Unknown 18 62 110 Yes
124 272 First born 27 62 110 Not
129 275 First born 26 64 115 Yes
91 248 First born 33 63 202 Not
109 295 First born 32 61 135 Not
108 268 First born 22 58 112 Yes
79 268 First born 36 61 108 Not
133 301 First born 23 62 108 Not
114 309 Unknown 27 62 118 Not
128 273 First born 34 61 125 Not
129 280 Unknown 24 65 126 Not
97 234 Unknown 26 65 112 Not
103 276 Unknown 21 62 130 Yes
176 293 Unknown 19 68 180 Not
143 294 First born 44 65 145 Not
127 292 Unknown 21 68 130 Yes
107 256 First born 28 59 90 Yes
113 268 First born 31 62 100 Not
106 279 Unknown 21 62 118 Yes
152 285 First born 24 61 120 Yes
150 275 First born 29 65 145 Not
136 278 First born 35 64 118 Yes
151 298 First born 37 64 135 NA
124 279 First born 35 66 129 Not
123 284 Unknown 18 64 112 Yes
119 288 First born 37 62 128 Not
122 291 First born 40 64 155 Not
112 250 First born 34 67 124 Not
93 270 First born 25 64 125 Yes
109 271 First born 27 61 NA Yes
136 274 Unknown 20 63 165 Not
121 NA First born 31 68 132 Not
150 292 First born 26 64 124 Not
94 264 Unknown 26 64 135 Not
120 280 First born 29 NA NA Yes
146 306 First born 38 63 112 Not
129 274 First born 19 65 101 Yes
125 292 First born 27 65 117 Yes
124 273 First born 21 63 115 Not
141 282 First born 27 63 115 Not
96 266 First born 33 67 135 Yes
138 297 First born 30 66 133 Yes
127 282 First born 28 67 134 Not
114 251 First born 26 64 119 Yes
103 297 First born 31 64 125 Not
127 288 Unknown 20 65 115 Yes
141 292 First born 29 62 110 NA
113 274 First born 23 63 108 Yes
99 249 Unknown 31 57 98 Yes
97 279 First born 33 61 105 Yes
116 275 Unknown 20 68 145 Not
126 297 First born 26 66 120 Yes
158 296 First born 28 66 140 NA
119 277 First born 28 66 130 Yes
123 283 First born 27 62 110 Not
129 287 First born 24 60 107 Not
117 256 First born 37 65 132 Yes
100 275 First born 26 60 115 Not
131 274 First born 28 64 118 Yes
146 279 First born 27 64 124 Not
84 267 First born 29 60 95 Not
115 302 First born 28 64 116 Not
115 281 First born 25 60 94 Not
118 284 First born 28 70 145 Yes
91 292 Unknown 19 61 125 Not
112 255 First born 39 60 115 Not
115 316 Unknown 29 64 110 Not
110 269 First born 38 61 102 Not
117 277 First born 34 66 140 Not
109 268 Unknown 29 65 120 Yes
99 267 First born 22 62 94 Not
131 274 First born 27 62 160 Yes
136 291 Unknown 25 61 105 Not
130 298 First born 20 62 120 Not
134 296 First born 35 60 117 Yes
128 271 First born 29 65 126 Yes
150 286 First born 38 67 175 Not
86 284 First born 39 65 174 Yes
115 278 First born 26 63 112 Yes
141 281 First born 28 NA NA Yes
78 237 Unknown 23 63 144 Not
100 295 Unknown 21 68 125 Yes
116 270 First born 25 68 169 Not
110 271 Unknown 26 66 135 Not
109 283 First born 34 64 120 Not
113 259 First born 38 64 128 Not
136 297 Unknown 23 66 135 Not
114 NA First born 23 63 116 Yes
121 273 Unknown 34 61 125 Not
117 288 Unknown 28 63 140 Not
166 299 First born 26 68 140 Not
87 229 First born 27 62 138 Not
120 294 Unknown 23 66 128 Yes
95 286 First born 26 66 118 Yes
132 273 First born 28 62 113 Not
90 286 First born 32 63 105 Yes
131 308 First born 30 58 150 Yes
103 279 Unknown 22 65 145 Yes
144 287 Unknown 33 71 153 Yes
137 299 First born 24 62 115 Not
124 270 First born 20 64 122 Not
136 281 Unknown 27 64 127 Not
117 298 Unknown 22 64 160 Not
121 269 First born 23 62 130 Not
116 280 First born 34 68 198 Not
139 275 First born 33 62 118 Not
110 280 First born 39 67 125 Not
86 242 First born 20 64 110 Yes
133 287 First born 20 65 165 Not
81 254 First born 23 62 157 Not
133 281 First born 33 63 120 Not
132 284 Unknown 20 66 140 Not
132 287 First born 29 64 148 Not
137 274 First born 27 64 126 Not
84 279 First born 34 63 190 Not
136 279 First born 30 69 130 Yes
92 270 First born 34 62 100 Yes
114 298 Unknown 28 67 114 Not
129 274 First born 33 69 136 Yes
167 288 Unknown 19 63 117 Not
71 NA First born 19 64 120 Not
124 282 Unknown 22 65 118 Not
105 269 First born 27 62 100 Yes
155 283 Unknown 19 70 137 Not
125 279 Unknown 21 66 126 Not
125 266 First born 21 62 120 Yes
125 283 Unknown 22 59 96 Not
115 315 Unknown 22 62 110 Not
174 288 First born 25 61 182 Not
127 290 First born 35 66 122 Not
113 262 First born 24 60 105 Not
115 273 First born 22 66 130 Yes
139 277 First born 35 63 140 Not
127 275 First born 26 62 125 Not
111 289 First born 26 NA NA Yes
112 272 First born 26 60 98 Not
143 285 First born 30 64 135 Yes
116 286 Unknown 22 58 105 Yes
155 279 First born 33 61 125 Not
121 290 First born 31 64 127 Not
110 282 Unknown 21 66 125 Yes
87 277 First born 31 62 120 Yes
132 330 First born 34 64 130 Yes
105 261 First born 32 NA NA Yes
129 277 First born 24 68 142 Not
123 280 First born 20 62 105 Yes
91 279 Unknown 27 62 118 Not
147 286 First born 30 68 147 Not
144 289 First born 20 62 106 Not
128 292 First born 30 64 127 Not
137 318 Unknown 19 64 110 Not
104 289 First born 24 60 104 Yes
120 271 First born 32 63 130 Not
112 277 Unknown 23 64 118 Not
138 286 First born 26 63 111 Not
96 280 First born 27 63 105 Yes
134 285 First born 35 62 134 Not
126 285 First born 24 64 140 Not
112 300 First born 29 62 121 Not
138 313 Unknown 27 65 111 Not
110 275 First born 25 63 120 Not
83 253 First born 29 63 110 Yes
112 288 Unknown 20 62 110 Not
148 286 First born 38 68 160 Not
119 300 Unknown 34 63 124 Not
86 246 First born 25 64 113 Yes
110 269 First born 38 63 145 Yes
126 282 First born 23 61 120 Not
125 272 First born 30 60 96 Not
136 252 First born 27 63 130 Not
127 283 Unknown 29 64 119 Not
84 272 First born 25 64 150 Yes
131 278 First born 22 66 124 Not
123 286 Unknown 21 67 130 Yes
96 282 Unknown 30 68 127 Yes
110 286 First born 26 62 100 Not
123 282 First born 29 68 164 Not
152 286 Unknown 19 67 135 Not
127 288 First born 28 65 155 Not
117 269 Unknown 21 64 149 Yes
125 277 First born 29 66 139 Yes
139 273 First born 29 68 130 Not
114 280 First born 31 66 134 Yes
96 280 Unknown 34 62 127 Yes
124 289 First born 29 63 110 Not
107 272 First born 30 64 140 Yes
113 277 First born 38 64 108 Not
98 292 Unknown 20 65 124 Yes
119 285 Unknown 28 65 127 Not
107 268 First born 37 58 112 Yes
117 255 First born 26 61 120 Not
117 305 First born 24 64 155 Not
144 276 First born 23 67 129 Yes
136 268 First born 30 63 132 Yes
121 278 First born 28 69 132 Not
165 282 First born 29 66 145 Not
120 279 First born 38 64 124 Not
125 280 First born 30 65 130 Yes
137 285 First born 29 65 110 Not
100 288 Unknown 28 61 108 Yes
134 284 First born 28 62 112 Not
88 262 First born 20 65 118 Yes
108 291 First born 39 65 135 Not
123 271 First born 41 64 162 Not
141 277 First born 38 66 162 Not
130 270 Unknown 19 66 130 Not
139 299 Unknown 20 67 112 Not
130 283 First born 32 65 118 Not
113 289 Unknown 26 59 91 Not
77 238 Unknown 23 63 103 Yes
62 228 First born 24 61 107 Not
93 245 First born 33 61 100 Yes
109 275 Unknown 37 63 112 Yes
145 283 First born 27 65 125 Yes
92 224 First born 19 63 134 Yes
120 281 First born 26 61 115 Not
135 284 First born 39 67 141 Not
113 287 First born 36 63 118 Not
126 251 Unknown 28 64 123 Not
143 270 Unknown 27 70 148 Not
128 282 Unknown 25 64 125 Not
98 262 First born 22 67 120 Not
110 306 Unknown 32 61 122 Not
162 284 First born 27 64 126 Not
116 292 Unknown 20 65 118 Not
128 284 First born 23 62 110 Not
111 275 Unknown 18 61 108 Yes
137 280 First born 34 60 107 Not
134 278 First born 28 NA 126 Yes
100 264 First born 29 64 120 Yes
160 271 First born 32 67 215 Not
112 267 Unknown 22 62 138 Not
134 297 First born 27 67 170 Yes
145 308 First born 35 64 110 Yes
116 295 First born 32 65 120 Not
126 278 First born 26 64 150 Yes
111 285 First born 29 65 130 Not
126 282 First born 33 62 117 Not
109 291 First born 39 64 107 Not
136 291 First born 41 66 191 Not
119 286 First born 22 63 185 Yes
103 267 Unknown 21 66 150 Yes
124 284 Unknown 17 62 112 Not
155 286 First born 31 66 127 Not
122 282 Unknown 21 66 110 Not
113 285 First born 26 66 140 Not
122 273 First born 26 66 210 Not
126 293 Unknown 27 62 111 Not
116 277 First born 41 64 124 Yes
102 294 First born 21 65 130 Yes
110 181 First born 27 64 133 Not
133 285 Unknown 30 64 160 Not
125 283 First born 29 65 125 Not
164 286 Unknown 32 66 143 Not
133 297 First born 36 61 125 Not
135 300 First born 25 64 NA Not
124 293 Unknown 19 65 150 Not
122 306 Unknown 22 62 100 Not
121 271 Unknown 34 63 129 Yes
100 272 First born 30 64 150 Yes
129 NA Unknown 19 61 110 Not
90 266 Unknown 26 67 135 Not
128 272 Unknown 18 67 109 Not
116 280 Unknown 22 59 NA Yes
86 276 Unknown 23 65 125 Yes
123 282 First born 30 63 118 Not
87 275 First born 28 63 110 Yes
128 291 Unknown 27 63 132 Not
120 288 First born 28 63 125 Not
125 301 Unknown 35 68 181 Not
118 265 First born 27 61 123 Not
116 284 Unknown 24 66 117 Not
131 262 First born 22 67 135 Not
151 286 Unknown 22 66 130 Not
88 273 First born 20 66 110 Yes
137 284 First born 30 67 110 Not
127 289 First born 23 67 140 Not
96 278 Unknown 18 60 120 Yes
129 281 First born 31 67 155 Not
128 288 Unknown 26 65 114 Not
85 255 First born 24 68 159 Not
111 281 Unknown 27 64 112 Not
124 275 First born 28 61 116 Not
112 292 Unknown 28 62 110 Yes
115 281 First born 28 61 128 Yes
72 271 First born 39 61 136 Not
122 281 Unknown 24 65 137 Yes
116 291 First born 26 66 153 Not
127 272 First born 20 64 130 Yes
90 266 First born 23 61 99 Yes
99 273 Unknown 27 59 115 Not
144 307 Unknown 26 66 125 Not
138 280 Unknown 30 65 175 Not
58 245 First born 34 64 156 Yes
109 265 Unknown 24 63 107 Yes
110 277 Unknown 19 62 160 Not
129 278 First born 27 63 128 Not
150 284 First born 40 67 130 Not
128 279 First born 27 66 135 Not
142 284 Unknown 31 66 137 Yes
115 268 Unknown 31 64 125 Not
108 274 First born 28 66 175 NA
108 283 First born 35 62 108 Not
139 281 First born 27 63 137 Not
115 275 First born 25 61 155 Yes
136 288 First born 23 62 217 Not
163 289 Unknown 25 64 126 Yes
131 285 First born 26 64 130 Not
77 238 First born 38 67 135 Yes
124 283 Unknown 33 67 156 Yes
104 270 Unknown 26 62 115 Not
102 267 Unknown 24 61 109 Yes
94 268 First born 30 62 105 Yes
158 295 Unknown 37 70 137 Not
112 275 Unknown 21 68 143 Yes
119 286 First born 26 64 123 Yes
97 279 First born 29 68 178 Yes
99 252 First born 21 64 120 Not
115 264 Unknown 23 67 134 Yes
139 284 First born 37 61 121 Not
144 304 Unknown 27 58 102 Yes
99 270 First born 22 63 115 Yes
105 280 Unknown 22 63 116 Not
89 275 First born 34 66 170 Not
129 270 First born 43 67 160 Not
119 270 Unknown 20 64 109 Not
114 291 First born 35 60 112 Not
106 289 First born 28 67 120 Yes
122 292 Unknown 34 65 133 Not
136 261 First born 24 65 110 Not
121 286 Unknown 22 69 130 Yes
112 282 First born 26 65 122 Not
112 266 First born 26 64 122 Not
123 314 First born 22 61 121 Yes
139 286 First born 33 65 125 Yes
125 290 First born 36 59 105 Not
105 295 Unknown 20 64 112 Yes
130 276 First born 41 68 130 Not
146 294 First born 22 66 145 Yes
133 290 First born 21 64 145 Not
147 296 Unknown 19 67 124 Not
109 269 First born 23 63 113 Not
122 286 First born 23 64 120 Yes
135 260 First born 43 65 135 Not
107 NA First born 19 60 118 Not
117 272 First born 32 66 118 Not
138 284 First born 30 66 133 Yes
120 283 First born 28 64 122 Yes
119 273 First born 35 65 125 Yes
118 278 Unknown 19 62 126 Not
105 330 First born 23 64 112 Yes
113 306 Unknown 21 65 137 Not
136 NA First born 36 66 135 Not
148 291 Unknown 21 63 115 Not
140 281 Unknown 22 69 135 Not
134 287 Unknown 33 67 131 Not
120 280 First born 31 61 111 Not
123 296 Unknown 26 64 110 Yes
102 275 First born 43 64 160 Not
55 204 First born 35 65 140 Not
103 276 Unknown 19 63 149 Yes
123 283 First born 21 65 110 Not
105 270 Unknown 27 65 134 Yes
138 289 First born 33 65 155 Not
128 281 First born 28 63 150 Not
139 285 First born 30 65 129 Yes
104 288 Unknown 27 61 122 Yes
159 296 Unknown 27 64 112 Not
118 276 First born 29 62 130 Yes
99 285 First born 25 69 128 Yes
144 281 First born 20 63 120 Not
121 270 First born 25 62 108 Yes
117 265 Unknown 24 66 98 Not
119 293 Unknown 23 65 127 Not
105 281 Unknown 19 61 130 Not
125 283 First born 37 63 145 Yes
119 259 First born 37 62 130 Not
101 273 First born 39 60 113 Not
105 277 Unknown 25 64 156 Not
110 281 First born 27 60 110 Not
100 270 Unknown 21 65 132 Yes
98 284 First born 29 68 140 Not
127 276 First born 37 64 159 Not
117 324 First born 22 62 164 Yes
122 278 First born 37 68 114 Not
122 273 Unknown 23 64 130 Yes
118 281 Unknown 36 66 140 Yes
137 303 Unknown 23 66 127 Yes
120 275 First born 32 63 115 Yes
143 285 First born 27 68 185 Not
108 270 First born 29 67 124 Yes
131 284 Unknown 19 61 114 Yes
110 277 First born 36 61 116 Not
105 276 First born 20 62 112 Yes
133 274 First born 30 63 NA Not
125 255 First born 23 63 133 Not
78 258 Unknown 24 66 115 Yes
114 289 First born 36 60 115 Not
111 278 First born 29 65 145 Yes
103 250 First born 40 59 140 Not
114 276 First born 26 62 127 Not
75 247 First born 36 64 120 Yes
169 296 First born 33 67 185 Not
94 271 First born 36 61 130 Yes
150 287 First born 36 62 135 Not
144 248 First born 30 70 145 Not
144 291 First born 28 67 130 Not
143 313 First born 20 68 150 Not
145 304 Unknown 25 63 109 Yes
121 285 First born 34 64 110 Not
105 256 First born 31 66 142 Not
134 286 First born 25 64 125 Not
129 294 Unknown 21 65 132 Not
114 276 First born 24 63 110 Not
97 265 First born 30 61 110 Not
160 292 First born 28 64 120 Not
65 237 First born 31 67 130 Not
145 288 First born 28 64 116 Not
95 273 First born 23 60 90 Not
139 293 Unknown 21 69 130 Not
123 288 First born 27 63 125 Not
109 283 First born 23 65 112 Yes
110 268 First born 34 64 127 Not
122 296 Unknown 24 65 132 Not
115 307 First born 34 65 128 Yes
117 323 First born 26 62 NA Not
108 279 Unknown 19 64 115 Not
120 287 First born 23 67 116 Yes
131 269 First born 36 68 145 Not
136 283 Unknown 24 63 119 Not
125 290 First born 32 63 135 Not
96 285 Unknown 20 66 117 Yes
102 282 Unknown 29 65 125 Yes
102 288 Unknown 18 65 117 Not
112 277 Unknown 22 67 120 Not
135 272 First born 30 65 130 Not
91 266 First born 23 60 120 Yes
129 276 First born 31 63 125 Not
155 290 First born 26 66 129 Yes
109 274 First born 33 69 144 Yes
80 262 Unknown 31 61 100 Yes
125 273 First born 30 64 145 Not
94 284 First born 24 63 104 Yes
148 281 First born 27 63 110 Yes
73 277 First born 29 65 145 Not
123 267 Unknown 19 66 132 Yes
65 232 First born 24 66 125 Yes
118 279 Unknown 21 64 108 Not
102 283 First born 39 60 119 Not
120 280 First born 24 61 118 Not
108 270 Unknown 21 65 130 Yes
122 280 Unknown 45 62 128 Not
103 268 First born 32 62 97 Yes
105 312 First born 41 61 115 Yes
126 273 Unknown 25 68 135 Not
145 316 First born 22 67 142 Not
139 293 First born 34 66 131 Not
124 290 First born 26 65 165 Not
121 282 First born 30 65 122 Not
126 299 Unknown 21 60 114 Not
119 286 Unknown 33 67 137 Not
114 277 Unknown 19 63 107 Not
118 272 First born 23 64 113 Not
127 295 First born 36 65 145 Not
117 290 Unknown 22 67 110 Not
137 277 First born 41 65 126 Not
133 292 First born 29 65 135 Not
100 264 First born 28 60 111 Yes
107 273 Unknown 26 65 135 Not
115 276 Unknown 20 62 105 Yes
91 292 Unknown 26 61 113 Yes
112 287 First born 27 64 110 Yes
125 289 Unknown 31 61 120 Not
157 291 First born 33 65 121 Not
108 256 Unknown 26 67 130 Not
130 279 First born 31 62 122 Not
135 289 First born 25 64 127 Not
123 277 First born 24 66 122 Not
100 281 First born 24 61 115 Not
124 277 Unknown 23 64 104 Not
174 284 First born 39 65 163 Not
129 278 First born 26 67 146 Not
119 275 First born 27 59 113 Yes
126 272 Unknown 35 61 120 Yes
128 267 First born 37 61 142 Not
116 282 Unknown 19 64 124 Not
100 285 First born 18 68 127 Yes
96 285 First born 37 66 135 Yes
131 279 Unknown 20 68 122 Yes
110 292 First born 35 62 127 Not
108 278 First born 28 63 125 Yes
129 275 First born 24 65 135 Not
141 285 First born 23 67 150 Not
110 276 First born 31 70 155 Not
118 273 First born 21 63 120 Not
111 267 Unknown 24 60 115 Not
160 297 First born 20 68 136 Not
120 280 First born 30 60 115 Not
121 281 First born 29 63 108 Not
113 282 First born 30 64 118 Yes
117 270 First born 23 58 115 Not
158 267 First born 35 64 125 Not
128 277 First born 39 61 120 Not
158 289 First born 30 66 140 Not
133 289 First born 22 65 123 Yes
163 298 First born 37 61 98 Not
128 282 Unknown 19 66 118 Not
126 271 Unknown 21 60 105 Not
127 283 First born 42 62 154 Yes
134 287 First born 40 63 118 Not
140 274 First born 41 63 122 Not
102 285 First born 29 63 117 Yes
100 252 First born 24 61 150 Not
120 295 First born 29 59 100 Yes
98 279 Unknown 18 65 115 Yes
130 246 First born 19 62 118 Not
104 280 First born 41 63 118 Yes
122 285 First born 31 62 102 Yes
137 276 Unknown 25 64 127 Not
114 285 Unknown 20 61 104 Not
63 236 Unknown 24 58 99 Not
98 318 First born 23 63 107 Not
99 268 First born 32 63 124 Yes
89 238 Unknown 26 64 136 Not
117 283 First born 22 65 142 Yes
143 281 First born 29 67 132 Not
106 279 First born 29 63 125 Yes
99 246 First born 35 62 106 Not
156 300 First born 27 65 120 Yes
72 266 Unknown 25 66 200 Yes
75 266 First born 37 61 113 Yes
97 285 First born 35 61 112 Yes
106 264 First born 41 64 114 Not
91 225 First born 18 68 117 Yes
117 269 Unknown 28 61 99 Not
117 284 First born 25 66 177 Yes
112 291 First born 23 66 145 Not
112 270 First born 29 61 124 Not
141 293 First born 28 61 125 Not
131 259 First born 19 63 134 Not
130 290 First born 19 65 123 Yes
132 270 First born 26 67 140 Not
114 265 First born 23 67 130 Yes
160 291 First born 34 64 110 Yes
106 283 First born 24 63 119 Not
84 260 Unknown 20 64 104 Yes
112 268 Unknown 25 59 103 Not
139 311 First born 37 66 135 Not
104 267 First born 30 63 180 Not
130 294 First born 32 63 110 Yes
71 254 First born 19 61 145 Yes
82 270 First born 21 65 150 Yes
119 280 Unknown 21 64 128 Not
123 353 First born 26 63 115 Not
115 278 First born 27 59 95 Not
124 289 Unknown 21 67 145 Yes
138 292 First born 25 65 130 Yes
88 276 First born 25 63 103 Yes
146 305 First born 23 NA NA Not
128 241 Unknown 17 64 126 Not
82 274 First born 31 64 101 Yes
100 274 First born 24 63 113 Not
114 271 First born 32 61 130 Not
97 269 First born 20 65 137 Yes
126 298 First born 24 61 112 Not
122 275 Unknown 20 65 127 Not
152 295 First born 39 62 140 Not
116 274 First born 21 62 110 Yes
132 302 First born 36 63 145 Yes
84 260 Unknown 37 66 140 Not
119 277 Unknown 18 61 89 Yes
104 275 First born 24 NA NA Not
106 312 First born 24 62 135 Yes
124 NA Unknown 39 65 228 Not
139 291 First born 24 65 160 Not
103 273 First born 36 65 158 Yes
112 299 First born 24 67 145 Yes
96 276 First born 33 64 127 Yes
102 281 Unknown 19 67 135 Yes
120 300 First born 34 63 150 Yes
102 338 First born 19 64 170 Not
97 255 Unknown 22 63 107 Yes
113 285 First born 22 70 145 Not
130 297 First born 32 58 130 Not
97 260 Unknown 25 63 115 Yes
116 273 First born 31 61 120 Not
114 266 First born 29 64 113 Not
127 242 First born 17 61 135 Yes
87 247 Unknown 18 66 125 Yes
141 281 First born 29 54 156 Yes
144 283 Unknown 25 66 140 Not
116 273 First born 33 66 130 Yes
75 265 First born 21 65 103 Yes
138 286 Unknown 28 68 120 Not
99 271 First born 39 69 151 Not
118 293 First born 21 63 103 Not
152 267 First born 28 NA 119 Yes
97 266 First born 24 62 109 Not
146 319 First born 28 66 145 Not
81 285 First born 19 63 150 Yes
110 321 First born 28 66 180 Not
135 284 Unknown 19 60 95 Not
114 290 Unknown 21 65 120 Yes
124 288 Unknown 21 64 116 Yes
115 262 Unknown 23 64 136 Yes
143 281 First born 28 65 135 Yes
113 287 Unknown 29 70 145 Yes
109 244 Unknown 21 63 102 Yes
103 278 First born 30 60 87 Yes
118 276 First born 34 64 116 Not
127 290 First born 27 65 121 Not
132 270 First born 27 65 126 Not
113 275 Unknown 27 60 100 Not
128 265 First born 24 67 120 Not
130 291 First born 30 65 150 Yes
125 281 Unknown 21 65 110 Not
117 297 First born 38 65 129 Not 
summary(Datosweb)
##       bwt          gestation        parity               age       
##  Min.   : 55.0   Min.   :148.0   Length:1236        Min.   :15.00  
##  1st Qu.:108.8   1st Qu.:272.0   Class :character   1st Qu.:23.00  
##  Median :120.0   Median :280.0   Mode  :character   Median :26.00  
##  Mean   :119.6   Mean   :279.3                      Mean   :27.26  
##  3rd Qu.:131.0   3rd Qu.:288.0                      3rd Qu.:31.00  
##  Max.   :176.0   Max.   :353.0                      Max.   :45.00  
##                  NA's   :13                         NA's   :2      
##      height          weight         smoke          
##  Min.   :53.00   Min.   : 87.0   Length:1236       
##  1st Qu.:62.00   1st Qu.:114.8   Class :character  
##  Median :64.00   Median :125.0   Mode  :character  
##  Mean   :64.05   Mean   :128.6                     
##  3rd Qu.:66.00   3rd Qu.:139.0                     
##  Max.   :72.00   Max.   :250.0                     
##  NA's   :22      NA's   :36
str(Datosweb)  #Descripcion de la data
## 'data.frame':    1236 obs. of  7 variables:
##  $ bwt      : int  120 113 128 123 108 136 138 132 120 143 ...
##  $ gestation: int  284 282 279 NA 282 286 244 245 289 299 ...
##  $ parity   : chr  "First born" "First born" "First born" "First born" ...
##  $ age      : int  27 33 28 36 23 25 33 23 25 30 ...
##  $ height   : int  62 64 64 69 67 62 62 65 62 66 ...
##  $ weight   : int  100 135 115 190 125 93 178 140 125 136 ...
##  $ smoke    : chr  "Not" "Not" "Yes" "Not" ...

bwt: Peso del bebé al nacer, redondeado a la onza más cercana.

Gestación: Duración del embarazo en días, calculado a partir del primer día del último período menstrual normal.

Paridad: Indicador de si el bebé es el primogénito o desconocido.

Edad: edad de la madre en el momento de la concepción, en años.

Esta linea se usa para saber si la mujer 68 fuma.

Datosweb$smoke[68]
## [1] "Yes"
hist(Datosweb$gestation)

barplot(Datosweb$gestation)

Esta linea nos sirve para mostrar dos graficos juntos.

# Crear datos de ejemplo
categorias <- c("A", "B", "C", "D")
frecuencia1 <- c(5, 3, 7, 2)
frecuencia2 <- c(4, 6, 1, 8)

# Configurar la cuadrícula 1 fila, 2 columnas
par(mfrow = c(2, 2))  # 1 fila, 2 columnas de gráficos

# Primer gráfico de barras
barplot(frecuencia1,
        names.arg = categorias,
        col = "skyblue",
        main = "Gráfico 1: Grupo A",
        ylab = "Frecuencia")
# Segundo gráfico de barras
barplot(frecuencia2,
        names.arg = categorias,
        col = "salmon",
        main = "Gráfico 2: Grupo B",
        ylab = "Frecuencia")

# Restaurar parámetro gráfico (opcional, si quieres seguir con un solo gráfico después)

par(mfrow = c(1, 1))

boxplot(Datosweb$gestation)

barplot(Datosweb$gestation)

Temario corte 3

Estadística inferencial

Es una parte de la estadística que comprende los métodos y procedimientos que por medio de la inducción determina propiedades de una población estadística, a partir de una parte de esta. Su objetivo es obtener conclusiones útiles para hacer razonamientos deductivos sobre una totalidad, basándose en la información numérica dada por la muestra.

Correlacion: se refiere a la relación estadística entre dos variables, indicando si cambian juntas y en qué dirección. Puede ser positiva, negativa o nula.

H_O - nula: La variable presenta una distribucion normal H_A -alterna: La variable no presenta una distrbucion normal.

Si el p-valor es > 0.05 no se rechaza, o sea que se acepta la hipotesis nula, la distribucion es normal y si p-valor es < que 0.05 se rechaza la hipotesis nula, la distribucion no es normal.

Coeficiente de pearson: Mide la fuerza y dirección de la relación lineal entre dos variables, con valores entre -1 y 1.

Coeficiente de kendal: Es una medida no paramétrica que evalúa la asociación (o concordancia) entre dos variables ordinales o continuas.

Coeficiente de spearman: Es una medida no paramétrica que evalúa la relación monótona (no necesariamente lineal) entre dos variables continuas u ordinales. Se basa en los rangos de los datos en lugar de sus valores brutos, lo que lo hace robusto frente a outliers y adecuado para distribuciones no normales.

poblacion: Conjunto total de individuos u observaciones de interes.

Muestra: Subconjunto representativo extraido de una poblacion.

parametro: Valor numerico que describe una caracteristica.

Estadistico: Valor calculado a partir de los datos muestrales.

Error de muestreo: Diferencia entre el estadistico de la muestra y el parametro real.

Nivel de significancia: Probabildad de rechazar la hipotesis nula cuando esta es verdadera.

Distribucion muestral:Distribucon de un estadistico basado en multiples muestras.

Prueba de hopotesis

Pruebas parametricas, cando los datos son normales Pruebas no parametricas, cuando los datos no son normales.

prueba t student de una muestra: para comparar medias.

x<-c(48, 52, 49, 51, 53, 47)
t.test(x, mu=50)
## 
##  One Sample t-test
## 
## data:  x
## t = 0, df = 5, p-value = 1
## alternative hypothesis: true mean is not equal to 50
## 95 percent confidence interval:
##  47.51658 52.48342
## sample estimates:
## mean of x 
##        50

EJEMPLO

#Describir la data, en la pestaña help aparece la descripcion. 

Data<-(mtcars)
print(Data)
##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
head(mtcars)
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 
#Hallar grafico de dispersion para saber si presenta distribucion normal o no 

Data<-(mtcars)
plot(mtcars$mpg)

plot(mtcars$cyl)

#Prueba de normalidad 

shapiro.test(mtcars$mpg) #para datos menores que 50
## 
##  Shapiro-Wilk normality test
## 
## data:  mtcars$mpg
## W = 0.94756, p-value = 0.1229
#correlacion 

cor.test(mtcars$mpg, mtcars$cyl, Data=mtcars, method="pearson")
## 
##  Pearson's product-moment correlation
## 
## data:  mtcars$mpg and mtcars$cyl
## t = -8.9197, df = 30, p-value = 6.113e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.9257694 -0.7163171
## sample estimates:
##       cor 
## -0.852162
#prueba

fisher.test(mtcars$mpg, mtcars$cyl)
## 
##  Fisher's Exact Test for Count Data
## 
## data:  mtcars$mpg and mtcars$cyl
## p-value = 0.01327
## alternative hypothesis: two.sided