#Tema1
url.eph.2020 = "https://www.ine.gov.py/datos/encuestas/eph/Poblacion/EPH-2020/data/55f07reg02_ephc2020.csv"
data.eph.2020 = read.csv(url.eph.2020,sep = ";",header = T)# Visualizamos los nombres de las variables en data.eph.2020
names(data.eph.2020)## [1] "UPM" "NVIVI"
## [3] "NHOGA" "DPTOREP"
## [5] "AREA" "L02"
## [7] "P02" "P03"
## [9] "P04" "P04A"
## [11] "P04B" "P05C"
## [13] "P05P" "P05M"
## [15] "P06" "P08D"
## [17] "P08M" "P08A"
## [19] "P09" "P10A"
## [21] "P10AB" "P10Z"
## [23] "P11A" "P11AB"
## [25] "P11Z" "P12"
## [27] "A01" "A01A"
## [29] "A02" "A03"
## [31] "A04" "A04B"
## [33] "A04A" "A05"
## [35] "A07" "A08"
## [37] "A10" "A11A"
## [39] "A11M" "A11S"
## [41] "A12" "A13REC"
## [43] "A14REC" "A15"
## [45] "A16" "A17A"
## [47] "A17M" "A17S"
## [49] "A18" "A18A"
## [51] "B01REC" "B02REC"
## [53] "B03LU" "B03MA"
## [55] "B03MI" "B03JU"
## [57] "B03VI" "B03SA"
## [59] "B03DO" "B04"
## [61] "B05" "B05A"
## [63] "B06" "B07A"
## [65] "B07M" "B07S"
## [67] "B08" "B09A"
## [69] "B09M" "B09S"
## [71] "B10" "B11"
## [73] "B12" "B12A"
## [75] "B12B" "B12C"
## [77] "B13" "B14"
## [79] "B15" "B16G"
## [81] "B16U" "B16D"
## [83] "B16T" "B17"
## [85] "B18AG" "B18AU"
## [87] "B18BG" "B18BU"
## [89] "B19" "B20G"
## [91] "B20U" "B20D"
## [93] "B20T" "B21"
## [95] "B22" "B23"
## [97] "B24" "B25"
## [99] "B26" "B271"
## [101] "B272" "B28"
## [103] "B29" "B30"
## [105] "B31" "C01REC"
## [107] "C02REC" "C03"
## [109] "C04" "C05"
## [111] "C06" "C07"
## [113] "C08" "C09"
## [115] "C101" "C102"
## [117] "C11G" "C11U"
## [119] "C11D" "C11T"
## [121] "C12" "C13AG"
## [123] "C13AU" "C13BG"
## [125] "C13BU" "C14"
## [127] "C14A" "C14B"
## [129] "C14C" "C15"
## [131] "C16REC" "C17REC"
## [133] "C18" "C18A"
## [135] "C18B" "C19"
## [137] "D01" "D02"
## [139] "D03" "D04"
## [141] "D05" "E01A"
## [143] "E01B" "E01C"
## [145] "E01D" "E01E"
## [147] "E01F" "E01G"
## [149] "E01H" "E01I"
## [151] "E01J" "E01K"
## [153] "E01L" "E01M"
## [155] "E02C1" "E02D1"
## [157] "E02D2" "E02B"
## [159] "E02G1" "E02G2"
## [161] "E02F" "ED01"
## [163] "ED02" "ED03"
## [165] "ED0504" "ED06C"
## [167] "ED08" "ED09"
## [169] "ED10" "ED11F1"
## [171] "ED11F1A" "ED11GH1"
## [173] "ED11GH1A" "ED12"
## [175] "ED13" "ED14"
## [177] "ED14A" "ED15"
## [179] "S01A" "S01B"
## [181] "S02" "S03"
## [183] "S03A" "S03B"
## [185] "S03C" "S04"
## [187] "S05" "S06"
## [189] "S07" "S08"
## [191] "S09" "CATE_PEA"
## [193] "TAMA_PEA" "OCUP_PEA"
## [195] "RAMA_PEA" "HORAB"
## [197] "HORABC" "HORABCO"
## [199] "PEAD" "PEAA"
## [201] "informalidad" "TIPOHOGA"
## [203] "FEX" "NJEF"
## [205] "NCON" "NPAD"
## [207] "NMAD" "TIC01"
## [209] "TIC02" "TIC03"
## [211] "TIC0401" "TIC0402"
## [213] "TIC0403" "TIC0404"
## [215] "TIC0405" "TIC0406"
## [217] "TIC0407" "TIC0408"
## [219] "TIC0409" "TIC0501"
## [221] "TIC0502" "TIC0503"
## [223] "TIC0504" "TIC0505"
## [225] "TIC0506" "TIC0507"
## [227] "TIC0508" "TIC0509"
## [229] "TIC0510" "TIC0511"
## [231] "TIC0512" "TIC0513"
## [233] "TIC06" "TIC07"
## [235] "añoest" "ra06ya09"
## [237] "e01aimde" "e01bimde"
## [239] "e01cimde" "e01dde"
## [241] "e01ede" "e01fde"
## [243] "e01gde" "e01hde"
## [245] "e01ide" "e01jde"
## [247] "e01kde" "e01lde"
## [249] "e01mde" "e01kjde"
## [251] "e02bde" "ingrevasode"
## [253] "ingreñangarekode" "ingrepytyvõde"
## [255] "ingresect_privadode" "ingreadicional_tekoporãde"
## [257] "otroingre_subside" "ipcm"
## [259] "pobrezai" "pobnopoi"
## [261] "quintili" "decili"
## [263] "quintiai" "decilai"#P06 Sexo
data.eph.2020$P06=factor(data.eph.2020$P06,labels=c("Hombres","Mujeres"))
table(data.eph.2020$P06)##
## Hombres Mujeres
## 8762 8820#Enfermedad que tuvo en los últimos 90 días_A
table(data.eph.2020$S03A)##
## 1 2 3 4 5 6 9
## 1388 25 17 286 41 1531 2# explorar y codificar las variables de interes
data.eph.2020$S03A=factor(data.eph.2020$S03A,labels=c("Resfrio","Bronquitis","Neumonia","COVID19","Denguezikchik","otro","NR"))
table(data.eph.2020$S03A)##
## Resfrio Bronquitis Neumonia COVID19 Denguezikchik
## 1388 25 17 286 41
## otro NR
## 1531 2table(data.eph.2020$S03A,data.eph.2020$P06)##
## Hombres Mujeres
## Resfrio 687 701
## Bronquitis 14 11
## Neumonia 8 9
## COVID19 143 143
## Denguezikchik 17 24
## otro 591 940
## NR 1 1Dado una poblacion de 2600 canicas que contienen una urna,los cuales son de color rojo y plata respectivamente;para estimar la proporcion de canicAs procedemos a tomar muestras aleatorias de tamaño 30 de los cuales hacemos el conteo de las canicas rojas obtenidas p(r):numero de canicas rojas/numero total de canicas de la muestra,teniedo 17 replicas obtuvimos que dicho valor es de p=0,30. Luego aumentamos el tamaño de las muestras a valores aleatorios distintos y el resultados de las proporciones obtenidas lo grafica en una tabla mediante el cual observamos que habia una suerte de concentracion aldedor del 30% incluso se tuvo en una de las muestras el valor de la proporcion esperada,porlo que podemos decir que a medida aumentamos el numeros de muestras nos acercamos al valor del parametro mediante la estadistica muestral.Luego se comprobo por el conteo de todas las canicas que la p=686/2600.
#a) La estadistica bayesiana utlizamos el teorema de bayes para describir la incertidumbre mediante una funcion a priori cuyos valores siguen una distribucion de probabilidad que describe el compartamiento de la variable de estudio para utilizarla con la distribucion a posterior.Lo que tambien menciono el Dr.Bernardo que la estadistica bayesiana no era una rama de la estadistica clasica,sino una alternativa completa para el analisis de las variables de estudio.
#b) Para el trabajo utice la EPH 2019 Y 2020 para realizar el contraste de la hipotesis de la proporcion de hombres que utilizo internet para transferencia bancarias no es igual a la proporcion de mujeres,con um nivel de significancia de 0,05 llegue a la conclusion de que la proporcion de hombres es mayor que al de las mujeres que utilizaron internet para transferencias bancarias.