#setwd(" ")
library(rio)
data = import("./data/s15/b-Latinobarometro_2020.dta")16 Repaso Final II
names(data) [1] "numinves" "idenpa" "numentre" "reg" "ciudad"
[6] "tamciud" "comdist" "edad" "sexo" "codigo"
[11] "diareal" "mesreal" "ini" "fin" "dura"
[16] "totrevi" "totcuot" "totrech" "totperd" "numcasa"
[21] "codsuper" "supervvi" "superven" "codif" "digit"
[26] "p1st" "p2st" "p3stgbs" "p4stgbs" "p5stgbs"
[31] "p6st" "p7stgbs" "p8st_a" "p8st_b" "p8st_c"
[36] "p9stgbs" "p10stgbs" "P11STGBS_A" "P11STGBS_B" "p12st"
[41] "P13STGBS_A" "P13STGBS_B" "p13st_c" "p13st_d" "p13st_e"
[46] "p13st_f" "p13st_g" "p13st_h" "p13st_i" "p14gbs"
[51] "p15st_a" "p15st_b" "p15st_c" "p15st_d" "p15st_e"
[56] "p15st_f" "p15st_g" "p15n_h" "p15n_i" "p15n_j"
[61] "p15n_k" "P16N_A_01" "P16N_A_02" "P16N_A_03" "P16N_A_04"
[66] "P16N_A_05" "P16N_A_06" "P16N_A_07" "P16N_A_08" "P16N_A_09"
[71] "P16N_A_10" "P16N_A_11" "P16N_A_12" "P16N_A_13" "P16N_A_14"
[76] "P16N_A_15" "P16N_A_16" "P16N_B_01" "P16N_B_02" "P16N_B_03"
[81] "P16N_B_04" "P16N_B_05" "P16N_B_06" "P16N_B_07" "P16N_B_08"
[86] "P16N_B_09" "P16N_B_10" "P16N_B_11" "P16N_B_12" "P16N_B_13"
[91] "P16N_B_14" "P16N_B_15" "P16N_B_16" "p17stgbs" "p18st"
[96] "p19st_a" "p19n_b" "p19n_c" "p19n_d" "p20st_a"
[101] "p22stm_b" "p20stm_c" "p20stm_d" "p20st_e" "p20st_f"
[106] "p20st_g" "p21stm" "p22st_a" "p22st_b" "p22st_c"
[111] "p22st_d" "p23n_01" "p23n_02" "p23n_03" "p23n_04"
[116] "p23n_05" "p23n_06" "p23n_07" "p23n_08" "p23n_09"
[121] "p23n_10" "p24st_a" "p24st_b" "p24st_c" "p25n"
[126] "p26n_a" "p26n_b" "p27n" "p28st" "p29st_a"
[131] "p29st_b" "p29st_c" "p29st_d" "p29n_e" "p29st_f"
[136] "p29st_g" "p30st_a" "p30st_b" "p30st_c" "p30st_d"
[141] "p30st_e" "p30n" "p31st_a" "p31st_b" "p31st_c"
[146] "p31st_d" "p31stm_e" "p32na" "p32n_b" "p33n"
[151] "p34n" "p35n_a" "p35n_b" "p36n_a" "p36n_b"
[156] "p36stm_a" "p36stm_b" "p36stm_c" "p36stm_d" "P36STMB_A"
[161] "P36STMB_B" "P36STMB_C" "P36STMB_D" "p37n_a" "p37n_b"
[166] "p37n_c" "p37n_d" "p38n" "p39n_a" "p39st_b"
[171] "p39n_c" "p39n_d" "p39n_e" "p39n_f" "p39n_g"
[176] "p39n_h" "p40n" "p41n" "p42n" "p43n"
[181] "p44n" "p45n" "p46stgbs" "p47st_a" "p47st_b"
[186] "p47st_c" "p47st_d" "p47st_e" "p47st_f" "p47st_g"
[191] "p47st_h" "p47st_i" "p47st_j" "p47st_k" "p47st_l"
[196] "p47st_m" "p48st_1" "p48st_2" "p48st_3" "p49stgbs"
[201] "P50STGBS_A" "P51STGBS_B" "p52st" "p53n" "p54st_a"
[206] "p54st_b" "p54st_c" "p54st_d" "p55st_a" "p55st_b"
[211] "p55st_c" "p55st_d" "p55st_e" "p55st_f" "p56n"
[216] "p57st" "p58st" "p59st_a" "p59st_b" "p59n_c"
[221] "p59st_d" "p59n_e" "p59n_f" "p60st" "p60n_b"
[226] "p60n_c" "p61st" "p62n_a" "p62st_b" "p62st_c"
[231] "p62st_d" "p63st_01" "p63st_02" "p63st_03" "p63st_04"
[236] "p63st_05" "p63st_06" "p63st_07" "p63st_08" "p63st_09"
[241] "p64st" "p65st" "p66npn" "p67npn_a" "P67NPN_B_01"
[246] "P67NPN_B_02" "P67NPN_B_03" "P67NPN_B_04" "P67NPN_B_05" "P67NPN_B_06"
[251] "p68st" "p69st" "p70st" "P71STM_01" "P71STM_02"
[256] "P71STM_03" "P71STM_04" "P71STM_05" "P71STM_06" "P71STM_07"
[261] "P71STM_08" "P71STM_09" "P71STM_10" "P71STM_11" "p72npn"
[266] "p73npn_1" "p73npn_2" "p73npn_3" "p73npn_4" "p73npn_5"
[271] "p74npn_1" "p74npn_2" "p74npn_3" "p74npn_4" "p74npn_5"
[276] "P75NPN_01" "P75NPN_02" "P75NPN_03" "P75NPN_04" "P75NPN_05"
[281] "P75NPN_06" "P75NPN_07" "P75NPN_08" "P75NPN_09" "P75NPN_10"
[286] "P75NPN_11" "P75NPN_12" "P75NPN_13" "P75NPN_14" "P75NPN_15"
[291] "P75NPN_16" "p76n_1" "p76st_2" "p76n_3" "p76n_4"
[296] "p76st_5" "p76st_6" "p76st_7" "p76n_8" "p76n_9"
[301] "p76n_10" "p76n_11" "p76n_12" "p76n_13" "p77n"
[306] "p78n" "p79n" "p80n" "p81n_01" "p81n_02"
[311] "p81n_03" "p81n_04" "p81n_05" "s1" "s2"
[316] "s3" "s4" "s5npn" "s6npn_01" "s6npn_02"
[321] "s6npn_03" "s6npn_04" "s6npn_05" "s6npn_06" "s6npn_07"
[326] "s6npn_08" "s6npn_09" "s7npn" "s8npn_a" "s8npn_b"
[331] "s9npn" "s10" "s11_a" "s12" "s13"
[336] "s14" "s15" "s16" "s17" "s18mn_a"
[341] "S18N_B_01" "S18N_B_02" "S18N_B_03" "S18N_B_04" "S18N_B_05"
[346] "S18N_B_06" "S18N_B_07" "S18N_B_08" "S18N_B_09" "S18N_B_10"
[351] "S18N_B_11" "S18N_B_12" "S18N_B_13" "S18N_B_14" "S18N_B_15"
[356] "S18N_B_16" "S18N_B_17" "S18N_B_18" "S18N_B_19" "S18N_B_20"
[361] "S18N_B_21" "s19m_01" "s19m_02" "s19m_03" "s19m_04"
[366] "s19m_05" "s19m_06" "s19m_07" "s19m_08" "s19m_09"
[371] "s19m_10" "s20_a" "s20_b" "s21" "s22"
[376] "s22_a" "s22_b" "s22_c" "s23_a" "s23_b"
[381] "s24_a" "s24_b" "s25" "s26_a" "s26_b"
[386] "s26_c" "s26_d" "s26_e" "s26_f" "s26_g"
[391] "s26_h" "s26_i" "s26_j" "s26_k" "s26_l"
[396] "m_nc" "n_ni" "s27" "s28_a" "s28_b"
[401] "s29" "s30" "reeduc_1" "reeduc_2" "reedad"
[406] "perpart" "fampart" "wt" Descargue la base de datos Latinobarometro2020.dta que corresponde a las respuestas de peruanos y residentes al cuestionario del Latinobarometro. Descargue el cuestionario https://www.latinobarometro.org/latContents.jsp. Seleccione Perú.
• Crear un modelo de regresión lineal múltiple en que explique la variable “dinero que le queda al grupo más rico”* (p73npn_5), con por lo menos dos variables independientes (V. independiente (X1): Autoubicación en la escala de pobreza (Variable dicotómica) V. Independiente (X2): Satisfacción con el funcionamiento de la economía en el país) (Variable dicotómica) Recuerda practicar la recodificación de variables.
Determinar si el modelo es valido, el poder explicativo del modelo.
Analizar el modelo de regresión lineal múltiple.
Estima los resultados de la ecuación del modelo y coloca un ejemplo.
Realiza el gráfico de los coeficientes.
#FILTRAMOS: SOLO PERU
#instalar rtoools
#library(dyplr)
#dataperu <- filter(data, idenpa == 604)#OTRO MODO DE FILTRAR SIN PAQUETES
dataperu = subset(data, idenpa == "604") #recodificacion de -> Autoidentificacion en la escala de pobreza (p8st_a)
str(dataperu$p8st_a) num [1:1200] 2 6 1 3 1 1 4 1 -1 3 ...dataperu$p8st_a = as.numeric(dataperu$p8st_a)
str(dataperu$p8st_a) num [1:1200] 2 6 1 3 1 1 4 1 -1 3 ...library(car)
dataperu$pobre = recode(dataperu$p8st_a,
"1:5 = 'pobre'; 6:10 = 'rico'; else = NA")
table(dataperu$pobre)
pobre rico
1014 179 #Satisfacción con la Economía
DONE ————————->