Expansión de la CASEN sobre el CENSO (Región 11)

De ingresos sobre una categoría de respuesta RURAL

VE-CC-AJ

DataIntelligence

Martes 06-07-2021

Abstract

Expandiremos los ingresos promedios comunales obtenidos de la CASEN sobre la pregunta (P15): “Nivel del curso más alto aprobado” en la categoría de respuesta “Profesional” (12), que más alto correlaciona con los ingresos expandidos (obtenidos de la multiplicación del ingreso promedio y los habitantes), ambos a nivel comunal.

Haremos el análisis sobre la región 11.

Ensayaremos diferentes modelos dentro del análisis de regresión cuya variable independiente será: “frecuencia de población que posee la variable Censal respecto a la zona” y la dependiente: “ingreso expandido de la Casen por zona”

Lo anterior para ir combinando a nivel nacional los mejores modelos para construir una tabla de valores predichos.

1 Variable CENSO

Necesitamos calcular las frecuencias a nivel censal de las respuestas correspondientes a la categoría: “Profesional (4 o más años)” del campo P15 a nivel rural del Censo de personas. Recordemos que ésta fué la más alta correlación en relación a los ingresos expandidos (ver punto 3.4 aquí).

1.1 Lectura y filtrado de la tabla censal de personas

Leemos la tabla Censo 2017 de personas que ya tiene integrada la clave zonal:

tabla_con_clave <- readRDS("../censo_personas_con_clave_17")
r3_100 <- tabla_con_clave[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	NHOGAR	PERSONAN	P07	P08	P09	P10	P10COMUNA	P10PAIS	P11	P11COMUNA	P11PAIS	P12	P12COMUNA	P12PAIS	P12A_LLEGADA	P12A_TRAMO	P13	P14	P15	P15A	P16	P16A	P16A_OTRO	P17	P18	P19	P20	P21M	P21A	P10PAIS_GRUPO	P11PAIS_GRUPO	P12PAIS_GRUPO	ESCOLARIDAD	P16A_GRUPO	REGION_15R	PROVINCIA_15R	COMUNA_15R	P10COMUNA_15R	P11COMUNA_15R	P12COMUNA_15R	clave
15	152	15202	1	2	6	13225	1	1	1	1	1	73	1	98	998	3	15101	998	1	98	998	9998	98	2	4	6	2	1	2	98	7	98	98	98	98	9998	998	998	998	4	2	15	152	15202	98	15101	98	15202012006
15	152	15202	1	2	6	13225	3	1	1	1	1	78	1	98	998	2	98	998	1	98	998	9998	98	3	98	98	98	1	2	98	7	98	98	98	98	9998	998	998	998	0	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	3	1	2	2	2	78	1	98	998	2	98	998	1	98	998	9998	98	3	98	98	98	1	2	98	7	98	1	1	3	1965	998	998	998	0	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	3	1	3	5	2	52	1	98	998	2	98	998	1	98	998	9998	98	1	2	5	2	1	2	98	7	98	2	1	4	1995	998	998	998	2	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	3	1	4	11	1	44	1	98	998	2	98	998	1	98	998	9998	98	1	3	5	2	1	2	98	1	Z	98	98	98	9998	998	998	998	3	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	9	1	1	1	1	39	1	98	998	2	98	998	1	98	998	9998	98	2	8	5	1	1	2	98	8	98	98	98	98	9998	998	998	998	8	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	9	1	2	2	2	35	1	98	998	2	98	998	1	98	998	9998	98	2	6	5	2	1	2	98	1	Z	2	2	11	2004	998	998	998	6	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	9	1	3	5	1	13	1	98	998	2	98	998	1	98	998	9998	98	1	7	5	2	1	2	98	98	98	98	98	98	9998	998	998	998	7	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	9	1	4	5	1	12	1	98	998	2	98	998	1	98	998	9998	98	1	6	5	2	1	2	98	98	98	98	98	98	9998	998	998	998	6	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	10	1	1	1	2	65	1	98	998	2	98	998	1	98	998	9998	98	2	4	5	2	1	2	98	6	98	3	3	9	1992	998	998	998	4	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	13	1	1	1	1	50	1	98	998	2	98	998	1	98	998	9998	98	2	5	5	2	1	2	98	1	Z	98	98	98	9998	998	998	998	5	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	13	1	2	4	2	43	1	98	998	2	98	998	1	98	998	9998	98	2	6	5	2	1	2	98	6	98	2	2	3	2002	998	998	998	6	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	13	1	3	5	1	15	3	15201	998	2	98	998	1	98	998	9998	98	1	1	7	2	1	2	98	8	98	98	98	98	9998	998	998	998	9	2	15	152	15202	15201	98	98	15202012006
15	152	15202	1	2	6	13225	16	1	1	1	1	75	1	98	998	2	98	998	1	98	998	9998	98	3	98	98	98	1	2	98	7	98	98	98	98	9998	998	998	998	0	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	16	1	2	16	2	58	4	98	68	6	98	998	5	98	998	9999	1	3	98	98	98	1	2	98	7	98	4	4	99	9999	68	68	68	0	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	16	1	3	2	2	70	1	98	998	2	98	998	1	98	998	9998	98	3	98	98	98	1	2	98	7	98	5	4	99	9999	998	998	998	0	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	17	1	1	1	2	43	2	98	998	2	98	998	1	98	998	9998	98	2	8	5	1	1	2	98	1	I	3	3	9	2008	998	998	998	8	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	17	1	2	4	1	55	2	98	998	2	98	998	1	98	998	9998	98	2	6	5	2	1	2	98	6	98	98	98	98	9998	998	998	998	6	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	17	1	3	5	2	13	2	98	998	2	98	998	2	15101	998	9998	98	1	7	5	2	1	2	98	98	98	98	98	98	9998	998	998	998	7	2	15	152	15202	98	98	15101	15202012006
15	152	15202	1	2	6	13225	17	1	4	5	1	8	2	98	998	2	98	998	2	15101	998	9998	98	1	2	5	2	1	2	98	98	98	98	98	98	9998	998	998	998	2	2	15	152	15202	98	98	15101	15202012006
15	152	15202	1	2	6	13225	17	1	5	15	2	29	2	98	998	4	98	998	3	98	998	2015	1	2	6	5	2	1	2	98	6	98	5	5	11	2014	998	604	604	6	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	17	1	6	15	1	4	2	98	998	1	98	998	5	98	998	2015	1	1	0	1	2	1	2	98	98	98	98	98	98	9998	998	998	68	0	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	17	1	7	15	2	2	2	98	998	1	98	998	3	98	998	2015	1	1	0	1	2	1	2	98	98	98	98	98	98	9998	998	998	604	0	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	17	1	8	15	1	16	2	98	998	6	98	998	1	98	998	9998	98	2	4	5	2	1	2	98	6	98	98	98	98	9998	998	68	998	4	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	18	1	1	1	2	74	1	98	998	2	98	998	1	98	998	9998	98	2	2	5	2	1	2	98	6	98	2	2	12	1976	998	998	998	2	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	19	1	1	1	1	68	1	98	998	2	98	998	1	98	998	9998	98	3	98	98	98	1	2	98	7	98	98	98	98	9998	998	998	998	0	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	20	1	1	1	1	74	1	98	998	3	15101	998	1	98	998	9998	98	2	2	5	2	1	2	98	1	Z	98	98	98	9998	998	998	998	2	2	15	152	15202	98	15101	98	15202012006
15	152	15202	1	2	6	13225	20	1	2	2	2	65	1	98	998	3	997	998	3	98	998	9999	2	2	2	5	2	1	2	98	6	98	2	2	9	1982	998	998	604	2	2	15	152	15202	98	997	98	15202012006
15	152	15202	1	2	6	13225	25	1	1	1	2	76	1	98	998	2	98	998	1	98	998	9998	98	3	98	98	98	1	2	98	6	98	8	6	3	1981	998	998	998	0	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	25	1	2	5	2	36	1	98	998	2	98	998	1	98	998	9998	98	2	4	8	1	1	2	98	1	A	0	98	98	9998	998	998	998	12	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	28	1	1	1	2	31	1	98	998	2	98	998	5	98	998	2007	2	2	5	5	2	1	2	98	1	A	2	2	4	2008	998	998	68	5	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	28	1	2	4	1	35	1	98	998	2	98	998	5	98	998	2007	2	2	6	5	2	1	2	98	1	F	98	98	98	9998	998	998	68	6	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	28	1	3	5	1	11	1	98	998	2	98	998	5	98	998	2007	2	1	5	5	2	1	2	98	98	98	98	98	98	9998	998	998	68	5	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	28	1	4	5	1	8	1	98	998	2	98	998	1	98	998	9998	98	1	2	5	2	1	2	98	98	98	98	98	98	9998	998	998	998	2	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	28	1	5	15	2	74	1	98	998	2	98	998	1	98	998	9998	98	2	3	5	2	1	2	98	6	98	6	6	99	9999	998	998	998	3	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	33	1	1	1	1	41	1	98	998	2	98	998	1	98	998	9998	98	2	8	5	1	1	2	98	1	Z	98	98	98	9998	998	998	998	8	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	33	1	2	2	2	47	1	98	998	2	98	998	1	98	998	9998	98	2	8	5	1	1	2	98	1	A	2	1	4	1996	998	998	998	8	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	33	1	3	14	1	88	1	98	998	2	98	998	1	98	998	9998	98	3	98	98	98	1	2	98	7	98	98	98	98	9998	998	998	998	0	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	33	1	4	14	1	65	1	98	998	2	98	998	1	98	998	9998	98	2	2	5	2	1	2	98	7	98	98	98	98	9998	998	998	998	2	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	36	1	1	1	2	59	1	98	998	2	98	998	1	98	998	9998	98	2	2	5	2	1	2	98	6	98	8	8	2	1998	998	998	998	2	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	36	1	2	2	1	56	1	98	998	99	99	999	1	98	998	9998	98	2	2	5	2	1	2	98	6	98	98	98	98	9998	998	999	998	2	2	15	152	15202	98	99	98	15202012006
15	152	15202	1	2	6	13225	36	1	3	5	2	36	1	98	998	2	98	998	1	98	998	9998	98	2	8	5	1	1	2	98	6	98	2	2	7	2010	998	998	998	8	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	36	1	4	12	2	13	1	98	998	2	98	998	1	98	998	9998	98	1	7	5	2	1	2	98	98	98	98	98	98	9998	998	998	998	7	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	36	1	5	12	2	6	1	98	998	2	98	998	1	98	998	9998	98	1	0	3	1	1	2	98	98	98	98	98	98	9998	998	998	998	0	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	36	1	6	5	1	24	1	98	998	3	15101	998	1	98	998	9998	98	2	4	7	1	1	2	98	1	Z	98	98	98	9998	998	998	998	12	2	15	152	15202	98	15101	98	15202012006
15	152	15202	1	2	6	13225	36	1	7	11	2	24	1	98	998	3	15101	998	1	98	998	9998	98	2	4	7	1	1	2	98	1	N	2	2	11	2015	998	998	998	12	2	15	152	15202	98	15101	98	15202012006
15	152	15202	1	2	6	13225	36	1	8	12	1	6	1	98	998	2	98	998	2	15101	998	9998	98	1	0	3	1	1	2	98	98	98	98	98	98	9998	998	998	998	0	2	15	152	15202	98	98	15101	15202012006
15	152	15202	1	2	6	13225	36	1	9	12	2	1	1	98	998	1	98	998	2	15101	998	9998	98	3	98	98	98	1	2	98	98	98	98	98	98	9998	998	998	998	0	2	15	152	15202	98	98	15101	15202012006
15	152	15202	1	2	6	13225	38	1	1	1	1	19	1	98	998	3	15101	998	2	15101	998	9998	98	1	1	8	2	1	2	98	1	A	98	98	98	9998	998	998	998	9	2	15	152	15202	98	15101	15101	15202012006
15	152	15202	1	2	6	13225	39	1	1	1	1	21	1	98	998	2	98	998	1	98	998	9998	98	2	1	7	2	1	2	98	1	F	98	98	98	9998	998	998	998	9	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	39	1	2	4	2	22	1	98	998	2	98	998	1	98	998	9998	98	2	1	8	2	1	2	98	6	98	0	98	98	9998	998	998	998	9	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	43	1	1	1	2	26	1	98	998	2	98	998	1	98	998	9998	98	2	8	5	1	1	2	98	6	98	2	2	10	2013	998	998	998	8	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	43	1	2	2	1	24	1	98	998	2	98	998	1	98	998	9998	98	2	8	5	1	1	2	98	1	Z	98	98	98	9998	998	998	998	8	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	43	1	3	13	2	71	1	98	998	2	98	998	1	98	998	9998	98	2	1	5	2	1	2	98	6	98	3	3	12	1974	998	998	998	1	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	43	1	4	5	2	6	1	98	998	2	98	998	1	98	998	9998	98	1	0	3	1	1	2	98	98	98	98	98	98	9998	998	998	998	0	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	6	13225	43	1	5	5	2	3	1	98	998	1	98	998	1	98	998	9998	98	1	0	1	1	1	2	98	98	98	98	98	98	9998	998	998	998	0	2	15	152	15202	98	98	98	15202012006
15	152	15202	1	2	8	13910	5	1	1	1	1	44	1	98	998	2	98	998	3	98	998	2005	2	2	4	7	1	1	2	98	6	98	98	98	98	9998	998	998	604	12	2	15	152	15202	98	98	98	15202012008
15	152	15202	1	2	8	13910	5	1	2	2	2	42	1	98	998	2	98	998	1	98	998	9998	98	2	3	5	2	1	2	98	1	P	3	3	12	2006	998	998	998	3	2	15	152	15202	98	98	98	15202012008
15	152	15202	1	2	8	13910	5	1	3	5	2	10	1	98	998	2	98	998	1	98	998	9998	98	1	4	5	2	1	2	98	98	98	98	98	98	9998	998	998	998	4	2	15	152	15202	98	98	98	15202012008
15	152	15202	1	2	8	13910	7	1	1	1	2	70	1	98	998	2	98	998	1	98	998	9998	98	2	2	5	2	1	2	98	6	98	7	7	6	1994	998	998	998	2	2	15	152	15202	98	98	98	15202012008
15	152	15202	1	2	8	13910	7	1	2	5	1	44	1	98	998	2	98	998	1	98	998	9998	98	2	5	5	2	1	2	98	7	98	98	98	98	9998	998	998	998	5	2	15	152	15202	98	98	98	15202012008
15	152	15202	1	2	8	13910	8	1	1	1	1	58	1	98	998	2	98	998	3	98	998	2004	2	2	4	5	2	1	2	98	6	98	98	98	98	9998	998	998	604	4	2	15	152	15202	98	98	98	15202012008
15	152	15202	1	2	8	13910	8	1	2	2	2	59	1	98	998	2	98	998	3	98	998	2004	2	2	2	5	2	1	2	98	6	98	3	3	7	1999	998	998	604	2	2	15	152	15202	98	98	98	15202012008
15	152	15202	1	2	8	13910	19	1	1	1	1	58	99	99	999	99	99	999	99	99	999	9999	99	99	99	99	99	99	99	99	99	99	98	98	98	9998	999	999	999	99	99	15	152	15202	99	99	99	15202012008
15	152	15202	1	2	8	13910	21	1	1	1	1	53	1	98	998	2	98	998	1	98	998	9998	98	2	8	5	1	1	2	98	1	H	98	98	98	9998	998	998	998	8	2	15	152	15202	98	98	98	15202012008
15	152	15202	1	2	8	13910	21	1	2	2	2	46	1	98	998	2	98	998	1	98	998	9998	98	2	3	5	2	1	2	98	6	98	3	3	2	1990	998	998	998	3	2	15	152	15202	98	98	98	15202012008
15	152	15202	1	2	8	13910	22	1	1	1	2	73	1	98	998	2	98	998	1	98	998	9998	98	3	98	98	98	1	2	98	6	98	6	5	3	1979	998	998	998	0	2	15	152	15202	98	98	98	15202012008
15	152	15202	1	2	8	13910	30	1	1	1	1	57	1	98	998	2	98	998	2	997	998	9998	98	2	3	5	2	1	2	98	6	98	98	98	98	9998	998	998	998	3	2	15	152	15202	98	98	997	15202012008
15	152	15202	1	2	12	8394	3	1	1	2	2	64	1	98	998	2	98	998	3	98	998	1974	4	3	98	98	98	1	2	98	1	A	12	10	99	9999	998	998	604	0	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	3	1	2	1	1	74	2	98	998	2	98	998	1	98	998	9998	98	3	98	98	98	1	2	98	99	99	98	98	98	9998	998	998	998	0	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	3	1	3	5	2	38	1	98	998	2	98	998	1	98	998	9998	98	2	8	5	1	1	2	98	2	A	0	98	98	9998	998	998	998	8	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	3	1	4	14	1	38	99	99	999	99	99	999	99	99	999	9999	99	99	99	99	99	99	99	99	8	98	98	98	98	9998	999	999	999	99	99	15	152	15202	99	99	99	15202012012
15	152	15202	1	2	12	8394	9	1	1	1	2	79	1	98	998	2	98	998	1	98	998	9998	98	3	98	98	98	1	2	98	8	98	2	2	99	9999	998	998	998	0	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	19	1	1	1	1	46	99	99	999	99	99	999	99	99	999	9999	99	99	99	99	99	99	99	99	99	99	98	98	98	9998	999	999	999	99	99	15	152	15202	99	99	99	15202012012
15	152	15202	1	2	12	8394	20	1	1	1	2	58	1	98	998	2	98	998	1	98	998	9998	98	2	8	5	1	1	2	98	1	A	3	3	7	1982	998	998	998	8	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	21	1	1	1	2	45	1	98	998	6	98	998	2	997	998	9998	98	2	4	5	2	1	2	98	1	A	6	6	2	2007	998	68	998	4	2	15	152	15202	98	98	997	15202012012
15	152	15202	1	2	12	8394	21	1	2	5	2	10	1	98	998	6	98	998	2	3201	998	9998	98	1	4	5	2	1	2	98	98	98	98	98	98	9998	998	68	998	4	2	15	152	15202	98	98	3201	15202012012
15	152	15202	1	2	12	8394	24	1	1	1	1	67	1	98	998	2	98	998	1	98	998	9998	98	3	98	98	98	1	2	98	8	98	98	98	98	9998	998	998	998	0	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	24	1	2	2	2	53	1	98	998	2	98	998	3	98	998	9999	99	3	98	98	98	1	2	98	8	98	0	98	98	9998	998	998	604	0	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	27	1	1	1	1	48	1	98	998	2	98	998	1	98	998	9998	98	2	4	7	1	1	2	98	8	98	98	98	98	9998	998	998	998	12	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	31	1	1	1	1	49	1	98	998	4	98	998	3	98	998	2001	2	2	8	5	1	1	2	98	1	A	98	98	98	9998	998	604	604	8	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	42	1	1	1	1	46	1	98	998	2	98	998	3	98	998	1992	3	2	8	5	1	1	2	98	2	A	98	98	98	9998	998	998	604	8	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	42	1	2	2	2	24	1	98	998	6	98	998	5	98	998	2013	1	2	7	5	2	1	2	98	6	98	2	2	6	2016	998	68	68	7	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	42	1	3	6	2	2	1	98	998	1	98	998	1	98	998	9998	98	3	98	98	98	1	2	98	98	98	98	98	98	9998	998	998	998	0	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	42	1	4	5	1	0	1	98	998	1	98	998	2	15101	998	9998	98	99	99	99	99	1	2	98	98	98	98	98	98	9998	998	998	998	99	2	15	152	15202	98	98	15101	15202012012
15	152	15202	1	2	12	8394	42	1	5	5	2	13	1	98	998	2	98	998	3	98	998	9999	99	1	7	5	2	1	2	98	98	98	98	98	98	9998	998	998	604	7	2	15	152	15202	98	98	98	15202012012
15	152	15202	1	2	12	8394	42	1	6	5	1	6	1	98	998	2	98	998	2	15101	998	9998	98	1	0	3	1	1	2	98	98	98	98	98	98	9998	998	998	998	0	2	15	152	15202	98	98	15101	15202012012
15	152	15202	1	2	15	4094	2	1	1	1	1	41	1	98	998	2	98	998	1	98	998	9998	98	2	4	12	1	1	2	98	1	O	98	98	98	9998	998	998	998	16	2	15	152	15202	98	98	98	15202012015
15	152	15202	1	2	15	4094	8	1	1	17	1	70	2	98	998	2	98	998	1	98	998	9998	98	3	98	98	98	1	2	98	7	98	98	98	98	9998	998	998	998	0	2	15	152	15202	98	98	98	15202012015
15	152	15202	1	2	15	4094	8	1	2	17	1	47	2	98	998	3	15101	998	2	8101	998	9998	98	2	4	8	1	1	2	98	1	Z	98	98	98	9998	998	998	998	12	2	15	152	15202	98	15101	8101	15202012015
15	152	15202	1	2	15	4094	8	1	3	17	1	19	2	98	998	3	15101	998	2	15101	998	9998	98	1	99	7	99	1	2	98	1	I	98	98	98	9998	998	998	998	99	2	15	152	15202	98	15101	15101	15202012015
15	152	15202	1	2	15	4094	8	1	4	17	1	43	2	98	998	3	4302	998	2	8101	998	9998	98	99	4	8	1	1	2	98	1	N	98	98	98	9998	998	998	998	12	2	15	152	15202	98	4302	8101	15202012015
15	152	15202	1	2	15	4094	8	1	5	17	2	35	2	98	998	6	98	998	5	98	998	2016	1	2	8	5	1	1	2	98	1	I	2	2	3	2007	998	68	68	8	2	15	152	15202	98	98	98	15202012015
15	152	15202	1	2	15	4094	8	1	6	17	1	36	3	13123	998	3	13123	998	2	12101	998	9998	98	2	5	12	1	2	98	98	1	J	98	98	98	9998	998	998	998	17	98	15	152	15202	13123	13123	12101	15202012015
15	152	15202	1	2	15	4094	8	1	7	17	2	25	2	98	998	3	15101	998	2	15101	998	9998	98	2	5	12	1	1	2	98	1	Q	1	1	12	2011	998	998	998	17	2	15	152	15202	98	15101	15101	15202012015
15	152	15202	1	2	15	4094	9	1	1	1	1	72	1	98	998	2	98	998	1	98	998	9998	98	2	1	5	2	1	2	98	1	G	98	98	98	9998	998	998	998	1	2	15	152	15202	98	98	98	15202012015
15	152	15202	1	2	15	4094	12	1	1	1	1	21	1	98	998	3	15101	998	2	15101	998	9998	98	2	4	8	1	1	2	98	1	N	98	98	98	9998	998	998	998	12	2	15	152	15202	98	15101	15101	15202012015
15	152	15202	1	2	15	4094	15	1	1	1	1	61	1	98	998	2	98	998	1	98	998	9998	98	2	3	7	2	1	2	98	4	98	98	98	98	9998	998	998	998	11	2	15	152	15202	98	98	98	15202012015
15	152	15202	1	2	15	4094	15	1	2	5	2	31	1	98	998	3	15101	998	1	98	998	9998	98	2	4	12	1	1	2	98	1	P	1	1	10	2007	998	998	998	16	2	15	152	15202	98	15101	98	15202012015
15	152	15202	1	2	15	4094	16	1	1	1	1	34	1	98	998	3	15101	998	1	98	998	9998	98	2	5	12	1	1	2	98	1	O	98	98	98	9998	998	998	998	17	2	15	152	15202	98	15101	98	15202012015

Despleguemos los códigos de regiones de nuestra tabla:

regiones <- unique(tabla_con_clave$REGION)
regiones

##  [1] 15 14 13 12 11 10  9 16  8  7  6  5  4  3  2  1

Hagamos un subset con la region 11 y con la zona = 2:

tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$REGION == 11) 
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$AREA== 2)

1.2 Cálculo de frecuencias

tabla_con_clave_f <- tabla_con_clave[,-c(1,2,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20),drop=FALSE]

names(tabla_con_clave_f)[9] <- "Nivel del curso más alto aprobado"

# Ahora filtramos por Nivel del curso más alto aprobado = 11.
tabla_con_clave_ff <- filter(tabla_con_clave_f, tabla_con_clave_f$`Nivel del curso más alto aprobado` == 12)

# Determinamos las frecuencias por zona:
b <- tabla_con_clave_ff$clave
c <- tabla_con_clave_ff$`Nivel del curso más alto aprobado`
d <- tabla_con_clave_ff$COMUNA
cross_tab =  xtabs( ~ unlist(b) + unlist(c)+ unlist(d))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
names(d)[1] <- "zona" 
d$anio <- "2017"

Veamos los primeros 100 registros:

r3_100 <- d[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	unlist.c.	unlist.d.	Freq	anio
1	11101022003	12	11101	2	2017
2	11101022010	12	11101	2	2017
3	11101022031	12	11101	3	2017
4	11101022038	12	11101	9	2017
5	11101032005	12	11101	2	2017
6	11101032015	12	11101	1	2017
7	11101032016	12	11101	1	2017
8	11101032032	12	11101	9	2017
9	11101042024	12	11101	3	2017
10	11101042027	12	11101	4	2017
11	11101052004	12	11101	28	2017
12	11101052008	12	11101	2	2017
13	11101052014	12	11101	11	2017
14	11101052039	12	11101	1	2017
15	11101052901	12	11101	3	2017
16	11101062020	12	11101	4	2017
17	11101062023	12	11101	4	2017
18	11101072014	12	11101	3	2017
19	11101072018	12	11101	156	2017
20	11101072019	12	11101	9	2017
21	11101072020	12	11101	17	2017
22	11101072023	12	11101	1	2017
23	11101072025	12	11101	1	2017
24	11101072035	12	11101	8	2017
25	11101072036	12	11101	35	2017
26	11101072037	12	11101	10	2017
27	11101092002	12	11101	12	2017
28	11101092007	12	11101	1	2017
29	11101092901	12	11101	3	2017
30	11101102011	12	11101	35	2017
31	11101102020	12	11101	70	2017
32	11101102033	12	11101	192	2017
33	11101112001	12	11101	26	2017
34	11101112009	12	11101	62	2017
35	11101112011	12	11101	189	2017
36	11101112012	12	11101	5	2017
37	11101112013	12	11101	181	2017
38	11101112029	12	11101	23	2017
39	11101122011	12	11101	52	2017
40	11101132011	12	11101	10	2017
198	11102012004	12	11102	25	2017
199	11102032001	12	11102	3	2017
200	11102042003	12	11102	8	2017
201	11102052002	12	11102	13	2017
359	11201012009	12	11201	9	2017
360	11201012013	12	11201	2	2017
361	11201012055	12	11201	38	2017
362	11201012060	12	11201	13	2017
363	11201012066	12	11201	3	2017
364	11201012067	12	11201	15	2017
365	11201012901	12	11201	22	2017
366	11201022008	12	11201	1	2017
367	11201022057	12	11201	4	2017
368	11201022058	12	11201	4	2017
369	11201022901	12	11201	1	2017
370	11201032010	12	11201	2	2017
371	11201032012	12	11201	2	2017
372	11201032019	12	11201	1	2017
373	11201032038	12	11201	3	2017
374	11201032068	12	11201	11	2017
375	11201042006	12	11201	36	2017
376	11201042007	12	11201	15	2017
377	11201042065	12	11201	1	2017
378	11201052003	12	11201	2	2017
379	11201052020	12	11201	8	2017
380	11201052033	12	11201	3	2017
381	11201052035	12	11201	3	2017
382	11201052901	12	11201	3	2017
383	11201062005	12	11201	12	2017
384	11201062022	12	11201	8	2017
385	11201062023	12	11201	4	2017
386	11201062024	12	11201	2	2017
387	11201062025	12	11201	4	2017
388	11201062026	12	11201	1	2017
389	11201062027	12	11201	5	2017
390	11201062028	12	11201	1	2017
391	11201062029	12	11201	2	2017
392	11201062031	12	11201	4	2017
393	11201062032	12	11201	15	2017
394	11201062036	12	11201	15	2017
395	11201062037	12	11201	9	2017
396	11201062044	12	11201	30	2017
397	11201062063	12	11201	3	2017
398	11201072015	12	11201	10	2017
399	11201072021	12	11201	5	2017
400	11201072040	12	11201	6	2017
401	11201072047	12	11201	1	2017
402	11201072050	12	11201	6	2017
403	11201072901	12	11201	27	2017
561	11202012003	12	11202	3	2017
562	11202012007	12	11202	14	2017
563	11202012018	12	11202	2	2017
564	11202012019	12	11202	12	2017
565	11202012901	12	11202	1	2017
566	11202022016	12	11202	18	2017
567	11202022020	12	11202	109	2017
568	11202042004	12	11202	1	2017
569	11202042005	12	11202	1	2017
570	11202042006	12	11202	10	2017
571	11202042008	12	11202	7	2017

Agregamos un cero a los códigos comunales de cuatro dígitos:

codigos <- d$unlist.d.
rango <- seq(1:nrow(d))
cadena <- paste("0",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(d,cadena)
comuna_corr <- comuna_corr[,-c(2,3),drop=FALSE]
names(comuna_corr)[4] <- "código" 
r3_100 <- comuna_corr[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	zona	Freq	anio	código
1	11101022003	2	2017	11101
2	11101022010	2	2017	11101
3	11101022031	3	2017	11101
4	11101022038	9	2017	11101
5	11101032005	2	2017	11101
6	11101032015	1	2017	11101
7	11101032016	1	2017	11101
8	11101032032	9	2017	11101
9	11101042024	3	2017	11101
10	11101042027	4	2017	11101
11	11101052004	28	2017	11101
12	11101052008	2	2017	11101
13	11101052014	11	2017	11101
14	11101052039	1	2017	11101
15	11101052901	3	2017	11101
16	11101062020	4	2017	11101
17	11101062023	4	2017	11101
18	11101072014	3	2017	11101
19	11101072018	156	2017	11101
20	11101072019	9	2017	11101
21	11101072020	17	2017	11101
22	11101072023	1	2017	11101
23	11101072025	1	2017	11101
24	11101072035	8	2017	11101
25	11101072036	35	2017	11101
26	11101072037	10	2017	11101
27	11101092002	12	2017	11101
28	11101092007	1	2017	11101
29	11101092901	3	2017	11101
30	11101102011	35	2017	11101
31	11101102020	70	2017	11101
32	11101102033	192	2017	11101
33	11101112001	26	2017	11101
34	11101112009	62	2017	11101
35	11101112011	189	2017	11101
36	11101112012	5	2017	11101
37	11101112013	181	2017	11101
38	11101112029	23	2017	11101
39	11101122011	52	2017	11101
40	11101132011	10	2017	11101
198	11102012004	25	2017	11102
199	11102032001	3	2017	11102
200	11102042003	8	2017	11102
201	11102052002	13	2017	11102
359	11201012009	9	2017	11201
360	11201012013	2	2017	11201
361	11201012055	38	2017	11201
362	11201012060	13	2017	11201
363	11201012066	3	2017	11201
364	11201012067	15	2017	11201
365	11201012901	22	2017	11201
366	11201022008	1	2017	11201
367	11201022057	4	2017	11201
368	11201022058	4	2017	11201
369	11201022901	1	2017	11201
370	11201032010	2	2017	11201
371	11201032012	2	2017	11201
372	11201032019	1	2017	11201
373	11201032038	3	2017	11201
374	11201032068	11	2017	11201
375	11201042006	36	2017	11201
376	11201042007	15	2017	11201
377	11201042065	1	2017	11201
378	11201052003	2	2017	11201
379	11201052020	8	2017	11201
380	11201052033	3	2017	11201
381	11201052035	3	2017	11201
382	11201052901	3	2017	11201
383	11201062005	12	2017	11201
384	11201062022	8	2017	11201
385	11201062023	4	2017	11201
386	11201062024	2	2017	11201
387	11201062025	4	2017	11201
388	11201062026	1	2017	11201
389	11201062027	5	2017	11201
390	11201062028	1	2017	11201
391	11201062029	2	2017	11201
392	11201062031	4	2017	11201
393	11201062032	15	2017	11201
394	11201062036	15	2017	11201
395	11201062037	9	2017	11201
396	11201062044	30	2017	11201
397	11201062063	3	2017	11201
398	11201072015	10	2017	11201
399	11201072021	5	2017	11201
400	11201072040	6	2017	11201
401	11201072047	1	2017	11201
402	11201072050	6	2017	11201
403	11201072901	27	2017	11201
561	11202012003	3	2017	11202
562	11202012007	14	2017	11202
563	11202012018	2	2017	11202
564	11202012019	12	2017	11202
565	11202012901	1	2017	11202
566	11202022016	18	2017	11202
567	11202022020	109	2017	11202
568	11202042004	1	2017	11202
569	11202042005	1	2017	11202
570	11202042006	10	2017	11202
571	11202042008	7	2017	11202

2 Variable CASEN

2.1 Tabla de ingresos expandidos

Hemos calculado ya éste valor como conclusión del punto 1.1 de aquí

h_y_m_2017_censo <- readRDS("../corre_ing_exp-censo_casen/Ingresos_expandidos_rural_17.rds")
tablamadre <- head(h_y_m_2017_censo,50)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

código	comuna.x	promedio_i	año	personas	Ingresos_expandidos
01101	Iquique	272529.7	2017	191468	52180713221
01401	Pozo Almonte	243272.4	2017	15711	3822052676
01402	Camiña	226831.0	2017	1250	283538750
01404	Huara	236599.7	2017	2730	645917134
01405	Pica	269198.0	2017	9296	2502464414
02103	Sierra Gorda	322997.9	2017	10186	3290056742
02104	Taltal	288653.8	2017	13317	3844002134
02201	Calama	238080.9	2017	165731	39457387800
02203	San Pedro de Atacama	271472.6	2017	10996	2985112297
02301	Tocopilla	166115.9	2017	25186	4183793832
03101	Copiapó	251396.0	2017	153937	38699138722
03103	Tierra Amarilla	287819.4	2017	14019	4034940816
03202	Diego de Almagro	326439.0	2017	13925	4545663075
03301	Vallenar	217644.6	2017	51917	11299454698
03302	Alto del Carmen	196109.9	2017	5299	1039186477
03303	Freirina	202463.8	2017	7041	1425547554
03304	Huasco	205839.6	2017	10149	2089066548
04101	La Serena	200287.4	2017	221054	44274327972
04102	Coquimbo	206027.8	2017	227730	46918711304
04103	Andacollo	217096.4	2017	11044	2397612293
04104	La Higuera	231674.2	2017	4241	982530309
04105	Paiguano	174868.5	2017	4497	786383423
04106	Vicuña	169077.1	2017	27771	4695441470
04201	Illapel	165639.6	2017	30848	5109649759
04202	Canela	171370.3	2017	9093	1558270441
04203	Los Vilos	173238.5	2017	21382	3704185607
04204	Salamanca	193602.0	2017	29347	5681637894
04301	Ovalle	230819.8	2017	111272	25683781418
04302	Combarbalá	172709.2	2017	13322	2300832587
04303	Monte Patria	189761.6	2017	30751	5835357638
04304	Punitaqui	165862.0	2017	10956	1817183694
04305	Río Hurtado	182027.2	2017	4278	778712384
05101	Valparaíso	251998.5	2017	296655	74756602991
05102	Casablanca	252317.7	2017	26867	6779018483
05105	Puchuncaví	231606.0	2017	18546	4295363979
05107	Quintero	285125.8	2017	31923	9102071069
05301	Los Andes	280548.0	2017	66708	18714795984
05302	Calle Larga	234044.6	2017	14832	3471349123
05303	Rinconada	246136.9	2017	10207	2512319225
05304	San Esteban	211907.3	2017	18855	3995512770
05401	La Ligua	172675.9	2017	35390	6111000517
05402	Cabildo	212985.0	2017	19388	4129354103
05404	Petorca	270139.8	2017	9826	2654393853
05405	Zapallar	235661.4	2017	7339	1729518700
05501	Quillota	212067.6	2017	90517	19195726144
05502	Calera	226906.2	2017	50554	11471016698
05503	Hijuelas	215402.0	2017	17988	3874650405
05504	La Cruz	243333.4	2017	22098	5377180726
05506	Nogales	219800.7	2017	22120	4861992055
05601	San Antonio	230261.5	2017	91350	21034388728

3 Unión Censo-Casen

Integramos a la tabla censal de frecuencias la tabla de ingresos expandidos de la Casen.

comunas_con_ing_exp = merge( x = comuna_corr, y = h_y_m_2017_censo, by = "código", all.x = TRUE)

comunas_con_ing_exp <-comunas_con_ing_exp[!(is.na(comunas_con_ing_exp$Ingresos_expandidos)),]

r3_100 <- comunas_con_ing_exp
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	zona	Freq	anio	comuna.x	promedio_i	año	personas	Ingresos_expandidos
1	11101	11101032005	2	2017	Coyhaique	230013.0	2017	57818	13298894369
2	11101	11101052014	11	2017	Coyhaique	230013.0	2017	57818	13298894369
3	11101	11101052039	1	2017	Coyhaique	230013.0	2017	57818	13298894369
4	11101	11101052008	2	2017	Coyhaique	230013.0	2017	57818	13298894369
5	11101	11101062023	4	2017	Coyhaique	230013.0	2017	57818	13298894369
6	11101	11101072014	3	2017	Coyhaique	230013.0	2017	57818	13298894369
7	11101	11101052901	3	2017	Coyhaique	230013.0	2017	57818	13298894369
8	11101	11101062020	4	2017	Coyhaique	230013.0	2017	57818	13298894369
9	11101	11101072020	17	2017	Coyhaique	230013.0	2017	57818	13298894369
10	11101	11101072023	1	2017	Coyhaique	230013.0	2017	57818	13298894369
11	11101	11101072025	1	2017	Coyhaique	230013.0	2017	57818	13298894369
12	11101	11101072035	8	2017	Coyhaique	230013.0	2017	57818	13298894369
13	11101	11101072036	35	2017	Coyhaique	230013.0	2017	57818	13298894369
14	11101	11101072037	10	2017	Coyhaique	230013.0	2017	57818	13298894369
15	11101	11101092002	12	2017	Coyhaique	230013.0	2017	57818	13298894369
16	11101	11101092007	1	2017	Coyhaique	230013.0	2017	57818	13298894369
17	11101	11101092901	3	2017	Coyhaique	230013.0	2017	57818	13298894369
18	11101	11101102011	35	2017	Coyhaique	230013.0	2017	57818	13298894369
19	11101	11101102020	70	2017	Coyhaique	230013.0	2017	57818	13298894369
20	11101	11101072018	156	2017	Coyhaique	230013.0	2017	57818	13298894369
21	11101	11101072019	9	2017	Coyhaique	230013.0	2017	57818	13298894369
22	11101	11101112009	62	2017	Coyhaique	230013.0	2017	57818	13298894369
23	11101	11101112011	189	2017	Coyhaique	230013.0	2017	57818	13298894369
24	11101	11101112012	5	2017	Coyhaique	230013.0	2017	57818	13298894369
25	11101	11101112013	181	2017	Coyhaique	230013.0	2017	57818	13298894369
26	11101	11101112029	23	2017	Coyhaique	230013.0	2017	57818	13298894369
27	11101	11101122011	52	2017	Coyhaique	230013.0	2017	57818	13298894369
28	11101	11101132011	10	2017	Coyhaique	230013.0	2017	57818	13298894369
29	11101	11101022003	2	2017	Coyhaique	230013.0	2017	57818	13298894369
30	11101	11101022010	2	2017	Coyhaique	230013.0	2017	57818	13298894369
31	11101	11101022031	3	2017	Coyhaique	230013.0	2017	57818	13298894369
32	11101	11101022038	9	2017	Coyhaique	230013.0	2017	57818	13298894369
33	11101	11101042024	3	2017	Coyhaique	230013.0	2017	57818	13298894369
34	11101	11101032015	1	2017	Coyhaique	230013.0	2017	57818	13298894369
35	11101	11101032016	1	2017	Coyhaique	230013.0	2017	57818	13298894369
36	11101	11101032032	9	2017	Coyhaique	230013.0	2017	57818	13298894369
37	11101	11101042027	4	2017	Coyhaique	230013.0	2017	57818	13298894369
38	11101	11101052004	28	2017	Coyhaique	230013.0	2017	57818	13298894369
39	11101	11101112001	26	2017	Coyhaique	230013.0	2017	57818	13298894369
40	11101	11101102033	192	2017	Coyhaique	230013.0	2017	57818	13298894369
45	11201	11201012009	9	2017	Aysén	246614.5	2017	23959	5908637554
46	11201	11201022057	4	2017	Aysén	246614.5	2017	23959	5908637554
47	11201	11201022058	4	2017	Aysén	246614.5	2017	23959	5908637554
48	11201	11201022008	1	2017	Aysén	246614.5	2017	23959	5908637554
49	11201	11201032012	2	2017	Aysén	246614.5	2017	23959	5908637554
50	11201	11201032019	1	2017	Aysén	246614.5	2017	23959	5908637554
51	11201	11201022901	1	2017	Aysén	246614.5	2017	23959	5908637554
52	11201	11201032010	2	2017	Aysén	246614.5	2017	23959	5908637554
53	11201	11201042006	36	2017	Aysén	246614.5	2017	23959	5908637554
54	11201	11201042007	15	2017	Aysén	246614.5	2017	23959	5908637554
55	11201	11201042065	1	2017	Aysén	246614.5	2017	23959	5908637554
56	11201	11201052003	2	2017	Aysén	246614.5	2017	23959	5908637554
57	11201	11201052020	8	2017	Aysén	246614.5	2017	23959	5908637554
58	11201	11201052033	3	2017	Aysén	246614.5	2017	23959	5908637554
59	11201	11201052035	3	2017	Aysén	246614.5	2017	23959	5908637554
60	11201	11201052901	3	2017	Aysén	246614.5	2017	23959	5908637554
61	11201	11201062005	12	2017	Aysén	246614.5	2017	23959	5908637554
62	11201	11201062022	8	2017	Aysén	246614.5	2017	23959	5908637554
63	11201	11201062023	4	2017	Aysén	246614.5	2017	23959	5908637554
64	11201	11201032038	3	2017	Aysén	246614.5	2017	23959	5908637554
65	11201	11201032068	11	2017	Aysén	246614.5	2017	23959	5908637554
66	11201	11201062026	1	2017	Aysén	246614.5	2017	23959	5908637554
67	11201	11201062027	5	2017	Aysén	246614.5	2017	23959	5908637554
68	11201	11201062028	1	2017	Aysén	246614.5	2017	23959	5908637554
69	11201	11201062029	2	2017	Aysén	246614.5	2017	23959	5908637554
70	11201	11201062031	4	2017	Aysén	246614.5	2017	23959	5908637554
71	11201	11201062032	15	2017	Aysén	246614.5	2017	23959	5908637554
72	11201	11201062036	15	2017	Aysén	246614.5	2017	23959	5908637554
73	11201	11201062037	9	2017	Aysén	246614.5	2017	23959	5908637554
74	11201	11201062044	30	2017	Aysén	246614.5	2017	23959	5908637554
75	11201	11201062063	3	2017	Aysén	246614.5	2017	23959	5908637554
76	11201	11201072015	10	2017	Aysén	246614.5	2017	23959	5908637554
77	11201	11201072021	5	2017	Aysén	246614.5	2017	23959	5908637554
78	11201	11201012013	2	2017	Aysén	246614.5	2017	23959	5908637554
79	11201	11201012055	38	2017	Aysén	246614.5	2017	23959	5908637554
80	11201	11201012060	13	2017	Aysén	246614.5	2017	23959	5908637554
81	11201	11201012066	3	2017	Aysén	246614.5	2017	23959	5908637554
82	11201	11201012067	15	2017	Aysén	246614.5	2017	23959	5908637554
83	11201	11201012901	22	2017	Aysén	246614.5	2017	23959	5908637554
84	11201	11201062025	4	2017	Aysén	246614.5	2017	23959	5908637554
85	11201	11201072047	1	2017	Aysén	246614.5	2017	23959	5908637554
86	11201	11201072050	6	2017	Aysén	246614.5	2017	23959	5908637554
87	11201	11201062024	2	2017	Aysén	246614.5	2017	23959	5908637554
88	11201	11201072040	6	2017	Aysén	246614.5	2017	23959	5908637554
89	11201	11201072901	27	2017	Aysén	246614.5	2017	23959	5908637554
90	11202	11202012018	2	2017	Cisnes	262412.7	2017	6517	1710143349
91	11202	11202012901	1	2017	Cisnes	262412.7	2017	6517	1710143349
92	11202	11202022020	109	2017	Cisnes	262412.7	2017	6517	1710143349
93	11202	11202012019	12	2017	Cisnes	262412.7	2017	6517	1710143349
94	11202	11202042005	1	2017	Cisnes	262412.7	2017	6517	1710143349
95	11202	11202022016	18	2017	Cisnes	262412.7	2017	6517	1710143349
96	11202	11202042013	1	2017	Cisnes	262412.7	2017	6517	1710143349
97	11202	11202042004	1	2017	Cisnes	262412.7	2017	6517	1710143349
98	11202	11202052002	1	2017	Cisnes	262412.7	2017	6517	1710143349
99	11202	11202052017	12	2017	Cisnes	262412.7	2017	6517	1710143349
100	11202	11202052021	22	2017	Cisnes	262412.7	2017	6517	1710143349
101	11202	11202042014	7	2017	Cisnes	262412.7	2017	6517	1710143349
102	11202	11202052901	7	2017	Cisnes	262412.7	2017	6517	1710143349
103	11202	11202062010	1	2017	Cisnes	262412.7	2017	6517	1710143349
104	11202	11202042006	10	2017	Cisnes	262412.7	2017	6517	1710143349
105	11202	11202052023	11	2017	Cisnes	262412.7	2017	6517	1710143349
106	11202	11202042009	2	2017	Cisnes	262412.7	2017	6517	1710143349
107	11202	11202042011	3	2017	Cisnes	262412.7	2017	6517	1710143349
108	11202	11202042012	3	2017	Cisnes	262412.7	2017	6517	1710143349
109	11202	11202012003	3	2017	Cisnes	262412.7	2017	6517	1710143349
110	11202	11202012007	14	2017	Cisnes	262412.7	2017	6517	1710143349
111	11202	11202042008	7	2017	Cisnes	262412.7	2017	6517	1710143349
114	11301	11301022008	1	2017	Cochrane	211652.6	2017	3490	738667487
115	11301	11301022009	2	2017	Cochrane	211652.6	2017	3490	738667487
116	11301	11301022014	2	2017	Cochrane	211652.6	2017	3490	738667487
117	11301	11301022901	2	2017	Cochrane	211652.6	2017	3490	738667487
118	11301	11301032005	1	2017	Cochrane	211652.6	2017	3490	738667487
119	11301	11301042015	1	2017	Cochrane	211652.6	2017	3490	738667487
120	11301	11301022016	5	2017	Cochrane	211652.6	2017	3490	738667487
121	11301	11301012002	6	2017	Cochrane	211652.6	2017	3490	738667487
122	11301	11301012003	17	2017	Cochrane	211652.6	2017	3490	738667487
123	11301	11301012004	18	2017	Cochrane	211652.6	2017	3490	738667487
124	11301	11301012018	18	2017	Cochrane	211652.6	2017	3490	738667487
125	11301	11301012901	1	2017	Cochrane	211652.6	2017	3490	738667487
132	11401	11401012001	4	2017	Chile Chico	188913.8	2017	4865	919065674
133	11401	11401012002	87	2017	Chile Chico	188913.8	2017	4865	919065674
134	11401	11401012901	17	2017	Chile Chico	188913.8	2017	4865	919065674
135	11401	11401022008	13	2017	Chile Chico	188913.8	2017	4865	919065674
136	11401	11401022010	59	2017	Chile Chico	188913.8	2017	4865	919065674
137	11401	11401032005	4	2017	Chile Chico	188913.8	2017	4865	919065674
138	11401	11401032006	2	2017	Chile Chico	188913.8	2017	4865	919065674
139	11401	11401032009	19	2017	Chile Chico	188913.8	2017	4865	919065674
140	11401	11401042011	2	2017	Chile Chico	188913.8	2017	4865	919065674
141	11402	11402012004	3	2017	Río Ibáñez	171315.6	2017	2666	456727447
142	11402	11402012007	61	2017	Río Ibáñez	171315.6	2017	2666	456727447
143	11402	11402012009	1	2017	Río Ibáñez	171315.6	2017	2666	456727447
144	11402	11402012019	77	2017	Río Ibáñez	171315.6	2017	2666	456727447
145	11402	11402012901	1	2017	Río Ibáñez	171315.6	2017	2666	456727447
146	11402	11402032013	2	2017	Río Ibáñez	171315.6	2017	2666	456727447
147	11402	11402032023	2	2017	Río Ibáñez	171315.6	2017	2666	456727447
148	11402	11402032025	1	2017	Río Ibáñez	171315.6	2017	2666	456727447
149	11402	11402032901	2	2017	Río Ibáñez	171315.6	2017	2666	456727447
150	11402	11402052021	5	2017	Río Ibáñez	171315.6	2017	2666	456727447
151	11402	11402052901	1	2017	Río Ibáñez	171315.6	2017	2666	456727447
152	11402	11402062015	2	2017	Río Ibáñez	171315.6	2017	2666	456727447
153	11402	11402062020	57	2017	Río Ibáñez	171315.6	2017	2666	456727447
154	11402	11402062024	1	2017	Río Ibáñez	171315.6	2017	2666	456727447
155	11402	11402062026	2	2017	Río Ibáñez	171315.6	2017	2666	456727447
156	11402	11402072003	18	2017	Río Ibáñez	171315.6	2017	2666	456727447
157	11402	11402992999	2	2017	Río Ibáñez	171315.6	2017	2666	456727447

4 Proporción poblacional zonal respecto a la comunal

Del censo obtenemos la cantidad de población a nivel de zona y estimamos su proporción a nivel comunal. Ya hemos calculado ésta proporción aquí.

prop_pob <- readRDS("../tabla_de_prop_pob.rds")
names(prop_pob)[1] <- "zona"
names(prop_pob)[3] <- "p_poblacional"

Veamos los 100 primeros registros:

r3_100 <- prop_pob[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	Freq	p_poblacional	código
1101011001	2491	0.0130100	01101
1101011002	1475	0.0077036	01101
1101021001	1003	0.0052385	01101
1101021002	54	0.0002820	01101
1101021003	2895	0.0151200	01101
1101021004	2398	0.0125243	01101
1101021005	4525	0.0236332	01101
1101031001	2725	0.0142321	01101
1101031002	3554	0.0185618	01101
1101031003	5246	0.0273988	01101
1101031004	3389	0.0177001	01101
1101041001	1800	0.0094010	01101
1101041002	2538	0.0132555	01101
1101041003	3855	0.0201339	01101
1101041004	5663	0.0295767	01101
1101041005	4162	0.0217373	01101
1101041006	2689	0.0140441	01101
1101051001	3296	0.0172144	01101
1101051002	4465	0.0233198	01101
1101051003	4656	0.0243174	01101
1101051004	2097	0.0109522	01101
1101051005	3569	0.0186402	01101
1101051006	2741	0.0143157	01101
1101061001	1625	0.0084871	01101
1101061002	4767	0.0248971	01101
1101061003	4826	0.0252053	01101
1101061004	4077	0.0212934	01101
1101061005	2166	0.0113126	01101
1101071001	2324	0.0121378	01101
1101071002	2801	0.0146291	01101
1101071003	3829	0.0199981	01101
1101071004	1987	0.0103777	01101
1101081001	5133	0.0268087	01101
1101081002	3233	0.0168853	01101
1101081003	2122	0.0110828	01101
1101081004	2392	0.0124929	01101
1101092001	57	0.0002977	01101
1101092004	247	0.0012900	01101
1101092005	76	0.0003969	01101
1101092006	603	0.0031494	01101
1101092007	84	0.0004387	01101
1101092010	398	0.0020787	01101
1101092012	58	0.0003029	01101
1101092014	23	0.0001201	01101
1101092016	20	0.0001045	01101
1101092017	8	0.0000418	01101
1101092018	74	0.0003865	01101
1101092019	25	0.0001306	01101
1101092021	177	0.0009244	01101
1101092022	23	0.0001201	01101
1101092023	288	0.0015042	01101
1101092024	14	0.0000731	01101
1101092901	30	0.0001567	01101
1101101001	2672	0.0139553	01101
1101101002	4398	0.0229699	01101
1101101003	4524	0.0236280	01101
1101101004	3544	0.0185096	01101
1101101005	4911	0.0256492	01101
1101101006	3688	0.0192617	01101
1101111001	3886	0.0202958	01101
1101111002	2312	0.0120751	01101
1101111003	4874	0.0254560	01101
1101111004	4543	0.0237272	01101
1101111005	4331	0.0226200	01101
1101111006	3253	0.0169898	01101
1101111007	4639	0.0242286	01101
1101111008	4881	0.0254925	01101
1101111009	5006	0.0261454	01101
1101111010	366	0.0019115	01101
1101111011	4351	0.0227244	01101
1101111012	2926	0.0152819	01101
1101111013	3390	0.0177053	01101
1101111014	2940	0.0153550	01101
1101112003	33	0.0001724	01101
1101112013	104	0.0005432	01101
1101112019	34	0.0001776	01101
1101112025	21	0.0001097	01101
1101112901	6	0.0000313	01101
1101991999	1062	0.0055466	01101
1107011001	4104	0.0378685	01107
1107011002	4360	0.0402307	01107
1107011003	8549	0.0788835	01107
1107012003	3	0.0000277	01107
1107012901	17	0.0001569	01107
1107021001	6701	0.0618316	01107
1107021002	3971	0.0366413	01107
1107021003	6349	0.0585836	01107
1107021004	5125	0.0472895	01107
1107021005	4451	0.0410704	01107
1107021006	3864	0.0356540	01107
1107021007	5235	0.0483045	01107
1107021008	4566	0.0421315	01107
1107031001	4195	0.0387082	01107
1107031002	7099	0.0655040	01107
1107031003	4720	0.0435525	01107
1107032005	38	0.0003506	01107
1107032006	2399	0.0221361	01107
1107032008	4	0.0000369	01107
1107041001	3630	0.0334948	01107
1107041002	5358	0.0494394	01107

5 Ingreso medio

Deseamos el valor del ingreso promedio a nivel comunal, pero expandido a nivel zonal. Ésta información está contenida en el campo promedio_i de la tabla obtenida en el punto 3.

r3_100 <- comunas_con_ing_exp[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	código	zona	Freq	anio	comuna.x	promedio_i	año	personas	Ingresos_expandidos
1	11101	11101032005	2	2017	Coyhaique	230013.0	2017	57818	13298894369
2	11101	11101052014	11	2017	Coyhaique	230013.0	2017	57818	13298894369
3	11101	11101052039	1	2017	Coyhaique	230013.0	2017	57818	13298894369
4	11101	11101052008	2	2017	Coyhaique	230013.0	2017	57818	13298894369
5	11101	11101062023	4	2017	Coyhaique	230013.0	2017	57818	13298894369
6	11101	11101072014	3	2017	Coyhaique	230013.0	2017	57818	13298894369
7	11101	11101052901	3	2017	Coyhaique	230013.0	2017	57818	13298894369
8	11101	11101062020	4	2017	Coyhaique	230013.0	2017	57818	13298894369
9	11101	11101072020	17	2017	Coyhaique	230013.0	2017	57818	13298894369
10	11101	11101072023	1	2017	Coyhaique	230013.0	2017	57818	13298894369
11	11101	11101072025	1	2017	Coyhaique	230013.0	2017	57818	13298894369
12	11101	11101072035	8	2017	Coyhaique	230013.0	2017	57818	13298894369
13	11101	11101072036	35	2017	Coyhaique	230013.0	2017	57818	13298894369
14	11101	11101072037	10	2017	Coyhaique	230013.0	2017	57818	13298894369
15	11101	11101092002	12	2017	Coyhaique	230013.0	2017	57818	13298894369
16	11101	11101092007	1	2017	Coyhaique	230013.0	2017	57818	13298894369
17	11101	11101092901	3	2017	Coyhaique	230013.0	2017	57818	13298894369
18	11101	11101102011	35	2017	Coyhaique	230013.0	2017	57818	13298894369
19	11101	11101102020	70	2017	Coyhaique	230013.0	2017	57818	13298894369
20	11101	11101072018	156	2017	Coyhaique	230013.0	2017	57818	13298894369
21	11101	11101072019	9	2017	Coyhaique	230013.0	2017	57818	13298894369
22	11101	11101112009	62	2017	Coyhaique	230013.0	2017	57818	13298894369
23	11101	11101112011	189	2017	Coyhaique	230013.0	2017	57818	13298894369
24	11101	11101112012	5	2017	Coyhaique	230013.0	2017	57818	13298894369
25	11101	11101112013	181	2017	Coyhaique	230013.0	2017	57818	13298894369
26	11101	11101112029	23	2017	Coyhaique	230013.0	2017	57818	13298894369
27	11101	11101122011	52	2017	Coyhaique	230013.0	2017	57818	13298894369
28	11101	11101132011	10	2017	Coyhaique	230013.0	2017	57818	13298894369
29	11101	11101022003	2	2017	Coyhaique	230013.0	2017	57818	13298894369
30	11101	11101022010	2	2017	Coyhaique	230013.0	2017	57818	13298894369
31	11101	11101022031	3	2017	Coyhaique	230013.0	2017	57818	13298894369
32	11101	11101022038	9	2017	Coyhaique	230013.0	2017	57818	13298894369
33	11101	11101042024	3	2017	Coyhaique	230013.0	2017	57818	13298894369
34	11101	11101032015	1	2017	Coyhaique	230013.0	2017	57818	13298894369
35	11101	11101032016	1	2017	Coyhaique	230013.0	2017	57818	13298894369
36	11101	11101032032	9	2017	Coyhaique	230013.0	2017	57818	13298894369
37	11101	11101042027	4	2017	Coyhaique	230013.0	2017	57818	13298894369
38	11101	11101052004	28	2017	Coyhaique	230013.0	2017	57818	13298894369
39	11101	11101112001	26	2017	Coyhaique	230013.0	2017	57818	13298894369
40	11101	11101102033	192	2017	Coyhaique	230013.0	2017	57818	13298894369
45	11201	11201012009	9	2017	Aysén	246614.5	2017	23959	5908637554
46	11201	11201022057	4	2017	Aysén	246614.5	2017	23959	5908637554
47	11201	11201022058	4	2017	Aysén	246614.5	2017	23959	5908637554
48	11201	11201022008	1	2017	Aysén	246614.5	2017	23959	5908637554
49	11201	11201032012	2	2017	Aysén	246614.5	2017	23959	5908637554
50	11201	11201032019	1	2017	Aysén	246614.5	2017	23959	5908637554
51	11201	11201022901	1	2017	Aysén	246614.5	2017	23959	5908637554
52	11201	11201032010	2	2017	Aysén	246614.5	2017	23959	5908637554
53	11201	11201042006	36	2017	Aysén	246614.5	2017	23959	5908637554
54	11201	11201042007	15	2017	Aysén	246614.5	2017	23959	5908637554
55	11201	11201042065	1	2017	Aysén	246614.5	2017	23959	5908637554
56	11201	11201052003	2	2017	Aysén	246614.5	2017	23959	5908637554
57	11201	11201052020	8	2017	Aysén	246614.5	2017	23959	5908637554
58	11201	11201052033	3	2017	Aysén	246614.5	2017	23959	5908637554
59	11201	11201052035	3	2017	Aysén	246614.5	2017	23959	5908637554
60	11201	11201052901	3	2017	Aysén	246614.5	2017	23959	5908637554
61	11201	11201062005	12	2017	Aysén	246614.5	2017	23959	5908637554
62	11201	11201062022	8	2017	Aysén	246614.5	2017	23959	5908637554
63	11201	11201062023	4	2017	Aysén	246614.5	2017	23959	5908637554
64	11201	11201032038	3	2017	Aysén	246614.5	2017	23959	5908637554
65	11201	11201032068	11	2017	Aysén	246614.5	2017	23959	5908637554
66	11201	11201062026	1	2017	Aysén	246614.5	2017	23959	5908637554
67	11201	11201062027	5	2017	Aysén	246614.5	2017	23959	5908637554
68	11201	11201062028	1	2017	Aysén	246614.5	2017	23959	5908637554
69	11201	11201062029	2	2017	Aysén	246614.5	2017	23959	5908637554
70	11201	11201062031	4	2017	Aysén	246614.5	2017	23959	5908637554
71	11201	11201062032	15	2017	Aysén	246614.5	2017	23959	5908637554
72	11201	11201062036	15	2017	Aysén	246614.5	2017	23959	5908637554
73	11201	11201062037	9	2017	Aysén	246614.5	2017	23959	5908637554
74	11201	11201062044	30	2017	Aysén	246614.5	2017	23959	5908637554
75	11201	11201062063	3	2017	Aysén	246614.5	2017	23959	5908637554
76	11201	11201072015	10	2017	Aysén	246614.5	2017	23959	5908637554
77	11201	11201072021	5	2017	Aysén	246614.5	2017	23959	5908637554
78	11201	11201012013	2	2017	Aysén	246614.5	2017	23959	5908637554
79	11201	11201012055	38	2017	Aysén	246614.5	2017	23959	5908637554
80	11201	11201012060	13	2017	Aysén	246614.5	2017	23959	5908637554
81	11201	11201012066	3	2017	Aysén	246614.5	2017	23959	5908637554
82	11201	11201012067	15	2017	Aysén	246614.5	2017	23959	5908637554
83	11201	11201012901	22	2017	Aysén	246614.5	2017	23959	5908637554
84	11201	11201062025	4	2017	Aysén	246614.5	2017	23959	5908637554
85	11201	11201072047	1	2017	Aysén	246614.5	2017	23959	5908637554
86	11201	11201072050	6	2017	Aysén	246614.5	2017	23959	5908637554
87	11201	11201062024	2	2017	Aysén	246614.5	2017	23959	5908637554
88	11201	11201072040	6	2017	Aysén	246614.5	2017	23959	5908637554
89	11201	11201072901	27	2017	Aysén	246614.5	2017	23959	5908637554
90	11202	11202012018	2	2017	Cisnes	262412.7	2017	6517	1710143349
91	11202	11202012901	1	2017	Cisnes	262412.7	2017	6517	1710143349
92	11202	11202022020	109	2017	Cisnes	262412.7	2017	6517	1710143349
93	11202	11202012019	12	2017	Cisnes	262412.7	2017	6517	1710143349
94	11202	11202042005	1	2017	Cisnes	262412.7	2017	6517	1710143349
95	11202	11202022016	18	2017	Cisnes	262412.7	2017	6517	1710143349
96	11202	11202042013	1	2017	Cisnes	262412.7	2017	6517	1710143349
97	11202	11202042004	1	2017	Cisnes	262412.7	2017	6517	1710143349
98	11202	11202052002	1	2017	Cisnes	262412.7	2017	6517	1710143349
99	11202	11202052017	12	2017	Cisnes	262412.7	2017	6517	1710143349
100	11202	11202052021	22	2017	Cisnes	262412.7	2017	6517	1710143349
101	11202	11202042014	7	2017	Cisnes	262412.7	2017	6517	1710143349
102	11202	11202052901	7	2017	Cisnes	262412.7	2017	6517	1710143349
103	11202	11202062010	1	2017	Cisnes	262412.7	2017	6517	1710143349
104	11202	11202042006	10	2017	Cisnes	262412.7	2017	6517	1710143349

6 Ingreso promedio expandido por zona (multi_pob)

En éste momento vamos a construir nuestra variable dependiente de regresión aplicando la siguiente fórmula:

\[ multi\_pob = promedio\_i \cdot personas \cdot p\_poblacional \]

Para ello integramos a la tabla de ingresos expandidos a nivel zonal (punto 3) la tabla de proporciones poblacionales zonales respecto al total comunal (punto 4) :

h_y_m_comuna_corr_01 = merge( x = comunas_con_ing_exp, y = prop_pob, by = "zona", all.x = TRUE)
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	personas	Ingresos_expandidos	Freq.y	p_poblacional	código.y
11101022003	11101	2	2017	Coyhaique	230013.0	2017	57818	13298894369	11	0.0001903	11101
11101022010	11101	2	2017	Coyhaique	230013.0	2017	57818	13298894369	53	0.0009167	11101
11101022031	11101	3	2017	Coyhaique	230013.0	2017	57818	13298894369	55	0.0009513	11101
11101022038	11101	9	2017	Coyhaique	230013.0	2017	57818	13298894369	339	0.0058632	11101
11101032005	11101	2	2017	Coyhaique	230013.0	2017	57818	13298894369	53	0.0009167	11101
11101032015	11101	1	2017	Coyhaique	230013.0	2017	57818	13298894369	90	0.0015566	11101
11101032016	11101	1	2017	Coyhaique	230013.0	2017	57818	13298894369	16	0.0002767	11101
11101032032	11101	9	2017	Coyhaique	230013.0	2017	57818	13298894369	257	0.0044450	11101
11101042024	11101	3	2017	Coyhaique	230013.0	2017	57818	13298894369	47	0.0008129	11101
11101042027	11101	4	2017	Coyhaique	230013.0	2017	57818	13298894369	77	0.0013318	11101
11101052004	11101	28	2017	Coyhaique	230013.0	2017	57818	13298894369	474	0.0081981	11101
11101052008	11101	2	2017	Coyhaique	230013.0	2017	57818	13298894369	76	0.0013145	11101
11101052014	11101	11	2017	Coyhaique	230013.0	2017	57818	13298894369	250	0.0043239	11101
11101052039	11101	1	2017	Coyhaique	230013.0	2017	57818	13298894369	62	0.0010723	11101
11101052901	11101	3	2017	Coyhaique	230013.0	2017	57818	13298894369	50	0.0008648	11101
11101062020	11101	4	2017	Coyhaique	230013.0	2017	57818	13298894369	37	0.0006399	11101
11101062023	11101	4	2017	Coyhaique	230013.0	2017	57818	13298894369	54	0.0009340	11101
11101072014	11101	3	2017	Coyhaique	230013.0	2017	57818	13298894369	107	0.0018506	11101
11101072018	11101	156	2017	Coyhaique	230013.0	2017	57818	13298894369	806	0.0139403	11101
11101072019	11101	9	2017	Coyhaique	230013.0	2017	57818	13298894369	102	0.0017642	11101
11101072020	11101	17	2017	Coyhaique	230013.0	2017	57818	13298894369	190	0.0032862	11101
11101072023	11101	1	2017	Coyhaique	230013.0	2017	57818	13298894369	9	0.0001557	11101
11101072025	11101	1	2017	Coyhaique	230013.0	2017	57818	13298894369	15	0.0002594	11101
11101072035	11101	8	2017	Coyhaique	230013.0	2017	57818	13298894369	109	0.0018852	11101
11101072036	11101	35	2017	Coyhaique	230013.0	2017	57818	13298894369	539	0.0093224	11101
11101072037	11101	10	2017	Coyhaique	230013.0	2017	57818	13298894369	265	0.0045833	11101
11101092002	11101	12	2017	Coyhaique	230013.0	2017	57818	13298894369	92	0.0015912	11101
11101092007	11101	1	2017	Coyhaique	230013.0	2017	57818	13298894369	29	0.0005016	11101
11101092901	11101	3	2017	Coyhaique	230013.0	2017	57818	13298894369	39	0.0006745	11101
11101102011	11101	35	2017	Coyhaique	230013.0	2017	57818	13298894369	143	0.0024733	11101
11101102020	11101	70	2017	Coyhaique	230013.0	2017	57818	13298894369	250	0.0043239	11101
11101102033	11101	192	2017	Coyhaique	230013.0	2017	57818	13298894369	763	0.0131966	11101
11101112001	11101	26	2017	Coyhaique	230013.0	2017	57818	13298894369	186	0.0032170	11101
11101112009	11101	62	2017	Coyhaique	230013.0	2017	57818	13298894369	198	0.0034245	11101
11101112011	11101	189	2017	Coyhaique	230013.0	2017	57818	13298894369	579	0.0100142	11101
11101112012	11101	5	2017	Coyhaique	230013.0	2017	57818	13298894369	54	0.0009340	11101
11101112013	11101	181	2017	Coyhaique	230013.0	2017	57818	13298894369	615	0.0106368	11101
11101112029	11101	23	2017	Coyhaique	230013.0	2017	57818	13298894369	304	0.0052579	11101
11101122011	11101	52	2017	Coyhaique	230013.0	2017	57818	13298894369	206	0.0035629	11101
11101132011	11101	10	2017	Coyhaique	230013.0	2017	57818	13298894369	44	0.0007610	11101
11201012009	11201	9	2017	Aysén	246614.5	2017	23959	5908637554	52	0.0021704	11201
11201012013	11201	2	2017	Aysén	246614.5	2017	23959	5908637554	35	0.0014608	11201
11201012055	11201	38	2017	Aysén	246614.5	2017	23959	5908637554	148	0.0061772	11201
11201012060	11201	13	2017	Aysén	246614.5	2017	23959	5908637554	72	0.0030051	11201
11201012066	11201	3	2017	Aysén	246614.5	2017	23959	5908637554	37	0.0015443	11201
11201012067	11201	15	2017	Aysén	246614.5	2017	23959	5908637554	98	0.0040903	11201
11201012901	11201	22	2017	Aysén	246614.5	2017	23959	5908637554	172	0.0071789	11201
11201022008	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	20	0.0008348	11201
11201022057	11201	4	2017	Aysén	246614.5	2017	23959	5908637554	58	0.0024208	11201
11201022058	11201	4	2017	Aysén	246614.5	2017	23959	5908637554	50	0.0020869	11201
11201022901	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	16	0.0006678	11201
11201032010	11201	2	2017	Aysén	246614.5	2017	23959	5908637554	29	0.0012104	11201
11201032012	11201	2	2017	Aysén	246614.5	2017	23959	5908637554	56	0.0023373	11201
11201032019	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	8	0.0003339	11201
11201032038	11201	3	2017	Aysén	246614.5	2017	23959	5908637554	41	0.0017113	11201
11201032068	11201	11	2017	Aysén	246614.5	2017	23959	5908637554	149	0.0062190	11201
11201042006	11201	36	2017	Aysén	246614.5	2017	23959	5908637554	244	0.0101841	11201
11201042007	11201	15	2017	Aysén	246614.5	2017	23959	5908637554	54	0.0022539	11201
11201042065	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	13	0.0005426	11201
11201052003	11201	2	2017	Aysén	246614.5	2017	23959	5908637554	14	0.0005843	11201
11201052020	11201	8	2017	Aysén	246614.5	2017	23959	5908637554	78	0.0032556	11201
11201052033	11201	3	2017	Aysén	246614.5	2017	23959	5908637554	15	0.0006261	11201
11201052035	11201	3	2017	Aysén	246614.5	2017	23959	5908637554	15	0.0006261	11201
11201052901	11201	3	2017	Aysén	246614.5	2017	23959	5908637554	14	0.0005843	11201
11201062005	11201	12	2017	Aysén	246614.5	2017	23959	5908637554	229	0.0095580	11201
11201062022	11201	8	2017	Aysén	246614.5	2017	23959	5908637554	21	0.0008765	11201
11201062023	11201	4	2017	Aysén	246614.5	2017	23959	5908637554	31	0.0012939	11201
11201062024	11201	2	2017	Aysén	246614.5	2017	23959	5908637554	18	0.0007513	11201
11201062025	11201	4	2017	Aysén	246614.5	2017	23959	5908637554	38	0.0015860	11201
11201062026	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	6	0.0002504	11201
11201062027	11201	5	2017	Aysén	246614.5	2017	23959	5908637554	37	0.0015443	11201
11201062028	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	11	0.0004591	11201
11201062029	11201	2	2017	Aysén	246614.5	2017	23959	5908637554	20	0.0008348	11201
11201062031	11201	4	2017	Aysén	246614.5	2017	23959	5908637554	68	0.0028382	11201
11201062032	11201	15	2017	Aysén	246614.5	2017	23959	5908637554	155	0.0064694	11201
11201062036	11201	15	2017	Aysén	246614.5	2017	23959	5908637554	112	0.0046747	11201
11201062037	11201	9	2017	Aysén	246614.5	2017	23959	5908637554	79	0.0032973	11201
11201062044	11201	30	2017	Aysén	246614.5	2017	23959	5908637554	541	0.0225802	11201
11201062063	11201	3	2017	Aysén	246614.5	2017	23959	5908637554	47	0.0019617	11201
11201072015	11201	10	2017	Aysén	246614.5	2017	23959	5908637554	41	0.0017113	11201
11201072021	11201	5	2017	Aysén	246614.5	2017	23959	5908637554	23	0.0009600	11201
11201072040	11201	6	2017	Aysén	246614.5	2017	23959	5908637554	38	0.0015860	11201
11201072047	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	17	0.0007095	11201
11201072050	11201	6	2017	Aysén	246614.5	2017	23959	5908637554	39	0.0016278	11201
11201072901	11201	27	2017	Aysén	246614.5	2017	23959	5908637554	208	0.0086815	11201
11202012003	11202	3	2017	Cisnes	262412.7	2017	6517	1710143349	10	0.0015344	11202
11202012007	11202	14	2017	Cisnes	262412.7	2017	6517	1710143349	89	0.0136566	11202
11202012018	11202	2	2017	Cisnes	262412.7	2017	6517	1710143349	5	0.0007672	11202
11202012019	11202	12	2017	Cisnes	262412.7	2017	6517	1710143349	127	0.0194875	11202
11202012901	11202	1	2017	Cisnes	262412.7	2017	6517	1710143349	30	0.0046033	11202
11202022016	11202	18	2017	Cisnes	262412.7	2017	6517	1710143349	280	0.0429646	11202
11202022020	11202	109	2017	Cisnes	262412.7	2017	6517	1710143349	1037	0.1591223	11202
11202042004	11202	1	2017	Cisnes	262412.7	2017	6517	1710143349	36	0.0055240	11202
11202042005	11202	1	2017	Cisnes	262412.7	2017	6517	1710143349	18	0.0027620	11202
11202042006	11202	10	2017	Cisnes	262412.7	2017	6517	1710143349	39	0.0059843	11202
11202042008	11202	7	2017	Cisnes	262412.7	2017	6517	1710143349	27	0.0041430	11202
11202042009	11202	2	2017	Cisnes	262412.7	2017	6517	1710143349	7	0.0010741	11202
11202042011	11202	3	2017	Cisnes	262412.7	2017	6517	1710143349	25	0.0038361	11202
11202042012	11202	3	2017	Cisnes	262412.7	2017	6517	1710143349	39	0.0059843	11202
11202042013	11202	1	2017	Cisnes	262412.7	2017	6517	1710143349	16	0.0024551	11202

Hacemos la multiplicación que queda almacenada en la variable multi_pob:

h_y_m_comuna_corr_01$multi_pob <- h_y_m_comuna_corr_01$promedio_i * h_y_m_comuna_corr_01$personas * h_y_m_comuna_corr_01$p_poblacional
tablamadre <- head(h_y_m_comuna_corr_01,100)
kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	personas	Ingresos_expandidos	Freq.y	p_poblacional	código.y	multi_pob
11101022003	11101	2	2017	Coyhaique	230013.0	2017	57818	13298894369	11	0.0001903	11101	2530144
11101022010	11101	2	2017	Coyhaique	230013.0	2017	57818	13298894369	53	0.0009167	11101	12190692
11101022031	11101	3	2017	Coyhaique	230013.0	2017	57818	13298894369	55	0.0009513	11101	12650718
11101022038	11101	9	2017	Coyhaique	230013.0	2017	57818	13298894369	339	0.0058632	11101	77974423
11101032005	11101	2	2017	Coyhaique	230013.0	2017	57818	13298894369	53	0.0009167	11101	12190692
11101032015	11101	1	2017	Coyhaique	230013.0	2017	57818	13298894369	90	0.0015566	11101	20701174
11101032016	11101	1	2017	Coyhaique	230013.0	2017	57818	13298894369	16	0.0002767	11101	3680209
11101032032	11101	9	2017	Coyhaique	230013.0	2017	57818	13298894369	257	0.0044450	11101	59113353
11101042024	11101	3	2017	Coyhaique	230013.0	2017	57818	13298894369	47	0.0008129	11101	10810613
11101042027	11101	4	2017	Coyhaique	230013.0	2017	57818	13298894369	77	0.0013318	11101	17711005
11101052004	11101	28	2017	Coyhaique	230013.0	2017	57818	13298894369	474	0.0081981	11101	109026184
11101052008	11101	2	2017	Coyhaique	230013.0	2017	57818	13298894369	76	0.0013145	11101	17480992
11101052014	11101	11	2017	Coyhaique	230013.0	2017	57818	13298894369	250	0.0043239	11101	57503262
11101052039	11101	1	2017	Coyhaique	230013.0	2017	57818	13298894369	62	0.0010723	11101	14260809
11101052901	11101	3	2017	Coyhaique	230013.0	2017	57818	13298894369	50	0.0008648	11101	11500652
11101062020	11101	4	2017	Coyhaique	230013.0	2017	57818	13298894369	37	0.0006399	11101	8510483
11101062023	11101	4	2017	Coyhaique	230013.0	2017	57818	13298894369	54	0.0009340	11101	12420705
11101072014	11101	3	2017	Coyhaique	230013.0	2017	57818	13298894369	107	0.0018506	11101	24611396
11101072018	11101	156	2017	Coyhaique	230013.0	2017	57818	13298894369	806	0.0139403	11101	185390516
11101072019	11101	9	2017	Coyhaique	230013.0	2017	57818	13298894369	102	0.0017642	11101	23461331
11101072020	11101	17	2017	Coyhaique	230013.0	2017	57818	13298894369	190	0.0032862	11101	43702479
11101072023	11101	1	2017	Coyhaique	230013.0	2017	57818	13298894369	9	0.0001557	11101	2070117
11101072025	11101	1	2017	Coyhaique	230013.0	2017	57818	13298894369	15	0.0002594	11101	3450196
11101072035	11101	8	2017	Coyhaique	230013.0	2017	57818	13298894369	109	0.0018852	11101	25071422
11101072036	11101	35	2017	Coyhaique	230013.0	2017	57818	13298894369	539	0.0093224	11101	123977032
11101072037	11101	10	2017	Coyhaique	230013.0	2017	57818	13298894369	265	0.0045833	11101	60953458
11101092002	11101	12	2017	Coyhaique	230013.0	2017	57818	13298894369	92	0.0015912	11101	21161200
11101092007	11101	1	2017	Coyhaique	230013.0	2017	57818	13298894369	29	0.0005016	11101	6670378
11101092901	11101	3	2017	Coyhaique	230013.0	2017	57818	13298894369	39	0.0006745	11101	8970509
11101102011	11101	35	2017	Coyhaique	230013.0	2017	57818	13298894369	143	0.0024733	11101	32891866
11101102020	11101	70	2017	Coyhaique	230013.0	2017	57818	13298894369	250	0.0043239	11101	57503262
11101102033	11101	192	2017	Coyhaique	230013.0	2017	57818	13298894369	763	0.0131966	11101	175499955
11101112001	11101	26	2017	Coyhaique	230013.0	2017	57818	13298894369	186	0.0032170	11101	42782427
11101112009	11101	62	2017	Coyhaique	230013.0	2017	57818	13298894369	198	0.0034245	11101	45542583
11101112011	11101	189	2017	Coyhaique	230013.0	2017	57818	13298894369	579	0.0100142	11101	133177554
11101112012	11101	5	2017	Coyhaique	230013.0	2017	57818	13298894369	54	0.0009340	11101	12420705
11101112013	11101	181	2017	Coyhaique	230013.0	2017	57818	13298894369	615	0.0106368	11101	141458024
11101112029	11101	23	2017	Coyhaique	230013.0	2017	57818	13298894369	304	0.0052579	11101	69923966
11101122011	11101	52	2017	Coyhaique	230013.0	2017	57818	13298894369	206	0.0035629	11101	47382688
11101132011	11101	10	2017	Coyhaique	230013.0	2017	57818	13298894369	44	0.0007610	11101	10120574
11201012009	11201	9	2017	Aysén	246614.5	2017	23959	5908637554	52	0.0021704	11201	12823956
11201012013	11201	2	2017	Aysén	246614.5	2017	23959	5908637554	35	0.0014608	11201	8631509
11201012055	11201	38	2017	Aysén	246614.5	2017	23959	5908637554	148	0.0061772	11201	36498951
11201012060	11201	13	2017	Aysén	246614.5	2017	23959	5908637554	72	0.0030051	11201	17756246
11201012066	11201	3	2017	Aysén	246614.5	2017	23959	5908637554	37	0.0015443	11201	9124738
11201012067	11201	15	2017	Aysén	246614.5	2017	23959	5908637554	98	0.0040903	11201	24168224
11201012901	11201	22	2017	Aysén	246614.5	2017	23959	5908637554	172	0.0071789	11201	42417699
11201022008	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	20	0.0008348	11201	4932291
11201022057	11201	4	2017	Aysén	246614.5	2017	23959	5908637554	58	0.0024208	11201	14303643
11201022058	11201	4	2017	Aysén	246614.5	2017	23959	5908637554	50	0.0020869	11201	12330727
11201022901	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	16	0.0006678	11201	3945833
11201032010	11201	2	2017	Aysén	246614.5	2017	23959	5908637554	29	0.0012104	11201	7151821
11201032012	11201	2	2017	Aysén	246614.5	2017	23959	5908637554	56	0.0023373	11201	13810414
11201032019	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	8	0.0003339	11201	1972916
11201032038	11201	3	2017	Aysén	246614.5	2017	23959	5908637554	41	0.0017113	11201	10111196
11201032068	11201	11	2017	Aysén	246614.5	2017	23959	5908637554	149	0.0062190	11201	36745565
11201042006	11201	36	2017	Aysén	246614.5	2017	23959	5908637554	244	0.0101841	11201	60173946
11201042007	11201	15	2017	Aysén	246614.5	2017	23959	5908637554	54	0.0022539	11201	13317185
11201042065	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	13	0.0005426	11201	3205989
11201052003	11201	2	2017	Aysén	246614.5	2017	23959	5908637554	14	0.0005843	11201	3452603
11201052020	11201	8	2017	Aysén	246614.5	2017	23959	5908637554	78	0.0032556	11201	19235933
11201052033	11201	3	2017	Aysén	246614.5	2017	23959	5908637554	15	0.0006261	11201	3699218
11201052035	11201	3	2017	Aysén	246614.5	2017	23959	5908637554	15	0.0006261	11201	3699218
11201052901	11201	3	2017	Aysén	246614.5	2017	23959	5908637554	14	0.0005843	11201	3452603
11201062005	11201	12	2017	Aysén	246614.5	2017	23959	5908637554	229	0.0095580	11201	56474728
11201062022	11201	8	2017	Aysén	246614.5	2017	23959	5908637554	21	0.0008765	11201	5178905
11201062023	11201	4	2017	Aysén	246614.5	2017	23959	5908637554	31	0.0012939	11201	7645050
11201062024	11201	2	2017	Aysén	246614.5	2017	23959	5908637554	18	0.0007513	11201	4439062
11201062025	11201	4	2017	Aysén	246614.5	2017	23959	5908637554	38	0.0015860	11201	9371352
11201062026	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	6	0.0002504	11201	1479687
11201062027	11201	5	2017	Aysén	246614.5	2017	23959	5908637554	37	0.0015443	11201	9124738
11201062028	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	11	0.0004591	11201	2712760
11201062029	11201	2	2017	Aysén	246614.5	2017	23959	5908637554	20	0.0008348	11201	4932291
11201062031	11201	4	2017	Aysén	246614.5	2017	23959	5908637554	68	0.0028382	11201	16769788
11201062032	11201	15	2017	Aysén	246614.5	2017	23959	5908637554	155	0.0064694	11201	38225252
11201062036	11201	15	2017	Aysén	246614.5	2017	23959	5908637554	112	0.0046747	11201	27620828
11201062037	11201	9	2017	Aysén	246614.5	2017	23959	5908637554	79	0.0032973	11201	19482548
11201062044	11201	30	2017	Aysén	246614.5	2017	23959	5908637554	541	0.0225802	11201	133418461
11201062063	11201	3	2017	Aysén	246614.5	2017	23959	5908637554	47	0.0019617	11201	11590883
11201072015	11201	10	2017	Aysén	246614.5	2017	23959	5908637554	41	0.0017113	11201	10111196
11201072021	11201	5	2017	Aysén	246614.5	2017	23959	5908637554	23	0.0009600	11201	5672134
11201072040	11201	6	2017	Aysén	246614.5	2017	23959	5908637554	38	0.0015860	11201	9371352
11201072047	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	17	0.0007095	11201	4192447
11201072050	11201	6	2017	Aysén	246614.5	2017	23959	5908637554	39	0.0016278	11201	9617967
11201072901	11201	27	2017	Aysén	246614.5	2017	23959	5908637554	208	0.0086815	11201	51295823
11202012003	11202	3	2017	Cisnes	262412.7	2017	6517	1710143349	10	0.0015344	11202	2624127
11202012007	11202	14	2017	Cisnes	262412.7	2017	6517	1710143349	89	0.0136566	11202	23354727
11202012018	11202	2	2017	Cisnes	262412.7	2017	6517	1710143349	5	0.0007672	11202	1312063
11202012019	11202	12	2017	Cisnes	262412.7	2017	6517	1710143349	127	0.0194875	11202	33326409
11202012901	11202	1	2017	Cisnes	262412.7	2017	6517	1710143349	30	0.0046033	11202	7872380
11202022016	11202	18	2017	Cisnes	262412.7	2017	6517	1710143349	280	0.0429646	11202	73475547
11202022020	11202	109	2017	Cisnes	262412.7	2017	6517	1710143349	1037	0.1591223	11202	272121935
11202042004	11202	1	2017	Cisnes	262412.7	2017	6517	1710143349	36	0.0055240	11202	9446856
11202042005	11202	1	2017	Cisnes	262412.7	2017	6517	1710143349	18	0.0027620	11202	4723428
11202042006	11202	10	2017	Cisnes	262412.7	2017	6517	1710143349	39	0.0059843	11202	10234094
11202042008	11202	7	2017	Cisnes	262412.7	2017	6517	1710143349	27	0.0041430	11202	7085142
11202042009	11202	2	2017	Cisnes	262412.7	2017	6517	1710143349	7	0.0010741	11202	1836889
11202042011	11202	3	2017	Cisnes	262412.7	2017	6517	1710143349	25	0.0038361	11202	6560317
11202042012	11202	3	2017	Cisnes	262412.7	2017	6517	1710143349	39	0.0059843	11202	10234094
11202042013	11202	1	2017	Cisnes	262412.7	2017	6517	1710143349	16	0.0024551	11202	4198603

7 Análisis de regresión

Aplicaremos un análisis de regresión donde:

\[ Y(dependiente) = ingreso \ expandido \ por \ zona \ (multi\_pob)\]

\[ X(independiente) = frecuencia \ de \ población \ que \ posee \ la \ variable \ Censal \ respecto \ a \ la \ zona \ (Freq.x) \]

7.1 Diagrama de dispersión loess

scatter.smooth(x=h_y_m_comuna_corr_01$Freq.x, y=h_y_m_comuna_corr_01$multi_pob, main="multi_pob ~ Freq.x",
     xlab = "Freq.x",
     ylab = "multi_pob",
           col = 2)

7.2 Outliers

Hemos demostrado en el punto 5.7.2 de aquí que la exclusión de ouliers no genera ninguna mejora en el modelo de regresión.

7.3 Modelo lineal

Aplicaremos un análisis de regresión lineal del ingreso expandido por zona sobre las frecuencias de respuestas zonales.

linearMod <- lm( multi_pob~(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod)

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -75017755  -9880208  -6625212   1533413 147368327 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 11064290    2188287   5.056 1.29e-06 ***
## Freq.x       1043021      59915  17.408  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 23670000 on 143 degrees of freedom
## Multiple R-squared:  0.6794, Adjusted R-squared:  0.6772 
## F-statistic: 303.1 on 1 and 143 DF,  p-value: < 2.2e-16

7.4 Gráfica de la recta de regresión lineal

ggplot(h_y_m_comuna_corr_01, aes(x = Freq.x , y = multi_pob)) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

Si bien obtenemos nuestro modelo lineal da cuenta del 0.8214 de la variabilidad de los datos de respuesta en torno a su media, modelos alternativos pueden ofrecernos una explicación de la variable dependiente aún mayor.

8 Modelos alternativos

8.1 Modelo cuadrático

\[ \hat Y = \beta_0 + \beta_1 X^2 \]

linearMod <- lm( multi_pob~(Freq.x^2) , data=h_y_m_comuna_corr_01)
summary(linearMod)

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x^2), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -75017755  -9880208  -6625212   1533413 147368327 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 11064290    2188287   5.056 1.29e-06 ***
## Freq.x       1043021      59915  17.408  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 23670000 on 143 degrees of freedom
## Multiple R-squared:  0.6794, Adjusted R-squared:  0.6772 
## F-statistic: 303.1 on 1 and 143 DF,  p-value: < 2.2e-16

8.2 Modelo cúbico

\[ \hat Y = \beta_0 + \beta_1 X^3 \]

linearMod <- lm( multi_pob~(Freq.x^3) , data=h_y_m_comuna_corr_01)
summary(linearMod)

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x^3), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -75017755  -9880208  -6625212   1533413 147368327 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 11064290    2188287   5.056 1.29e-06 ***
## Freq.x       1043021      59915  17.408  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 23670000 on 143 degrees of freedom
## Multiple R-squared:  0.6794, Adjusted R-squared:  0.6772 
## F-statistic: 303.1 on 1 and 143 DF,  p-value: < 2.2e-16

8.3 Modelo logarítmico

\[ \hat Y = \beta_0 + \beta_1 ln X \]

linearMod <- lm( multi_pob~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod)

## 
## Call:
## lm(formula = multi_pob ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -43793672 -15546844  -2266224  14624082 174380956 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -11911323    3511216  -3.392 0.000896 ***
## log(Freq.x)  23373304    1607637  14.539  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 26550000 on 143 degrees of freedom
## Multiple R-squared:  0.5965, Adjusted R-squared:  0.5937 
## F-statistic: 211.4 on 1 and 143 DF,  p-value: < 2.2e-16

8.4 Modelo exponencial

\[ \hat Y = \beta_0 + \beta_1 e^X \]

No es aplicable sin una transformación pues los valores elevados a \(e\) de Freq.x tienden a infinito.

8.5 Modelo con raíz cuadrada

\[ \hat Y = \beta_0 + \beta_1 \sqrt {X} \]

linearMod <- lm( multi_pob~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod)

## 
## Call:
## lm(formula = multi_pob ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -48659058  -7643769   -548625   4400463 142673989 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -13942312    2683157  -5.196 6.88e-07 ***
## sqrt(Freq.x)  13734296     669492  20.515  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 21050000 on 143 degrees of freedom
## Multiple R-squared:  0.7464, Adjusted R-squared:  0.7446 
## F-statistic: 420.8 on 1 and 143 DF,  p-value: < 2.2e-16

8.6 Modelo raíz-raíz

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \sqrt{X}+ \beta_1^2 X \]

linearMod <- lm( sqrt(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod)

## 
## Call:
## lm(formula = sqrt(multi_pob) ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3552.2  -859.4  -230.5   523.9  4758.9 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1294.51     184.29   7.024 8.05e-11 ***
## sqrt(Freq.x)  1003.65      45.98  21.827  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1446 on 143 degrees of freedom
## Multiple R-squared:  0.7691, Adjusted R-squared:  0.7675 
## F-statistic: 476.4 on 1 and 143 DF,  p-value: < 2.2e-16

8.7 Modelo log-raíz

\[ \hat Y = e^{\beta_0 + \beta_1 \sqrt{X}} \]

linearMod <- lm( log(multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7465 -0.5021  0.0259  0.4643  1.8310 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  15.21098    0.09607  158.33   <2e-16 ***
## sqrt(Freq.x)  0.37665    0.02397   15.71   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7537 on 143 degrees of freedom
## Multiple R-squared:  0.6332, Adjusted R-squared:  0.6307 
## F-statistic: 246.9 on 1 and 143 DF,  p-value: < 2.2e-16

8.8 Modelo raíz-log

\[ \hat Y = {\beta_0}^2 + 2 \beta_0 \beta_1 \ln{X}+ \beta_1^2 ln^2X \]

linearMod <- lm( sqrt(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod)

## 
## Call:
## lm(formula = sqrt(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3129.3 -1156.6   -42.5   877.6  6557.1 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1168.44     204.30   5.719 6.03e-08 ***
## log(Freq.x)  1869.52      93.54  19.986  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1545 on 143 degrees of freedom
## Multiple R-squared:  0.7364, Adjusted R-squared:  0.7345 
## F-statistic: 399.5 on 1 and 143 DF,  p-value: < 2.2e-16

8.9 Modelo log-log

\[ \hat Y = e^{\beta_0+\beta_1 ln{X}} \]

linearMod <- lm( log(multi_pob)~log(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod)

## 
## Call:
## lm(formula = log(multi_pob) ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.66127 -0.43000  0.01706  0.37443  1.80881 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 15.03689    0.08359  179.89   <2e-16 ***
## log(Freq.x)  0.77618    0.03827   20.28   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6321 on 143 degrees of freedom
## Multiple R-squared:  0.742,  Adjusted R-squared:  0.7402 
## F-statistic: 411.3 on 1 and 143 DF,  p-value: < 2.2e-16

9 Modelo elegido: raíz-raíz (sqrt-sqrt)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.7675).

9.1 Diagrama de dispersión sobre sqrt-sqrt

Desplegamos una curva suavizada por loess en el diagrama de dispersión.

scatter.smooth(x=sqrt(h_y_m_comuna_corr_01$Freq.x), y=sqrt(h_y_m_comuna_corr_01$multi_pob), lpars = list(col = "red", lwd = 2, lty = 1), main="multi_pob ~ Freq.x")

9.2 Modelo sqrt-sqrt

Observemos nuevamente el resultado sobre sqrt-sqrt.

linearMod <- lm(sqrt( multi_pob)~sqrt(Freq.x) , data=h_y_m_comuna_corr_01)
summary(linearMod)

## 
## Call:
## lm(formula = sqrt(multi_pob) ~ sqrt(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3552.2  -859.4  -230.5   523.9  4758.9 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1294.51     184.29   7.024 8.05e-11 ***
## sqrt(Freq.x)  1003.65      45.98  21.827  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1446 on 143 degrees of freedom
## Multiple R-squared:  0.7691, Adjusted R-squared:  0.7675 
## F-statistic: 476.4 on 1 and 143 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = sqrt(Freq.x) , y = sqrt(multi_pob))) + 
  geom_point() +
  stat_smooth(method = "lm", col = "red")

9.3 Análisis de residuos

par(mfrow = c (2,2))
plot(linearMod)

9.4 Ecuación del modelo

\[ \hat Y = {1294.51}^2 + 2 \cdot 1294.51 \cdot 1003.65 \sqrt{X}+ 1003.65^2 \cdot X \]

10 Aplicación la regresión a los valores de la variable a nivel de zona

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- 
(1294.51)^2 + 2 * 1294.51 * 1003.65 * sqrt(h_y_m_comuna_corr_01$Freq.x)+  1003.65^2 * (h_y_m_comuna_corr_01$Freq.x)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	personas	Ingresos_expandidos	Freq.y	p_poblacional	código.y	multi_pob	est_ing
11101022003	11101	2	2017	Coyhaique	230013.0	2017	57818	13298894369	11	0.0001903	11101	2530144	7365174
11101022010	11101	2	2017	Coyhaique	230013.0	2017	57818	13298894369	53	0.0009167	11101	12190692	7365174
11101022031	11101	3	2017	Coyhaique	230013.0	2017	57818	13298894369	55	0.0009513	11101	12650718	9198378
11101022038	11101	9	2017	Coyhaique	230013.0	2017	57818	13298894369	339	0.0058632	11101	77974423	18536986
11101032005	11101	2	2017	Coyhaique	230013.0	2017	57818	13298894369	53	0.0009167	11101	12190692	7365174
11101032015	11101	1	2017	Coyhaique	230013.0	2017	57818	13298894369	90	0.0015566	11101	20701174	5281539
11101032016	11101	1	2017	Coyhaique	230013.0	2017	57818	13298894369	16	0.0002767	11101	3680209	5281539
11101032032	11101	9	2017	Coyhaique	230013.0	2017	57818	13298894369	257	0.0044450	11101	59113353	18536986
11101042024	11101	3	2017	Coyhaique	230013.0	2017	57818	13298894369	47	0.0008129	11101	10810613	9198378
11101042027	11101	4	2017	Coyhaique	230013.0	2017	57818	13298894369	77	0.0013318	11101	17711005	10901949
11101052004	11101	28	2017	Coyhaique	230013.0	2017	57818	13298894369	474	0.0081981	11101	109026184	43630340
11101052008	11101	2	2017	Coyhaique	230013.0	2017	57818	13298894369	76	0.0013145	11101	17480992	7365174
11101052014	11101	11	2017	Coyhaique	230013.0	2017	57818	13298894369	250	0.0043239	11101	57503262	21374352
11101052039	11101	1	2017	Coyhaique	230013.0	2017	57818	13298894369	62	0.0010723	11101	14260809	5281539
11101052901	11101	3	2017	Coyhaique	230013.0	2017	57818	13298894369	50	0.0008648	11101	11500652	9198378
11101062020	11101	4	2017	Coyhaique	230013.0	2017	57818	13298894369	37	0.0006399	11101	8510483	10901949
11101062023	11101	4	2017	Coyhaique	230013.0	2017	57818	13298894369	54	0.0009340	11101	12420705	10901949
11101072014	11101	3	2017	Coyhaique	230013.0	2017	57818	13298894369	107	0.0018506	11101	24611396	9198378
11101072018	11101	156	2017	Coyhaique	230013.0	2017	57818	13298894369	806	0.0139403	11101	185390516	191271513
11101072019	11101	9	2017	Coyhaique	230013.0	2017	57818	13298894369	102	0.0017642	11101	23461331	18536986
11101072020	11101	17	2017	Coyhaique	230013.0	2017	57818	13298894369	190	0.0032862	11101	43702479	29513849
11101072023	11101	1	2017	Coyhaique	230013.0	2017	57818	13298894369	9	0.0001557	11101	2070117	5281539
11101072025	11101	1	2017	Coyhaique	230013.0	2017	57818	13298894369	15	0.0002594	11101	3450196	5281539
11101072035	11101	8	2017	Coyhaique	230013.0	2017	57818	13298894369	109	0.0018852	11101	25071422	17083846
11101072036	11101	35	2017	Coyhaique	230013.0	2017	57818	13298894369	539	0.0093224	11101	123977032	52304478
11101072037	11101	10	2017	Coyhaique	230013.0	2017	57818	13298894369	265	0.0045833	11101	60953458	19965973
11101092002	11101	12	2017	Coyhaique	230013.0	2017	57818	13298894369	92	0.0015912	11101	21161200	22764880
11101092007	11101	1	2017	Coyhaique	230013.0	2017	57818	13298894369	29	0.0005016	11101	6670378	5281539
11101092901	11101	3	2017	Coyhaique	230013.0	2017	57818	13298894369	39	0.0006745	11101	8970509	9198378
11101102011	11101	35	2017	Coyhaique	230013.0	2017	57818	13298894369	143	0.0024733	11101	32891866	52304478
11101102020	11101	70	2017	Coyhaique	230013.0	2017	57818	13298894369	250	0.0043239	11101	57503262	93928048
11101102033	11101	192	2017	Coyhaique	230013.0	2017	57818	13298894369	763	0.0131966	11101	175499955	231085369
11101112001	11101	26	2017	Coyhaique	230013.0	2017	57818	13298894369	186	0.0032170	11101	42782427	41115551
11101112009	11101	62	2017	Coyhaique	230013.0	2017	57818	13298894369	198	0.0034245	11101	45542583	84589555
11101112011	11101	189	2017	Coyhaique	230013.0	2017	57818	13298894369	579	0.0100142	11101	133177554	227781029
11101112012	11101	5	2017	Coyhaique	230013.0	2017	57818	13298894369	54	0.0009340	11101	12420705	12522678
11101112013	11101	181	2017	Coyhaique	230013.0	2017	57818	13298894369	615	0.0106368	11101	141458024	218958305
11101112029	11101	23	2017	Coyhaique	230013.0	2017	57818	13298894369	304	0.0052579	11101	69923966	37305787
11101122011	11101	52	2017	Coyhaique	230013.0	2017	57818	13298894369	206	0.0035629	11101	47382688	72793882
11101132011	11101	10	2017	Coyhaique	230013.0	2017	57818	13298894369	44	0.0007610	11101	10120574	19965973
11201012009	11201	9	2017	Aysén	246614.5	2017	23959	5908637554	52	0.0021704	11201	12823956	18536986
11201012013	11201	2	2017	Aysén	246614.5	2017	23959	5908637554	35	0.0014608	11201	8631509	7365174
11201012055	11201	38	2017	Aysén	246614.5	2017	23959	5908637554	148	0.0061772	11201	36498951	55971707
11201012060	11201	13	2017	Aysén	246614.5	2017	23959	5908637554	72	0.0030051	11201	17756246	24139746
11201012066	11201	3	2017	Aysén	246614.5	2017	23959	5908637554	37	0.0015443	11201	9124738	9198378
11201012067	11201	15	2017	Aysén	246614.5	2017	23959	5908637554	98	0.0040903	11201	24168224	26849287
11201012901	11201	22	2017	Aysén	246614.5	2017	23959	5908637554	172	0.0071789	11201	42417699	36024554
11201022008	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	20	0.0008348	11201	4932291	5281539
11201022057	11201	4	2017	Aysén	246614.5	2017	23959	5908637554	58	0.0024208	11201	14303643	10901949
11201022058	11201	4	2017	Aysén	246614.5	2017	23959	5908637554	50	0.0020869	11201	12330727	10901949
11201022901	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	16	0.0006678	11201	3945833	5281539
11201032010	11201	2	2017	Aysén	246614.5	2017	23959	5908637554	29	0.0012104	11201	7151821	7365174
11201032012	11201	2	2017	Aysén	246614.5	2017	23959	5908637554	56	0.0023373	11201	13810414	7365174
11201032019	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	8	0.0003339	11201	1972916	5281539
11201032038	11201	3	2017	Aysén	246614.5	2017	23959	5908637554	41	0.0017113	11201	10111196	9198378
11201032068	11201	11	2017	Aysén	246614.5	2017	23959	5908637554	149	0.0062190	11201	36745565	21374352
11201042006	11201	36	2017	Aysén	246614.5	2017	23959	5908637554	244	0.0101841	11201	60173946	53529855
11201042007	11201	15	2017	Aysén	246614.5	2017	23959	5908637554	54	0.0022539	11201	13317185	26849287
11201042065	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	13	0.0005426	11201	3205989	5281539
11201052003	11201	2	2017	Aysén	246614.5	2017	23959	5908637554	14	0.0005843	11201	3452603	7365174
11201052020	11201	8	2017	Aysén	246614.5	2017	23959	5908637554	78	0.0032556	11201	19235933	17083846
11201052033	11201	3	2017	Aysén	246614.5	2017	23959	5908637554	15	0.0006261	11201	3699218	9198378
11201052035	11201	3	2017	Aysén	246614.5	2017	23959	5908637554	15	0.0006261	11201	3699218	9198378
11201052901	11201	3	2017	Aysén	246614.5	2017	23959	5908637554	14	0.0005843	11201	3452603	9198378
11201062005	11201	12	2017	Aysén	246614.5	2017	23959	5908637554	229	0.0095580	11201	56474728	22764880
11201062022	11201	8	2017	Aysén	246614.5	2017	23959	5908637554	21	0.0008765	11201	5178905	17083846
11201062023	11201	4	2017	Aysén	246614.5	2017	23959	5908637554	31	0.0012939	11201	7645050	10901949
11201062024	11201	2	2017	Aysén	246614.5	2017	23959	5908637554	18	0.0007513	11201	4439062	7365174
11201062025	11201	4	2017	Aysén	246614.5	2017	23959	5908637554	38	0.0015860	11201	9371352	10901949
11201062026	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	6	0.0002504	11201	1479687	5281539
11201062027	11201	5	2017	Aysén	246614.5	2017	23959	5908637554	37	0.0015443	11201	9124738	12522678
11201062028	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	11	0.0004591	11201	2712760	5281539
11201062029	11201	2	2017	Aysén	246614.5	2017	23959	5908637554	20	0.0008348	11201	4932291	7365174
11201062031	11201	4	2017	Aysén	246614.5	2017	23959	5908637554	68	0.0028382	11201	16769788	10901949
11201062032	11201	15	2017	Aysén	246614.5	2017	23959	5908637554	155	0.0064694	11201	38225252	26849287
11201062036	11201	15	2017	Aysén	246614.5	2017	23959	5908637554	112	0.0046747	11201	27620828	26849287
11201062037	11201	9	2017	Aysén	246614.5	2017	23959	5908637554	79	0.0032973	11201	19482548	18536986
11201062044	11201	30	2017	Aysén	246614.5	2017	23959	5908637554	541	0.0225802	11201	133418461	46127562
11201062063	11201	3	2017	Aysén	246614.5	2017	23959	5908637554	47	0.0019617	11201	11590883	9198378
11201072015	11201	10	2017	Aysén	246614.5	2017	23959	5908637554	41	0.0017113	11201	10111196	19965973
11201072021	11201	5	2017	Aysén	246614.5	2017	23959	5908637554	23	0.0009600	11201	5672134	12522678
11201072040	11201	6	2017	Aysén	246614.5	2017	23959	5908637554	38	0.0015860	11201	9371352	14084561
11201072047	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	17	0.0007095	11201	4192447	5281539
11201072050	11201	6	2017	Aysén	246614.5	2017	23959	5908637554	39	0.0016278	11201	9617967	14084561
11201072901	11201	27	2017	Aysén	246614.5	2017	23959	5908637554	208	0.0086815	11201	51295823	42375262
11202012003	11202	3	2017	Cisnes	262412.7	2017	6517	1710143349	10	0.0015344	11202	2624127	9198378
11202012007	11202	14	2017	Cisnes	262412.7	2017	6517	1710143349	89	0.0136566	11202	23354727	25500727
11202012018	11202	2	2017	Cisnes	262412.7	2017	6517	1710143349	5	0.0007672	11202	1312063	7365174
11202012019	11202	12	2017	Cisnes	262412.7	2017	6517	1710143349	127	0.0194875	11202	33326409	22764880
11202012901	11202	1	2017	Cisnes	262412.7	2017	6517	1710143349	30	0.0046033	11202	7872380	5281539
11202022016	11202	18	2017	Cisnes	262412.7	2017	6517	1710143349	280	0.0429646	11202	73475547	30831770
11202022020	11202	109	2017	Cisnes	262412.7	2017	6517	1710143349	1037	0.1591223	11202	272121935	138601731
11202042004	11202	1	2017	Cisnes	262412.7	2017	6517	1710143349	36	0.0055240	11202	9446856	5281539
11202042005	11202	1	2017	Cisnes	262412.7	2017	6517	1710143349	18	0.0027620	11202	4723428	5281539
11202042006	11202	10	2017	Cisnes	262412.7	2017	6517	1710143349	39	0.0059843	11202	10234094	19965973
11202042008	11202	7	2017	Cisnes	262412.7	2017	6517	1710143349	27	0.0041430	11202	7085142	15601855
11202042009	11202	2	2017	Cisnes	262412.7	2017	6517	1710143349	7	0.0010741	11202	1836889	7365174
11202042011	11202	3	2017	Cisnes	262412.7	2017	6517	1710143349	25	0.0038361	11202	6560317	9198378
11202042012	11202	3	2017	Cisnes	262412.7	2017	6517	1710143349	39	0.0059843	11202	10234094	9198378
11202042013	11202	1	2017	Cisnes	262412.7	2017	6517	1710143349	16	0.0024551	11202	4198603	5281539

11 División del valor estimado entre la población total de la zona para obtener el ingreso medio por zona

\[ Ingreso \_ Medio\_zona = est\_ing / (personas * p\_poblacional) \]

h_y_m_comuna_corr_01$ing_medio_zona <- h_y_m_comuna_corr_01$est_ing  /( h_y_m_comuna_corr_01$personas  * h_y_m_comuna_corr_01$p_poblacional)

r3_100 <- h_y_m_comuna_corr_01[c(1:100),]
kbl(r3_100) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	personas	Ingresos_expandidos	Freq.y	p_poblacional	código.y	multi_pob	est_ing	ing_medio_zona
11101022003	11101	2	2017	Coyhaique	230013.0	2017	57818	13298894369	11	0.0001903	11101	2530144	7365174	669561.29
11101022010	11101	2	2017	Coyhaique	230013.0	2017	57818	13298894369	53	0.0009167	11101	12190692	7365174	138965.55
11101022031	11101	3	2017	Coyhaique	230013.0	2017	57818	13298894369	55	0.0009513	11101	12650718	9198378	167243.24
11101022038	11101	9	2017	Coyhaique	230013.0	2017	57818	13298894369	339	0.0058632	11101	77974423	18536986	54681.37
11101032005	11101	2	2017	Coyhaique	230013.0	2017	57818	13298894369	53	0.0009167	11101	12190692	7365174	138965.55
11101032015	11101	1	2017	Coyhaique	230013.0	2017	57818	13298894369	90	0.0015566	11101	20701174	5281539	58683.77
11101032016	11101	1	2017	Coyhaique	230013.0	2017	57818	13298894369	16	0.0002767	11101	3680209	5281539	330096.21
11101032032	11101	9	2017	Coyhaique	230013.0	2017	57818	13298894369	257	0.0044450	11101	59113353	18536986	72128.35
11101042024	11101	3	2017	Coyhaique	230013.0	2017	57818	13298894369	47	0.0008129	11101	10810613	9198378	195710.17
11101042027	11101	4	2017	Coyhaique	230013.0	2017	57818	13298894369	77	0.0013318	11101	17711005	10901949	141583.76
11101052004	11101	28	2017	Coyhaique	230013.0	2017	57818	13298894369	474	0.0081981	11101	109026184	43630340	92047.13
11101052008	11101	2	2017	Coyhaique	230013.0	2017	57818	13298894369	76	0.0013145	11101	17480992	7365174	96910.19
11101052014	11101	11	2017	Coyhaique	230013.0	2017	57818	13298894369	250	0.0043239	11101	57503262	21374352	85497.41
11101052039	11101	1	2017	Coyhaique	230013.0	2017	57818	13298894369	62	0.0010723	11101	14260809	5281539	85186.12
11101052901	11101	3	2017	Coyhaique	230013.0	2017	57818	13298894369	50	0.0008648	11101	11500652	9198378	183967.56
11101062020	11101	4	2017	Coyhaique	230013.0	2017	57818	13298894369	37	0.0006399	11101	8510483	10901949	294647.28
11101062023	11101	4	2017	Coyhaique	230013.0	2017	57818	13298894369	54	0.0009340	11101	12420705	10901949	201887.95
11101072014	11101	3	2017	Coyhaique	230013.0	2017	57818	13298894369	107	0.0018506	11101	24611396	9198378	85966.15
11101072018	11101	156	2017	Coyhaique	230013.0	2017	57818	13298894369	806	0.0139403	11101	185390516	191271513	237309.57
11101072019	11101	9	2017	Coyhaique	230013.0	2017	57818	13298894369	102	0.0017642	11101	23461331	18536986	181735.16
11101072020	11101	17	2017	Coyhaique	230013.0	2017	57818	13298894369	190	0.0032862	11101	43702479	29513849	155336.05
11101072023	11101	1	2017	Coyhaique	230013.0	2017	57818	13298894369	9	0.0001557	11101	2070117	5281539	586837.71
11101072025	11101	1	2017	Coyhaique	230013.0	2017	57818	13298894369	15	0.0002594	11101	3450196	5281539	352102.63
11101072035	11101	8	2017	Coyhaique	230013.0	2017	57818	13298894369	109	0.0018852	11101	25071422	17083846	156732.53
11101072036	11101	35	2017	Coyhaique	230013.0	2017	57818	13298894369	539	0.0093224	11101	123977032	52304478	97039.85
11101072037	11101	10	2017	Coyhaique	230013.0	2017	57818	13298894369	265	0.0045833	11101	60953458	19965973	75343.29
11101092002	11101	12	2017	Coyhaique	230013.0	2017	57818	13298894369	92	0.0015912	11101	21161200	22764880	247444.35
11101092007	11101	1	2017	Coyhaique	230013.0	2017	57818	13298894369	29	0.0005016	11101	6670378	5281539	182122.05
11101092901	11101	3	2017	Coyhaique	230013.0	2017	57818	13298894369	39	0.0006745	11101	8970509	9198378	235855.85
11101102011	11101	35	2017	Coyhaique	230013.0	2017	57818	13298894369	143	0.0024733	11101	32891866	52304478	365765.58
11101102020	11101	70	2017	Coyhaique	230013.0	2017	57818	13298894369	250	0.0043239	11101	57503262	93928048	375712.19
11101102033	11101	192	2017	Coyhaique	230013.0	2017	57818	13298894369	763	0.0131966	11101	175499955	231085369	302864.18
11101112001	11101	26	2017	Coyhaique	230013.0	2017	57818	13298894369	186	0.0032170	11101	42782427	41115551	221051.35
11101112009	11101	62	2017	Coyhaique	230013.0	2017	57818	13298894369	198	0.0034245	11101	45542583	84589555	427219.97
11101112011	11101	189	2017	Coyhaique	230013.0	2017	57818	13298894369	579	0.0100142	11101	133177554	227781029	393404.20
11101112012	11101	5	2017	Coyhaique	230013.0	2017	57818	13298894369	54	0.0009340	11101	12420705	12522678	231901.45
11101112013	11101	181	2017	Coyhaique	230013.0	2017	57818	13298894369	615	0.0106368	11101	141458024	218958305	356029.76
11101112029	11101	23	2017	Coyhaique	230013.0	2017	57818	13298894369	304	0.0052579	11101	69923966	37305787	122716.40
11101122011	11101	52	2017	Coyhaique	230013.0	2017	57818	13298894369	206	0.0035629	11101	47382688	72793882	353368.36
11101132011	11101	10	2017	Coyhaique	230013.0	2017	57818	13298894369	44	0.0007610	11101	10120574	19965973	453772.11
11201012009	11201	9	2017	Aysén	246614.5	2017	23959	5908637554	52	0.0021704	11201	12823956	18536986	356480.50
11201012013	11201	2	2017	Aysén	246614.5	2017	23959	5908637554	35	0.0014608	11201	8631509	7365174	210433.55
11201012055	11201	38	2017	Aysén	246614.5	2017	23959	5908637554	148	0.0061772	11201	36498951	55971707	378187.21
11201012060	11201	13	2017	Aysén	246614.5	2017	23959	5908637554	72	0.0030051	11201	17756246	24139746	335274.25
11201012066	11201	3	2017	Aysén	246614.5	2017	23959	5908637554	37	0.0015443	11201	9124738	9198378	248604.81
11201012067	11201	15	2017	Aysén	246614.5	2017	23959	5908637554	98	0.0040903	11201	24168224	26849287	273972.31
11201012901	11201	22	2017	Aysén	246614.5	2017	23959	5908637554	172	0.0071789	11201	42417699	36024554	209445.08
11201022008	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	20	0.0008348	11201	4932291	5281539	264076.97
11201022057	11201	4	2017	Aysén	246614.5	2017	23959	5908637554	58	0.0024208	11201	14303643	10901949	187964.64
11201022058	11201	4	2017	Aysén	246614.5	2017	23959	5908637554	50	0.0020869	11201	12330727	10901949	218038.99
11201022901	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	16	0.0006678	11201	3945833	5281539	330096.21
11201032010	11201	2	2017	Aysén	246614.5	2017	23959	5908637554	29	0.0012104	11201	7151821	7365174	253971.52
11201032012	11201	2	2017	Aysén	246614.5	2017	23959	5908637554	56	0.0023373	11201	13810414	7365174	131520.97
11201032019	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	8	0.0003339	11201	1972916	5281539	660192.42
11201032038	11201	3	2017	Aysén	246614.5	2017	23959	5908637554	41	0.0017113	11201	10111196	9198378	224350.68
11201032068	11201	11	2017	Aysén	246614.5	2017	23959	5908637554	149	0.0062190	11201	36745565	21374352	143452.03
11201042006	11201	36	2017	Aysén	246614.5	2017	23959	5908637554	244	0.0101841	11201	60173946	53529855	219384.65
11201042007	11201	15	2017	Aysén	246614.5	2017	23959	5908637554	54	0.0022539	11201	13317185	26849287	497209.01
11201042065	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	13	0.0005426	11201	3205989	5281539	406272.26
11201052003	11201	2	2017	Aysén	246614.5	2017	23959	5908637554	14	0.0005843	11201	3452603	7365174	526083.87
11201052020	11201	8	2017	Aysén	246614.5	2017	23959	5908637554	78	0.0032556	11201	19235933	17083846	219023.66
11201052033	11201	3	2017	Aysén	246614.5	2017	23959	5908637554	15	0.0006261	11201	3699218	9198378	613225.20
11201052035	11201	3	2017	Aysén	246614.5	2017	23959	5908637554	15	0.0006261	11201	3699218	9198378	613225.20
11201052901	11201	3	2017	Aysén	246614.5	2017	23959	5908637554	14	0.0005843	11201	3452603	9198378	657027.00
11201062005	11201	12	2017	Aysén	246614.5	2017	23959	5908637554	229	0.0095580	11201	56474728	22764880	99409.96
11201062022	11201	8	2017	Aysén	246614.5	2017	23959	5908637554	21	0.0008765	11201	5178905	17083846	813516.45
11201062023	11201	4	2017	Aysén	246614.5	2017	23959	5908637554	31	0.0012939	11201	7645050	10901949	351675.78
11201062024	11201	2	2017	Aysén	246614.5	2017	23959	5908637554	18	0.0007513	11201	4439062	7365174	409176.34
11201062025	11201	4	2017	Aysén	246614.5	2017	23959	5908637554	38	0.0015860	11201	9371352	10901949	286893.40
11201062026	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	6	0.0002504	11201	1479687	5281539	880256.56
11201062027	11201	5	2017	Aysén	246614.5	2017	23959	5908637554	37	0.0015443	11201	9124738	12522678	338450.76
11201062028	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	11	0.0004591	11201	2712760	5281539	480139.94
11201062029	11201	2	2017	Aysén	246614.5	2017	23959	5908637554	20	0.0008348	11201	4932291	7365174	368258.71
11201062031	11201	4	2017	Aysén	246614.5	2017	23959	5908637554	68	0.0028382	11201	16769788	10901949	160322.78
11201062032	11201	15	2017	Aysén	246614.5	2017	23959	5908637554	155	0.0064694	11201	38225252	26849287	173221.20
11201062036	11201	15	2017	Aysén	246614.5	2017	23959	5908637554	112	0.0046747	11201	27620828	26849287	239725.77
11201062037	11201	9	2017	Aysén	246614.5	2017	23959	5908637554	79	0.0032973	11201	19482548	18536986	234645.39
11201062044	11201	30	2017	Aysén	246614.5	2017	23959	5908637554	541	0.0225802	11201	133418461	46127562	85263.52
11201062063	11201	3	2017	Aysén	246614.5	2017	23959	5908637554	47	0.0019617	11201	11590883	9198378	195710.17
11201072015	11201	10	2017	Aysén	246614.5	2017	23959	5908637554	41	0.0017113	11201	10111196	19965973	486974.95
11201072021	11201	5	2017	Aysén	246614.5	2017	23959	5908637554	23	0.0009600	11201	5672134	12522678	544464.27
11201072040	11201	6	2017	Aysén	246614.5	2017	23959	5908637554	38	0.0015860	11201	9371352	14084561	370646.36
11201072047	11201	1	2017	Aysén	246614.5	2017	23959	5908637554	17	0.0007095	11201	4192447	5281539	310678.79
11201072050	11201	6	2017	Aysén	246614.5	2017	23959	5908637554	39	0.0016278	11201	9617967	14084561	361142.60
11201072901	11201	27	2017	Aysén	246614.5	2017	23959	5908637554	208	0.0086815	11201	51295823	42375262	203727.22
11202012003	11202	3	2017	Cisnes	262412.7	2017	6517	1710143349	10	0.0015344	11202	2624127	9198378	919837.80
11202012007	11202	14	2017	Cisnes	262412.7	2017	6517	1710143349	89	0.0136566	11202	23354727	25500727	286525.02
11202012018	11202	2	2017	Cisnes	262412.7	2017	6517	1710143349	5	0.0007672	11202	1312063	7365174	1473034.84
11202012019	11202	12	2017	Cisnes	262412.7	2017	6517	1710143349	127	0.0194875	11202	33326409	22764880	179251.02
11202012901	11202	1	2017	Cisnes	262412.7	2017	6517	1710143349	30	0.0046033	11202	7872380	5281539	176051.31
11202022016	11202	18	2017	Cisnes	262412.7	2017	6517	1710143349	280	0.0429646	11202	73475547	30831770	110113.46
11202022020	11202	109	2017	Cisnes	262412.7	2017	6517	1710143349	1037	0.1591223	11202	272121935	138601731	133656.44
11202042004	11202	1	2017	Cisnes	262412.7	2017	6517	1710143349	36	0.0055240	11202	9446856	5281539	146709.43
11202042005	11202	1	2017	Cisnes	262412.7	2017	6517	1710143349	18	0.0027620	11202	4723428	5281539	293418.85
11202042006	11202	10	2017	Cisnes	262412.7	2017	6517	1710143349	39	0.0059843	11202	10234094	19965973	511948.02
11202042008	11202	7	2017	Cisnes	262412.7	2017	6517	1710143349	27	0.0041430	11202	7085142	15601855	577846.47
11202042009	11202	2	2017	Cisnes	262412.7	2017	6517	1710143349	7	0.0010741	11202	1836889	7365174	1052167.74
11202042011	11202	3	2017	Cisnes	262412.7	2017	6517	1710143349	25	0.0038361	11202	6560317	9198378	367935.12
11202042012	11202	3	2017	Cisnes	262412.7	2017	6517	1710143349	39	0.0059843	11202	10234094	9198378	235855.85
11202042013	11202	1	2017	Cisnes	262412.7	2017	6517	1710143349	16	0.0024551	11202	4198603	5281539	330096.21

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "P15/region_11_P15_r.rds")

12 Referencias

https://rpubs.com/osoramirez/316691

https://dataintelligencechile.shinyapps.io/casenfinal

Manual_de_usuario_Censo_2017_16R.pdf

http://www.censo2017.cl/microdatos/

Censo de Población y Vivienda

https://www.ine.cl/estadisticas/sociales/censos-de-poblacion-y-vivienda/poblacion-y-vivienda

http://r-statistics.co/Linear-Regression.html