Expansión de la CASEN sobre el CENSO (Región 03)

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 03.

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 03 y con la zona = 2:

tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$REGION == 3) 
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$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	3101092006	12	3101	3	2017
2	3101102901	12	3101	7	2017
3	3101122007	12	3101	8	2017
4	3101122013	12	3101	109	2017
5	3101122047	12	3101	4	2017
6	3101122901	12	3101	3	2017
7	3101132901	12	3101	1	2017
8	3101162006	12	3101	1	2017
9	3101162050	12	3101	4	2017
10	3101172013	12	3101	36	2017
11	3101172017	12	3101	30	2017
12	3101172021	12	3101	5	2017
13	3101172026	12	3101	54	2017
14	3101172035	12	3101	34	2017
15	3101172037	12	3101	46	2017
16	3101172901	12	3101	3	2017
17	3101192014	12	3101	26	2017
18	3101192901	12	3101	1	2017
19	3101212032	12	3101	1	2017
20	3101212901	12	3101	2	2017
21	3101222015	12	3101	1	2017
22	3101222048	12	3101	1	2017
158	3102012001	12	3102	154	2017
159	3102012004	12	3102	9	2017
160	3102022010	12	3102	42	2017
161	3102022901	12	3102	4	2017
162	3102032003	12	3102	12	2017
163	3102032007	12	3102	3	2017
164	3102042003	12	3102	7	2017
300	3103012002	12	3103	2	2017
301	3103012003	12	3103	8	2017
302	3103012022	12	3103	24	2017
303	3103012029	12	3103	6	2017
304	3103032006	12	3103	102	2017
305	3103032014	12	3103	7	2017
306	3103032019	12	3103	58	2017
307	3103032026	12	3103	10	2017
308	3103032027	12	3103	3	2017
309	3103042001	12	3103	15	2017
310	3103042010	12	3103	3	2017
311	3103042028	12	3103	2	2017
312	3103042031	12	3103	1	2017
313	3103042901	12	3103	2	2017
314	3103052020	12	3103	17	2017
315	3103052901	12	3103	1	2017
316	3103062011	12	3103	5	2017
317	3103062016	12	3103	1	2017
318	3103062901	12	3103	4	2017
319	3103072004	12	3103	2	2017
320	3103072012	12	3103	1	2017
321	3103072023	12	3103	3	2017
322	3103072901	12	3103	1	2017
458	3201012005	12	3201	4	2017
459	3201012901	12	3201	2	2017
460	3201022006	12	3201	25	2017
461	3201032007	12	3201	13	2017
462	3201032015	12	3201	1	2017
463	3201032019	12	3201	2	2017
464	3201032020	12	3201	2	2017
465	3201032901	12	3201	13	2017
601	3202022901	12	3202	5	2017
602	3202042008	12	3202	16	2017
738	3301032005	12	3301	4	2017
739	3301032017	12	3301	12	2017
740	3301032901	12	3301	1	2017
741	3301042005	12	3301	19	2017
742	3301042060	12	3301	19	2017
743	3301042901	12	3301	3	2017
744	3301052002	12	3301	39	2017
745	3301052006	12	3301	7	2017
746	3301052008	12	3301	2	2017
747	3301052010	12	3301	15	2017
748	3301052014	12	3301	5	2017
749	3301052028	12	3301	53	2017
750	3301052036	12	3301	101	2017
751	3301052038	12	3301	9	2017
752	3301052063	12	3301	13	2017
753	3301082012	12	3301	35	2017
754	3301082901	12	3301	7	2017
755	3301092043	12	3301	2	2017
756	3301092901	12	3301	3	2017
757	3301122004	12	3301	3	2017
758	3301122009	12	3301	7	2017
759	3301122025	12	3301	19	2017
760	3301122030	12	3301	4	2017
761	3301122032	12	3301	6	2017
762	3301122033	12	3301	8	2017
763	3301122901	12	3301	2	2017
764	3301132026	12	3301	2	2017
765	3301132901	12	3301	3	2017
766	3301152010	12	3301	4	2017
767	3301152024	12	3301	1	2017
768	3301152901	12	3301	4	2017
904	3302012002	12	3302	58	2017
905	3302012005	12	3302	2	2017
906	3302012029	12	3302	1	2017
907	3302022005	12	3302	20	2017
908	3302032005	12	3302	34	2017
909	3302032018	12	3302	25	2017
910	3302042005	12	3302	17	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	3101092006	3	2017	03101
2	3101102901	7	2017	03101
3	3101122007	8	2017	03101
4	3101122013	109	2017	03101
5	3101122047	4	2017	03101
6	3101122901	3	2017	03101
7	3101132901	1	2017	03101
8	3101162006	1	2017	03101
9	3101162050	4	2017	03101
10	3101172013	36	2017	03101
11	3101172017	30	2017	03101
12	3101172021	5	2017	03101
13	3101172026	54	2017	03101
14	3101172035	34	2017	03101
15	3101172037	46	2017	03101
16	3101172901	3	2017	03101
17	3101192014	26	2017	03101
18	3101192901	1	2017	03101
19	3101212032	1	2017	03101
20	3101212901	2	2017	03101
21	3101222015	1	2017	03101
22	3101222048	1	2017	03101
158	3102012001	154	2017	03102
159	3102012004	9	2017	03102
160	3102022010	42	2017	03102
161	3102022901	4	2017	03102
162	3102032003	12	2017	03102
163	3102032007	3	2017	03102
164	3102042003	7	2017	03102
300	3103012002	2	2017	03103
301	3103012003	8	2017	03103
302	3103012022	24	2017	03103
303	3103012029	6	2017	03103
304	3103032006	102	2017	03103
305	3103032014	7	2017	03103
306	3103032019	58	2017	03103
307	3103032026	10	2017	03103
308	3103032027	3	2017	03103
309	3103042001	15	2017	03103
310	3103042010	3	2017	03103
311	3103042028	2	2017	03103
312	3103042031	1	2017	03103
313	3103042901	2	2017	03103
314	3103052020	17	2017	03103
315	3103052901	1	2017	03103
316	3103062011	5	2017	03103
317	3103062016	1	2017	03103
318	3103062901	4	2017	03103
319	3103072004	2	2017	03103
320	3103072012	1	2017	03103
321	3103072023	3	2017	03103
322	3103072901	1	2017	03103
458	3201012005	4	2017	03201
459	3201012901	2	2017	03201
460	3201022006	25	2017	03201
461	3201032007	13	2017	03201
462	3201032015	1	2017	03201
463	3201032019	2	2017	03201
464	3201032020	2	2017	03201
465	3201032901	13	2017	03201
601	3202022901	5	2017	03202
602	3202042008	16	2017	03202
738	3301032005	4	2017	03301
739	3301032017	12	2017	03301
740	3301032901	1	2017	03301
741	3301042005	19	2017	03301
742	3301042060	19	2017	03301
743	3301042901	3	2017	03301
744	3301052002	39	2017	03301
745	3301052006	7	2017	03301
746	3301052008	2	2017	03301
747	3301052010	15	2017	03301
748	3301052014	5	2017	03301
749	3301052028	53	2017	03301
750	3301052036	101	2017	03301
751	3301052038	9	2017	03301
752	3301052063	13	2017	03301
753	3301082012	35	2017	03301
754	3301082901	7	2017	03301
755	3301092043	2	2017	03301
756	3301092901	3	2017	03301
757	3301122004	3	2017	03301
758	3301122009	7	2017	03301
759	3301122025	19	2017	03301
760	3301122030	4	2017	03301
761	3301122032	6	2017	03301
762	3301122033	8	2017	03301
763	3301122901	2	2017	03301
764	3301132026	2	2017	03301
765	3301132901	3	2017	03301
766	3301152010	4	2017	03301
767	3301152024	1	2017	03301
768	3301152901	4	2017	03301
904	3302012002	58	2017	03302
905	3302012005	2	2017	03302
906	3302012029	1	2017	03302
907	3302022005	20	2017	03302
908	3302032005	34	2017	03302
909	3302032018	25	2017	03302
910	3302042005	17	2017	03302

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[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	03101	3101162050	4	2017	Copiapó	251396.0	2017	153937	38699138722
2	03101	3101162006	1	2017	Copiapó	251396.0	2017	153937	38699138722
3	03101	3101122013	109	2017	Copiapó	251396.0	2017	153937	38699138722
4	03101	3101122901	3	2017	Copiapó	251396.0	2017	153937	38699138722
5	03101	3101132901	1	2017	Copiapó	251396.0	2017	153937	38699138722
6	03101	3101222015	1	2017	Copiapó	251396.0	2017	153937	38699138722
7	03101	3101122047	4	2017	Copiapó	251396.0	2017	153937	38699138722
8	03101	3101172013	36	2017	Copiapó	251396.0	2017	153937	38699138722
9	03101	3101172017	30	2017	Copiapó	251396.0	2017	153937	38699138722
10	03101	3101172021	5	2017	Copiapó	251396.0	2017	153937	38699138722
11	03101	3101222048	1	2017	Copiapó	251396.0	2017	153937	38699138722
12	03101	3101092006	3	2017	Copiapó	251396.0	2017	153937	38699138722
13	03101	3101102901	7	2017	Copiapó	251396.0	2017	153937	38699138722
14	03101	3101122007	8	2017	Copiapó	251396.0	2017	153937	38699138722
15	03101	3101172901	3	2017	Copiapó	251396.0	2017	153937	38699138722
16	03101	3101192014	26	2017	Copiapó	251396.0	2017	153937	38699138722
17	03101	3101192901	1	2017	Copiapó	251396.0	2017	153937	38699138722
18	03101	3101212032	1	2017	Copiapó	251396.0	2017	153937	38699138722
19	03101	3101212901	2	2017	Copiapó	251396.0	2017	153937	38699138722
20	03101	3101172026	54	2017	Copiapó	251396.0	2017	153937	38699138722
21	03101	3101172035	34	2017	Copiapó	251396.0	2017	153937	38699138722
22	03101	3101172037	46	2017	Copiapó	251396.0	2017	153937	38699138722
30	03103	3103032014	7	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
31	03103	3103042001	15	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
32	03103	3103032019	58	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
33	03103	3103032026	10	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
34	03103	3103032027	3	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
35	03103	3103012002	2	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
36	03103	3103012003	8	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
37	03103	3103012022	24	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
38	03103	3103012029	6	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
39	03103	3103032006	102	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
40	03103	3103062901	4	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
41	03103	3103072004	2	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
42	03103	3103072012	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
43	03103	3103072023	3	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
44	03103	3103072901	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
45	03103	3103042010	3	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
46	03103	3103042028	2	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
47	03103	3103042031	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
48	03103	3103042901	2	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
49	03103	3103052020	17	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
50	03103	3103052901	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
51	03103	3103062011	5	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
52	03103	3103062016	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
61	03202	3202022901	5	2017	Diego de Almagro	326439.0	2017	13925	4545663075
62	03202	3202042008	16	2017	Diego de Almagro	326439.0	2017	13925	4545663075
63	03301	3301032005	4	2017	Vallenar	217644.6	2017	51917	11299454698
64	03301	3301032017	12	2017	Vallenar	217644.6	2017	51917	11299454698
65	03301	3301032901	1	2017	Vallenar	217644.6	2017	51917	11299454698
66	03301	3301042005	19	2017	Vallenar	217644.6	2017	51917	11299454698
67	03301	3301042060	19	2017	Vallenar	217644.6	2017	51917	11299454698
68	03301	3301042901	3	2017	Vallenar	217644.6	2017	51917	11299454698
69	03301	3301052002	39	2017	Vallenar	217644.6	2017	51917	11299454698
70	03301	3301052006	7	2017	Vallenar	217644.6	2017	51917	11299454698
71	03301	3301052008	2	2017	Vallenar	217644.6	2017	51917	11299454698
72	03301	3301052010	15	2017	Vallenar	217644.6	2017	51917	11299454698
73	03301	3301052014	5	2017	Vallenar	217644.6	2017	51917	11299454698
74	03301	3301052028	53	2017	Vallenar	217644.6	2017	51917	11299454698
75	03301	3301052036	101	2017	Vallenar	217644.6	2017	51917	11299454698
76	03301	3301052038	9	2017	Vallenar	217644.6	2017	51917	11299454698
77	03301	3301052063	13	2017	Vallenar	217644.6	2017	51917	11299454698
78	03301	3301082012	35	2017	Vallenar	217644.6	2017	51917	11299454698
79	03301	3301082901	7	2017	Vallenar	217644.6	2017	51917	11299454698
80	03301	3301092043	2	2017	Vallenar	217644.6	2017	51917	11299454698
81	03301	3301092901	3	2017	Vallenar	217644.6	2017	51917	11299454698
82	03301	3301122004	3	2017	Vallenar	217644.6	2017	51917	11299454698
83	03301	3301122009	7	2017	Vallenar	217644.6	2017	51917	11299454698
84	03301	3301122025	19	2017	Vallenar	217644.6	2017	51917	11299454698
85	03301	3301122030	4	2017	Vallenar	217644.6	2017	51917	11299454698
86	03301	3301122032	6	2017	Vallenar	217644.6	2017	51917	11299454698
87	03301	3301122033	8	2017	Vallenar	217644.6	2017	51917	11299454698
88	03301	3301122901	2	2017	Vallenar	217644.6	2017	51917	11299454698
89	03301	3301132026	2	2017	Vallenar	217644.6	2017	51917	11299454698
90	03301	3301132901	3	2017	Vallenar	217644.6	2017	51917	11299454698
91	03301	3301152010	4	2017	Vallenar	217644.6	2017	51917	11299454698
92	03301	3301152024	1	2017	Vallenar	217644.6	2017	51917	11299454698
93	03301	3301152901	4	2017	Vallenar	217644.6	2017	51917	11299454698
94	03302	3302012002	58	2017	Alto del Carmen	196109.9	2017	5299	1039186477
95	03302	3302012005	2	2017	Alto del Carmen	196109.9	2017	5299	1039186477
96	03302	3302012029	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477
97	03302	3302022005	20	2017	Alto del Carmen	196109.9	2017	5299	1039186477
98	03302	3302032005	34	2017	Alto del Carmen	196109.9	2017	5299	1039186477
99	03302	3302032018	25	2017	Alto del Carmen	196109.9	2017	5299	1039186477
100	03302	3302042005	17	2017	Alto del Carmen	196109.9	2017	5299	1039186477
101	03302	3302052005	10	2017	Alto del Carmen	196109.9	2017	5299	1039186477
102	03302	3302062032	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477
103	03302	3302062901	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477
104	03302	3302072009	2	2017	Alto del Carmen	196109.9	2017	5299	1039186477
105	03302	3302072031	3	2017	Alto del Carmen	196109.9	2017	5299	1039186477
106	03302	3302072034	25	2017	Alto del Carmen	196109.9	2017	5299	1039186477
107	03302	3302072901	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477
108	03302	3302082901	3	2017	Alto del Carmen	196109.9	2017	5299	1039186477
109	03302	3302092004	2	2017	Alto del Carmen	196109.9	2017	5299	1039186477
110	03302	3302092013	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477
111	03302	3302092033	3	2017	Alto del Carmen	196109.9	2017	5299	1039186477
112	03302	3302092901	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477
113	03302	3302102027	68	2017	Alto del Carmen	196109.9	2017	5299	1039186477
114	03302	3302102030	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477
115	03302	3302112015	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477

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	03101	3101162050	4	2017	Copiapó	251396.0	2017	153937	38699138722
2	03101	3101162006	1	2017	Copiapó	251396.0	2017	153937	38699138722
3	03101	3101122013	109	2017	Copiapó	251396.0	2017	153937	38699138722
4	03101	3101122901	3	2017	Copiapó	251396.0	2017	153937	38699138722
5	03101	3101132901	1	2017	Copiapó	251396.0	2017	153937	38699138722
6	03101	3101222015	1	2017	Copiapó	251396.0	2017	153937	38699138722
7	03101	3101122047	4	2017	Copiapó	251396.0	2017	153937	38699138722
8	03101	3101172013	36	2017	Copiapó	251396.0	2017	153937	38699138722
9	03101	3101172017	30	2017	Copiapó	251396.0	2017	153937	38699138722
10	03101	3101172021	5	2017	Copiapó	251396.0	2017	153937	38699138722
11	03101	3101222048	1	2017	Copiapó	251396.0	2017	153937	38699138722
12	03101	3101092006	3	2017	Copiapó	251396.0	2017	153937	38699138722
13	03101	3101102901	7	2017	Copiapó	251396.0	2017	153937	38699138722
14	03101	3101122007	8	2017	Copiapó	251396.0	2017	153937	38699138722
15	03101	3101172901	3	2017	Copiapó	251396.0	2017	153937	38699138722
16	03101	3101192014	26	2017	Copiapó	251396.0	2017	153937	38699138722
17	03101	3101192901	1	2017	Copiapó	251396.0	2017	153937	38699138722
18	03101	3101212032	1	2017	Copiapó	251396.0	2017	153937	38699138722
19	03101	3101212901	2	2017	Copiapó	251396.0	2017	153937	38699138722
20	03101	3101172026	54	2017	Copiapó	251396.0	2017	153937	38699138722
21	03101	3101172035	34	2017	Copiapó	251396.0	2017	153937	38699138722
22	03101	3101172037	46	2017	Copiapó	251396.0	2017	153937	38699138722
30	03103	3103032014	7	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
31	03103	3103042001	15	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
32	03103	3103032019	58	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
33	03103	3103032026	10	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
34	03103	3103032027	3	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
35	03103	3103012002	2	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
36	03103	3103012003	8	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
37	03103	3103012022	24	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
38	03103	3103012029	6	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
39	03103	3103032006	102	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
40	03103	3103062901	4	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
41	03103	3103072004	2	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
42	03103	3103072012	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
43	03103	3103072023	3	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
44	03103	3103072901	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
45	03103	3103042010	3	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
46	03103	3103042028	2	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
47	03103	3103042031	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
48	03103	3103042901	2	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
49	03103	3103052020	17	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
50	03103	3103052901	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
51	03103	3103062011	5	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
52	03103	3103062016	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816
61	03202	3202022901	5	2017	Diego de Almagro	326439.0	2017	13925	4545663075
62	03202	3202042008	16	2017	Diego de Almagro	326439.0	2017	13925	4545663075
63	03301	3301032005	4	2017	Vallenar	217644.6	2017	51917	11299454698
64	03301	3301032017	12	2017	Vallenar	217644.6	2017	51917	11299454698
65	03301	3301032901	1	2017	Vallenar	217644.6	2017	51917	11299454698
66	03301	3301042005	19	2017	Vallenar	217644.6	2017	51917	11299454698
67	03301	3301042060	19	2017	Vallenar	217644.6	2017	51917	11299454698
68	03301	3301042901	3	2017	Vallenar	217644.6	2017	51917	11299454698
69	03301	3301052002	39	2017	Vallenar	217644.6	2017	51917	11299454698
70	03301	3301052006	7	2017	Vallenar	217644.6	2017	51917	11299454698
71	03301	3301052008	2	2017	Vallenar	217644.6	2017	51917	11299454698
72	03301	3301052010	15	2017	Vallenar	217644.6	2017	51917	11299454698
73	03301	3301052014	5	2017	Vallenar	217644.6	2017	51917	11299454698
74	03301	3301052028	53	2017	Vallenar	217644.6	2017	51917	11299454698
75	03301	3301052036	101	2017	Vallenar	217644.6	2017	51917	11299454698
76	03301	3301052038	9	2017	Vallenar	217644.6	2017	51917	11299454698
77	03301	3301052063	13	2017	Vallenar	217644.6	2017	51917	11299454698
78	03301	3301082012	35	2017	Vallenar	217644.6	2017	51917	11299454698
79	03301	3301082901	7	2017	Vallenar	217644.6	2017	51917	11299454698
80	03301	3301092043	2	2017	Vallenar	217644.6	2017	51917	11299454698
81	03301	3301092901	3	2017	Vallenar	217644.6	2017	51917	11299454698
82	03301	3301122004	3	2017	Vallenar	217644.6	2017	51917	11299454698
83	03301	3301122009	7	2017	Vallenar	217644.6	2017	51917	11299454698
84	03301	3301122025	19	2017	Vallenar	217644.6	2017	51917	11299454698
85	03301	3301122030	4	2017	Vallenar	217644.6	2017	51917	11299454698
86	03301	3301122032	6	2017	Vallenar	217644.6	2017	51917	11299454698
87	03301	3301122033	8	2017	Vallenar	217644.6	2017	51917	11299454698
88	03301	3301122901	2	2017	Vallenar	217644.6	2017	51917	11299454698
89	03301	3301132026	2	2017	Vallenar	217644.6	2017	51917	11299454698
90	03301	3301132901	3	2017	Vallenar	217644.6	2017	51917	11299454698
91	03301	3301152010	4	2017	Vallenar	217644.6	2017	51917	11299454698
92	03301	3301152024	1	2017	Vallenar	217644.6	2017	51917	11299454698
93	03301	3301152901	4	2017	Vallenar	217644.6	2017	51917	11299454698
94	03302	3302012002	58	2017	Alto del Carmen	196109.9	2017	5299	1039186477
95	03302	3302012005	2	2017	Alto del Carmen	196109.9	2017	5299	1039186477
96	03302	3302012029	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477
97	03302	3302022005	20	2017	Alto del Carmen	196109.9	2017	5299	1039186477
98	03302	3302032005	34	2017	Alto del Carmen	196109.9	2017	5299	1039186477
99	03302	3302032018	25	2017	Alto del Carmen	196109.9	2017	5299	1039186477
100	03302	3302042005	17	2017	Alto del Carmen	196109.9	2017	5299	1039186477
101	03302	3302052005	10	2017	Alto del Carmen	196109.9	2017	5299	1039186477
102	03302	3302062032	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477
103	03302	3302062901	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477
104	03302	3302072009	2	2017	Alto del Carmen	196109.9	2017	5299	1039186477
105	03302	3302072031	3	2017	Alto del Carmen	196109.9	2017	5299	1039186477
106	03302	3302072034	25	2017	Alto del Carmen	196109.9	2017	5299	1039186477
107	03302	3302072901	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477
108	03302	3302082901	3	2017	Alto del Carmen	196109.9	2017	5299	1039186477
109	03302	3302092004	2	2017	Alto del Carmen	196109.9	2017	5299	1039186477
110	03302	3302092013	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477
111	03302	3302092033	3	2017	Alto del Carmen	196109.9	2017	5299	1039186477
112	03302	3302092901	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477
113	03302	3302102027	68	2017	Alto del Carmen	196109.9	2017	5299	1039186477
114	03302	3302102030	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477
115	03302	3302112015	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477

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
3101092006	03101	3	2017	Copiapó	251396.0	2017	153937	38699138722	14	0.0000909	03101
3101102901	03101	7	2017	Copiapó	251396.0	2017	153937	38699138722	26	0.0001689	03101
3101122007	03101	8	2017	Copiapó	251396.0	2017	153937	38699138722	22	0.0001429	03101
3101122013	03101	109	2017	Copiapó	251396.0	2017	153937	38699138722	332	0.0021567	03101
3101122047	03101	4	2017	Copiapó	251396.0	2017	153937	38699138722	183	0.0011888	03101
3101122901	03101	3	2017	Copiapó	251396.0	2017	153937	38699138722	54	0.0003508	03101
3101132901	03101	1	2017	Copiapó	251396.0	2017	153937	38699138722	14	0.0000909	03101
3101162006	03101	1	2017	Copiapó	251396.0	2017	153937	38699138722	8	0.0000520	03101
3101162050	03101	4	2017	Copiapó	251396.0	2017	153937	38699138722	33	0.0002144	03101
3101172013	03101	36	2017	Copiapó	251396.0	2017	153937	38699138722	194	0.0012603	03101
3101172017	03101	30	2017	Copiapó	251396.0	2017	153937	38699138722	121	0.0007860	03101
3101172021	03101	5	2017	Copiapó	251396.0	2017	153937	38699138722	74	0.0004807	03101
3101172026	03101	54	2017	Copiapó	251396.0	2017	153937	38699138722	340	0.0022087	03101
3101172035	03101	34	2017	Copiapó	251396.0	2017	153937	38699138722	293	0.0019034	03101
3101172037	03101	46	2017	Copiapó	251396.0	2017	153937	38699138722	859	0.0055802	03101
3101172901	03101	3	2017	Copiapó	251396.0	2017	153937	38699138722	29	0.0001884	03101
3101192014	03101	26	2017	Copiapó	251396.0	2017	153937	38699138722	97	0.0006301	03101
3101192901	03101	1	2017	Copiapó	251396.0	2017	153937	38699138722	30	0.0001949	03101
3101212032	03101	1	2017	Copiapó	251396.0	2017	153937	38699138722	25	0.0001624	03101
3101212901	03101	2	2017	Copiapó	251396.0	2017	153937	38699138722	41	0.0002663	03101
3101222015	03101	1	2017	Copiapó	251396.0	2017	153937	38699138722	97	0.0006301	03101
3101222048	03101	1	2017	Copiapó	251396.0	2017	153937	38699138722	62	0.0004028	03101
3103012002	03103	2	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	30	0.0021400	03103
3103012003	03103	8	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	74	0.0052786	03103
3103012022	03103	24	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	553	0.0394465	03103
3103012029	03103	6	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	86	0.0061345	03103
3103032006	03103	102	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	476	0.0339539	03103
3103032014	03103	7	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	41	0.0029246	03103
3103032019	03103	58	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	224	0.0159783	03103
3103032026	03103	10	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	181	0.0129110	03103
3103032027	03103	3	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	27	0.0019260	03103
3103042001	03103	15	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	401	0.0286040	03103
3103042010	03103	3	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	41	0.0029246	03103
3103042028	03103	2	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	195	0.0139097	03103
3103042031	03103	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	19	0.0013553	03103
3103042901	03103	2	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	77	0.0054925	03103
3103052020	03103	17	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	952	0.0679078	03103
3103052901	03103	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	53	0.0037806	03103
3103062011	03103	5	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	119	0.0084885	03103
3103062016	03103	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	7	0.0004993	03103
3103062901	03103	4	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	122	0.0087025	03103
3103072004	03103	2	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	42	0.0029959	03103
3103072012	03103	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	27	0.0019260	03103
3103072023	03103	3	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	74	0.0052786	03103
3103072901	03103	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	77	0.0054925	03103
3202022901	03202	5	2017	Diego de Almagro	326439.0	2017	13925	4545663075	294	0.0211131	03202
3202042008	03202	16	2017	Diego de Almagro	326439.0	2017	13925	4545663075	319	0.0229084	03202
3301032005	03301	4	2017	Vallenar	217644.6	2017	51917	11299454698	49	0.0009438	03301
3301032017	03301	12	2017	Vallenar	217644.6	2017	51917	11299454698	386	0.0074349	03301
3301032901	03301	1	2017	Vallenar	217644.6	2017	51917	11299454698	9	0.0001734	03301
3301042005	03301	19	2017	Vallenar	217644.6	2017	51917	11299454698	184	0.0035441	03301
3301042060	03301	19	2017	Vallenar	217644.6	2017	51917	11299454698	172	0.0033130	03301
3301042901	03301	3	2017	Vallenar	217644.6	2017	51917	11299454698	23	0.0004430	03301
3301052002	03301	39	2017	Vallenar	217644.6	2017	51917	11299454698	574	0.0110561	03301
3301052006	03301	7	2017	Vallenar	217644.6	2017	51917	11299454698	55	0.0010594	03301
3301052008	03301	2	2017	Vallenar	217644.6	2017	51917	11299454698	17	0.0003274	03301
3301052010	03301	15	2017	Vallenar	217644.6	2017	51917	11299454698	343	0.0066067	03301
3301052014	03301	5	2017	Vallenar	217644.6	2017	51917	11299454698	37	0.0007127	03301
3301052028	03301	53	2017	Vallenar	217644.6	2017	51917	11299454698	523	0.0100738	03301
3301052036	03301	101	2017	Vallenar	217644.6	2017	51917	11299454698	537	0.0103434	03301
3301052038	03301	9	2017	Vallenar	217644.6	2017	51917	11299454698	87	0.0016758	03301
3301052063	03301	13	2017	Vallenar	217644.6	2017	51917	11299454698	90	0.0017335	03301
3301082012	03301	35	2017	Vallenar	217644.6	2017	51917	11299454698	796	0.0153322	03301
3301082901	03301	7	2017	Vallenar	217644.6	2017	51917	11299454698	177	0.0034093	03301
3301092043	03301	2	2017	Vallenar	217644.6	2017	51917	11299454698	60	0.0011557	03301
3301092901	03301	3	2017	Vallenar	217644.6	2017	51917	11299454698	141	0.0027159	03301
3301122004	03301	3	2017	Vallenar	217644.6	2017	51917	11299454698	44	0.0008475	03301
3301122009	03301	7	2017	Vallenar	217644.6	2017	51917	11299454698	198	0.0038138	03301
3301122025	03301	19	2017	Vallenar	217644.6	2017	51917	11299454698	264	0.0050850	03301
3301122030	03301	4	2017	Vallenar	217644.6	2017	51917	11299454698	159	0.0030626	03301
3301122032	03301	6	2017	Vallenar	217644.6	2017	51917	11299454698	43	0.0008282	03301
3301122033	03301	8	2017	Vallenar	217644.6	2017	51917	11299454698	108	0.0020802	03301
3301122901	03301	2	2017	Vallenar	217644.6	2017	51917	11299454698	46	0.0008860	03301
3301132026	03301	2	2017	Vallenar	217644.6	2017	51917	11299454698	146	0.0028122	03301
3301132901	03301	3	2017	Vallenar	217644.6	2017	51917	11299454698	33	0.0006356	03301
3301152010	03301	4	2017	Vallenar	217644.6	2017	51917	11299454698	218	0.0041990	03301
3301152024	03301	1	2017	Vallenar	217644.6	2017	51917	11299454698	43	0.0008282	03301
3301152901	03301	4	2017	Vallenar	217644.6	2017	51917	11299454698	124	0.0023884	03301
3302012002	03302	58	2017	Alto del Carmen	196109.9	2017	5299	1039186477	645	0.1217211	03302
3302012005	03302	2	2017	Alto del Carmen	196109.9	2017	5299	1039186477	48	0.0090583	03302
3302012029	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	117	0.0220796	03302
3302022005	03302	20	2017	Alto del Carmen	196109.9	2017	5299	1039186477	540	0.1019060	03302
3302032005	03302	34	2017	Alto del Carmen	196109.9	2017	5299	1039186477	821	0.1549349	03302
3302032018	03302	25	2017	Alto del Carmen	196109.9	2017	5299	1039186477	512	0.0966220	03302
3302042005	03302	17	2017	Alto del Carmen	196109.9	2017	5299	1039186477	560	0.1056803	03302
3302052005	03302	10	2017	Alto del Carmen	196109.9	2017	5299	1039186477	419	0.0790715	03302
3302062032	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	54	0.0101906	03302
3302062901	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	13	0.0024533	03302
3302072009	03302	2	2017	Alto del Carmen	196109.9	2017	5299	1039186477	30	0.0056614	03302
3302072031	03302	3	2017	Alto del Carmen	196109.9	2017	5299	1039186477	96	0.0181166	03302
3302072034	03302	25	2017	Alto del Carmen	196109.9	2017	5299	1039186477	229	0.0432157	03302
3302072901	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	52	0.0098132	03302
3302082901	03302	3	2017	Alto del Carmen	196109.9	2017	5299	1039186477	74	0.0139649	03302
3302092004	03302	2	2017	Alto del Carmen	196109.9	2017	5299	1039186477	15	0.0028307	03302
3302092013	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	58	0.0109455	03302
3302092033	03302	3	2017	Alto del Carmen	196109.9	2017	5299	1039186477	149	0.0281185	03302
3302092901	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	10	0.0018871	03302
3302102027	03302	68	2017	Alto del Carmen	196109.9	2017	5299	1039186477	299	0.0564257	03302
3302102030	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	115	0.0217022	03302
3302112015	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	53	0.0100019	03302

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
3101092006	03101	3	2017	Copiapó	251396.0	2017	153937	38699138722	14	0.0000909	03101	3519543
3101102901	03101	7	2017	Copiapó	251396.0	2017	153937	38699138722	26	0.0001689	03101	6536295
3101122007	03101	8	2017	Copiapó	251396.0	2017	153937	38699138722	22	0.0001429	03101	5530711
3101122013	03101	109	2017	Copiapó	251396.0	2017	153937	38699138722	332	0.0021567	03101	83463456
3101122047	03101	4	2017	Copiapó	251396.0	2017	153937	38699138722	183	0.0011888	03101	46005459
3101122901	03101	3	2017	Copiapó	251396.0	2017	153937	38699138722	54	0.0003508	03101	13575381
3101132901	03101	1	2017	Copiapó	251396.0	2017	153937	38699138722	14	0.0000909	03101	3519543
3101162006	03101	1	2017	Copiapó	251396.0	2017	153937	38699138722	8	0.0000520	03101	2011168
3101162050	03101	4	2017	Copiapó	251396.0	2017	153937	38699138722	33	0.0002144	03101	8296066
3101172013	03101	36	2017	Copiapó	251396.0	2017	153937	38699138722	194	0.0012603	03101	48770815
3101172017	03101	30	2017	Copiapó	251396.0	2017	153937	38699138722	121	0.0007860	03101	30418910
3101172021	03101	5	2017	Copiapó	251396.0	2017	153937	38699138722	74	0.0004807	03101	18603300
3101172026	03101	54	2017	Copiapó	251396.0	2017	153937	38699138722	340	0.0022087	03101	85474624
3101172035	03101	34	2017	Copiapó	251396.0	2017	153937	38699138722	293	0.0019034	03101	73659014
3101172037	03101	46	2017	Copiapó	251396.0	2017	153937	38699138722	859	0.0055802	03101	215949123
3101172901	03101	3	2017	Copiapó	251396.0	2017	153937	38699138722	29	0.0001884	03101	7290483
3101192014	03101	26	2017	Copiapó	251396.0	2017	153937	38699138722	97	0.0006301	03101	24385407
3101192901	03101	1	2017	Copiapó	251396.0	2017	153937	38699138722	30	0.0001949	03101	7541879
3101212032	03101	1	2017	Copiapó	251396.0	2017	153937	38699138722	25	0.0001624	03101	6284899
3101212901	03101	2	2017	Copiapó	251396.0	2017	153937	38699138722	41	0.0002663	03101	10307234
3101222015	03101	1	2017	Copiapó	251396.0	2017	153937	38699138722	97	0.0006301	03101	24385407
3101222048	03101	1	2017	Copiapó	251396.0	2017	153937	38699138722	62	0.0004028	03101	15586549
3103012002	03103	2	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	30	0.0021400	03103	8634583
3103012003	03103	8	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	74	0.0052786	03103	21298639
3103012022	03103	24	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	553	0.0394465	03103	159164154
3103012029	03103	6	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	86	0.0061345	03103	24752472
3103032006	03103	102	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	476	0.0339539	03103	137002056
3103032014	03103	7	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	41	0.0029246	03103	11800597
3103032019	03103	58	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	224	0.0159783	03103	64471556
3103032026	03103	10	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	181	0.0129110	03103	52095320
3103032027	03103	3	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	27	0.0019260	03103	7771125
3103042001	03103	15	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	401	0.0286040	03103	115415598
3103042010	03103	3	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	41	0.0029246	03103	11800597
3103042028	03103	2	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	195	0.0139097	03103	56124792
3103042031	03103	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	19	0.0013553	03103	5468569
3103042901	03103	2	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	77	0.0054925	03103	22162097
3103052020	03103	17	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	952	0.0679078	03103	274004113
3103052901	03103	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	53	0.0037806	03103	15254431
3103062011	03103	5	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	119	0.0084885	03103	34250514
3103062016	03103	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	7	0.0004993	03103	2014736
3103062901	03103	4	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	122	0.0087025	03103	35113972
3103072004	03103	2	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	42	0.0029959	03103	12088417
3103072012	03103	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	27	0.0019260	03103	7771125
3103072023	03103	3	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	74	0.0052786	03103	21298639
3103072901	03103	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	77	0.0054925	03103	22162097
3202022901	03202	5	2017	Diego de Almagro	326439.0	2017	13925	4545663075	294	0.0211131	03202	95973066
3202042008	03202	16	2017	Diego de Almagro	326439.0	2017	13925	4545663075	319	0.0229084	03202	104134041
3301032005	03301	4	2017	Vallenar	217644.6	2017	51917	11299454698	49	0.0009438	03301	10664585
3301032017	03301	12	2017	Vallenar	217644.6	2017	51917	11299454698	386	0.0074349	03301	84010816
3301032901	03301	1	2017	Vallenar	217644.6	2017	51917	11299454698	9	0.0001734	03301	1958801
3301042005	03301	19	2017	Vallenar	217644.6	2017	51917	11299454698	184	0.0035441	03301	40046606
3301042060	03301	19	2017	Vallenar	217644.6	2017	51917	11299454698	172	0.0033130	03301	37434871
3301042901	03301	3	2017	Vallenar	217644.6	2017	51917	11299454698	23	0.0004430	03301	5005826
3301052002	03301	39	2017	Vallenar	217644.6	2017	51917	11299454698	574	0.0110561	03301	124928000
3301052006	03301	7	2017	Vallenar	217644.6	2017	51917	11299454698	55	0.0010594	03301	11970453
3301052008	03301	2	2017	Vallenar	217644.6	2017	51917	11299454698	17	0.0003274	03301	3699958
3301052010	03301	15	2017	Vallenar	217644.6	2017	51917	11299454698	343	0.0066067	03301	74652098
3301052014	03301	5	2017	Vallenar	217644.6	2017	51917	11299454698	37	0.0007127	03301	8052850
3301052028	03301	53	2017	Vallenar	217644.6	2017	51917	11299454698	523	0.0100738	03301	113828126
3301052036	03301	101	2017	Vallenar	217644.6	2017	51917	11299454698	537	0.0103434	03301	116875150
3301052038	03301	9	2017	Vallenar	217644.6	2017	51917	11299454698	87	0.0016758	03301	18935080
3301052063	03301	13	2017	Vallenar	217644.6	2017	51917	11299454698	90	0.0017335	03301	19588014
3301082012	03301	35	2017	Vallenar	217644.6	2017	51917	11299454698	796	0.0153322	03301	173245102
3301082901	03301	7	2017	Vallenar	217644.6	2017	51917	11299454698	177	0.0034093	03301	38523094
3301092043	03301	2	2017	Vallenar	217644.6	2017	51917	11299454698	60	0.0011557	03301	13058676
3301092901	03301	3	2017	Vallenar	217644.6	2017	51917	11299454698	141	0.0027159	03301	30687889
3301122004	03301	3	2017	Vallenar	217644.6	2017	51917	11299454698	44	0.0008475	03301	9576362
3301122009	03301	7	2017	Vallenar	217644.6	2017	51917	11299454698	198	0.0038138	03301	43093631
3301122025	03301	19	2017	Vallenar	217644.6	2017	51917	11299454698	264	0.0050850	03301	57458174
3301122030	03301	4	2017	Vallenar	217644.6	2017	51917	11299454698	159	0.0030626	03301	34605491
3301122032	03301	6	2017	Vallenar	217644.6	2017	51917	11299454698	43	0.0008282	03301	9358718
3301122033	03301	8	2017	Vallenar	217644.6	2017	51917	11299454698	108	0.0020802	03301	23505617
3301122901	03301	2	2017	Vallenar	217644.6	2017	51917	11299454698	46	0.0008860	03301	10011652
3301132026	03301	2	2017	Vallenar	217644.6	2017	51917	11299454698	146	0.0028122	03301	31776112
3301132901	03301	3	2017	Vallenar	217644.6	2017	51917	11299454698	33	0.0006356	03301	7182272
3301152010	03301	4	2017	Vallenar	217644.6	2017	51917	11299454698	218	0.0041990	03301	47446523
3301152024	03301	1	2017	Vallenar	217644.6	2017	51917	11299454698	43	0.0008282	03301	9358718
3301152901	03301	4	2017	Vallenar	217644.6	2017	51917	11299454698	124	0.0023884	03301	26987930
3302012002	03302	58	2017	Alto del Carmen	196109.9	2017	5299	1039186477	645	0.1217211	03302	126490900
3302012005	03302	2	2017	Alto del Carmen	196109.9	2017	5299	1039186477	48	0.0090583	03302	9413276
3302012029	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	117	0.0220796	03302	22944861
3302022005	03302	20	2017	Alto del Carmen	196109.9	2017	5299	1039186477	540	0.1019060	03302	105899358
3302032005	03302	34	2017	Alto del Carmen	196109.9	2017	5299	1039186477	821	0.1549349	03302	161006246
3302032018	03302	25	2017	Alto del Carmen	196109.9	2017	5299	1039186477	512	0.0966220	03302	100408280
3302042005	03302	17	2017	Alto del Carmen	196109.9	2017	5299	1039186477	560	0.1056803	03302	109821556
3302052005	03302	10	2017	Alto del Carmen	196109.9	2017	5299	1039186477	419	0.0790715	03302	82170057
3302062032	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	54	0.0101906	03302	10589936
3302062901	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	13	0.0024533	03302	2549429
3302072009	03302	2	2017	Alto del Carmen	196109.9	2017	5299	1039186477	30	0.0056614	03302	5883298
3302072031	03302	3	2017	Alto del Carmen	196109.9	2017	5299	1039186477	96	0.0181166	03302	18826553
3302072034	03302	25	2017	Alto del Carmen	196109.9	2017	5299	1039186477	229	0.0432157	03302	44909172
3302072901	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	52	0.0098132	03302	10197716
3302082901	03302	3	2017	Alto del Carmen	196109.9	2017	5299	1039186477	74	0.0139649	03302	14512134
3302092004	03302	2	2017	Alto del Carmen	196109.9	2017	5299	1039186477	15	0.0028307	03302	2941649
3302092013	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	58	0.0109455	03302	11374375
3302092033	03302	3	2017	Alto del Carmen	196109.9	2017	5299	1039186477	149	0.0281185	03302	29220378
3302092901	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	10	0.0018871	03302	1961099
3302102027	03302	68	2017	Alto del Carmen	196109.9	2017	5299	1039186477	299	0.0564257	03302	58636867
3302102030	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	115	0.0217022	03302	22552641
3302112015	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	53	0.0100019	03302	10393826

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 
## -89798935 -20574489 -13271495   5424995 227039952 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 23626444    4294935   5.501 2.23e-07 ***
## Freq.x       1372807     179961   7.628 6.71e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 39540000 on 118 degrees of freedom
## Multiple R-squared:  0.3303, Adjusted R-squared:  0.3246 
## F-statistic: 58.19 on 1 and 118 DF,  p-value: 6.706e-12

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 
## -89798935 -20574489 -13271495   5424995 227039952 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 23626444    4294935   5.501 2.23e-07 ***
## Freq.x       1372807     179961   7.628 6.71e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 39540000 on 118 degrees of freedom
## Multiple R-squared:  0.3303, Adjusted R-squared:  0.3246 
## F-statistic: 58.19 on 1 and 118 DF,  p-value: 6.706e-12

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 
## -89798935 -20574489 -13271495   5424995 227039952 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 23626444    4294935   5.501 2.23e-07 ***
## Freq.x       1372807     179961   7.628 6.71e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 39540000 on 118 degrees of freedom
## Multiple R-squared:  0.3303, Adjusted R-squared:  0.3246 
## F-statistic: 58.19 on 1 and 118 DF,  p-value: 6.706e-12

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 
## -55556683 -21917409  -4430049  12694218 204565052 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -597417    5394109  -0.111    0.912    
## log(Freq.x) 24719804    2523641   9.795   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 35880000 on 118 degrees of freedom
## Multiple R-squared:  0.4485, Adjusted R-squared:  0.4438 
## F-statistic: 95.95 on 1 and 118 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 
## -71103722 -17375753  -7212265   7008158 214441569 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -2445006    5645223  -0.433    0.666    
## sqrt(Freq.x) 15039040    1569733   9.581   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 36240000 on 118 degrees of freedom
## Multiple R-squared:  0.4375, Adjusted R-squared:  0.4328 
## F-statistic: 91.79 on 1 and 118 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 
## -4632.1 -1605.1  -496.5  1267.9  9665.5 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   2396.93     354.04    6.77 5.29e-10 ***
## sqrt(Freq.x)  1089.14      98.45   11.06  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2273 on 118 degrees of freedom
## Multiple R-squared:  0.5091, Adjusted R-squared:  0.505 
## F-statistic: 122.4 on 1 and 118 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 
## -1.89682 -0.59033 -0.02455  0.71327  2.04770 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   15.8225     0.1323  119.57   <2e-16 ***
## sqrt(Freq.x)   0.3780     0.0368   10.27   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8494 on 118 degrees of freedom
## Multiple R-squared:  0.4721, Adjusted R-squared:  0.4676 
## F-statistic: 105.5 on 1 and 118 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 
## -4876.6 -1655.9  -203.1  1240.7  8900.5 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   2456.4      327.9    7.49 1.37e-11 ***
## log(Freq.x)   1834.0      153.4   11.95  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2182 on 118 degrees of freedom
## Multiple R-squared:  0.5477, Adjusted R-squared:  0.5439 
## F-statistic: 142.9 on 1 and 118 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 
## -2.20684 -0.56691  0.00855  0.65431  1.75500 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 15.80239    0.11831  133.57   <2e-16 ***
## log(Freq.x)  0.66047    0.05535   11.93   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.787 on 118 degrees of freedom
## Multiple R-squared:  0.5468, Adjusted R-squared:  0.543 
## F-statistic: 142.4 on 1 and 118 DF,  p-value: < 2.2e-16

9 Modelo raíz-log (raíz-log)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.5439).

9.1 Diagrama de dispersión sobre raíz-log

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

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

9.2 Modelo raíz-log

Observemos nuevamente el resultado sobre raíz-log.

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 
## -4876.6 -1655.9  -203.1  1240.7  8900.5 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   2456.4      327.9    7.49 1.37e-11 ***
## log(Freq.x)   1834.0      153.4   11.95  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2182 on 118 degrees of freedom
## Multiple R-squared:  0.5477, Adjusted R-squared:  0.5439 
## F-statistic: 142.9 on 1 and 118 DF,  p-value: < 2.2e-16

ggplot(h_y_m_comuna_corr_01, aes(x = log(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

\[ \hat Y = {2456.4}^2 + 2 2456.4 1834.0 \ln{X}+ 1834.0 ^2 ln^2X \]

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$est_ing <- (2456.4)^2 + 2 * 2456.4 * 1834.0  *log(h_y_m_comuna_corr_01$Freq.x)+  1834.0 ^2* (log(h_y_m_comuna_corr_01$Freq.x))^2

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
3101092006	03101	3	2017	Copiapó	251396.0	2017	153937	38699138722	14	0.0000909	03101	3519543	19992121
3101102901	03101	7	2017	Copiapó	251396.0	2017	153937	38699138722	26	0.0001689	03101	6536295	36303026
3101122007	03101	8	2017	Copiapó	251396.0	2017	153937	38699138722	22	0.0001429	03101	5530711	39314101
3101122013	03101	109	2017	Copiapó	251396.0	2017	153937	38699138722	332	0.0021567	03101	83463456	122330944
3101122047	03101	4	2017	Copiapó	251396.0	2017	153937	38699138722	183	0.0011888	03101	46005459	24988640
3101122901	03101	3	2017	Copiapó	251396.0	2017	153937	38699138722	54	0.0003508	03101	13575381	19992121
3101132901	03101	1	2017	Copiapó	251396.0	2017	153937	38699138722	14	0.0000909	03101	3519543	6033901
3101162006	03101	1	2017	Copiapó	251396.0	2017	153937	38699138722	8	0.0000520	03101	2011168	6033901
3101162050	03101	4	2017	Copiapó	251396.0	2017	153937	38699138722	33	0.0002144	03101	8296066	24988640
3101172013	03101	36	2017	Copiapó	251396.0	2017	153937	38699138722	194	0.0012603	03101	48770815	81515144
3101172017	03101	30	2017	Copiapó	251396.0	2017	153937	38699138722	121	0.0007860	03101	30418910	75589044
3101172021	03101	5	2017	Copiapó	251396.0	2017	153937	38699138722	74	0.0004807	03101	18603300	29247644
3101172026	03101	54	2017	Copiapó	251396.0	2017	153937	38699138722	340	0.0022087	03101	85474624	95495829
3101172035	03101	34	2017	Copiapó	251396.0	2017	153937	38699138722	293	0.0019034	03101	73659014	79633228
3101172037	03101	46	2017	Copiapó	251396.0	2017	153937	38699138722	859	0.0055802	03101	215949123	89834916
3101172901	03101	3	2017	Copiapó	251396.0	2017	153937	38699138722	29	0.0001884	03101	7290483	19992121
3101192014	03101	26	2017	Copiapó	251396.0	2017	153937	38699138722	97	0.0006301	03101	24385407	71094392
3101192901	03101	1	2017	Copiapó	251396.0	2017	153937	38699138722	30	0.0001949	03101	7541879	6033901
3101212032	03101	1	2017	Copiapó	251396.0	2017	153937	38699138722	25	0.0001624	03101	6284899	6033901
3101212901	03101	2	2017	Copiapó	251396.0	2017	153937	38699138722	41	0.0002663	03101	10307234	13895240
3101222015	03101	1	2017	Copiapó	251396.0	2017	153937	38699138722	97	0.0006301	03101	24385407	6033901
3101222048	03101	1	2017	Copiapó	251396.0	2017	153937	38699138722	62	0.0004028	03101	15586549	6033901
3103012002	03103	2	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	30	0.0021400	03103	8634583	13895240
3103012003	03103	8	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	74	0.0052786	03103	21298639	39314101
3103012022	03103	24	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	553	0.0394465	03103	159164154	68640409
3103012029	03103	6	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	86	0.0061345	03103	24752472	32976155
3103032006	03103	102	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	476	0.0339539	03103	137002056	119652973
3103032014	03103	7	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	41	0.0029246	03103	11800597	36303026
3103032019	03103	58	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	224	0.0159783	03103	64471556	98074410
3103032026	03103	10	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	181	0.0129110	03103	52095320	44613597
3103032027	03103	3	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	27	0.0019260	03103	7771125	19992121
3103042001	03103	15	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	401	0.0286040	03103	115415598	55100396
3103042010	03103	3	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	41	0.0029246	03103	11800597	19992121
3103042028	03103	2	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	195	0.0139097	03103	56124792	13895240
3103042031	03103	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	19	0.0013553	03103	5468569	6033901
3103042901	03103	2	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	77	0.0054925	03103	22162097	13895240
3103052020	03103	17	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	952	0.0679078	03103	274004113	58560959
3103052901	03103	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	53	0.0037806	03103	15254431	6033901
3103062011	03103	5	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	119	0.0084885	03103	34250514	29247644
3103062016	03103	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	7	0.0004993	03103	2014736	6033901
3103062901	03103	4	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	122	0.0087025	03103	35113972	24988640
3103072004	03103	2	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	42	0.0029959	03103	12088417	13895240
3103072012	03103	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	27	0.0019260	03103	7771125	6033901
3103072023	03103	3	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	74	0.0052786	03103	21298639	19992121
3103072901	03103	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	77	0.0054925	03103	22162097	6033901
3202022901	03202	5	2017	Diego de Almagro	326439.0	2017	13925	4545663075	294	0.0211131	03202	95973066	29247644
3202042008	03202	16	2017	Diego de Almagro	326439.0	2017	13925	4545663075	319	0.0229084	03202	104134041	56871624
3301032005	03301	4	2017	Vallenar	217644.6	2017	51917	11299454698	49	0.0009438	03301	10664585	24988640
3301032017	03301	12	2017	Vallenar	217644.6	2017	51917	11299454698	386	0.0074349	03301	84010816	49192251
3301032901	03301	1	2017	Vallenar	217644.6	2017	51917	11299454698	9	0.0001734	03301	1958801	6033901
3301042005	03301	19	2017	Vallenar	217644.6	2017	51917	11299454698	184	0.0035441	03301	40046606	61724609
3301042060	03301	19	2017	Vallenar	217644.6	2017	51917	11299454698	172	0.0033130	03301	37434871	61724609
3301042901	03301	3	2017	Vallenar	217644.6	2017	51917	11299454698	23	0.0004430	03301	5005826	19992121
3301052002	03301	39	2017	Vallenar	217644.6	2017	51917	11299454698	574	0.0110561	03301	124928000	84187452
3301052006	03301	7	2017	Vallenar	217644.6	2017	51917	11299454698	55	0.0010594	03301	11970453	36303026
3301052008	03301	2	2017	Vallenar	217644.6	2017	51917	11299454698	17	0.0003274	03301	3699958	13895240
3301052010	03301	15	2017	Vallenar	217644.6	2017	51917	11299454698	343	0.0066067	03301	74652098	55100396
3301052014	03301	5	2017	Vallenar	217644.6	2017	51917	11299454698	37	0.0007127	03301	8052850	29247644
3301052028	03301	53	2017	Vallenar	217644.6	2017	51917	11299454698	523	0.0100738	03301	113828126	94826996
3301052036	03301	101	2017	Vallenar	217644.6	2017	51917	11299454698	537	0.0103434	03301	116875150	119257998
3301052038	03301	9	2017	Vallenar	217644.6	2017	51917	11299454698	87	0.0016758	03301	18935080	42069621
3301052063	03301	13	2017	Vallenar	217644.6	2017	51917	11299454698	90	0.0017335	03301	19588014	51273005
3301082012	03301	35	2017	Vallenar	217644.6	2017	51917	11299454698	796	0.0153322	03301	173245102	80584883
3301082901	03301	7	2017	Vallenar	217644.6	2017	51917	11299454698	177	0.0034093	03301	38523094	36303026
3301092043	03301	2	2017	Vallenar	217644.6	2017	51917	11299454698	60	0.0011557	03301	13058676	13895240
3301092901	03301	3	2017	Vallenar	217644.6	2017	51917	11299454698	141	0.0027159	03301	30687889	19992121
3301122004	03301	3	2017	Vallenar	217644.6	2017	51917	11299454698	44	0.0008475	03301	9576362	19992121
3301122009	03301	7	2017	Vallenar	217644.6	2017	51917	11299454698	198	0.0038138	03301	43093631	36303026
3301122025	03301	19	2017	Vallenar	217644.6	2017	51917	11299454698	264	0.0050850	03301	57458174	61724609
3301122030	03301	4	2017	Vallenar	217644.6	2017	51917	11299454698	159	0.0030626	03301	34605491	24988640
3301122032	03301	6	2017	Vallenar	217644.6	2017	51917	11299454698	43	0.0008282	03301	9358718	32976155
3301122033	03301	8	2017	Vallenar	217644.6	2017	51917	11299454698	108	0.0020802	03301	23505617	39314101
3301122901	03301	2	2017	Vallenar	217644.6	2017	51917	11299454698	46	0.0008860	03301	10011652	13895240
3301132026	03301	2	2017	Vallenar	217644.6	2017	51917	11299454698	146	0.0028122	03301	31776112	13895240
3301132901	03301	3	2017	Vallenar	217644.6	2017	51917	11299454698	33	0.0006356	03301	7182272	19992121
3301152010	03301	4	2017	Vallenar	217644.6	2017	51917	11299454698	218	0.0041990	03301	47446523	24988640
3301152024	03301	1	2017	Vallenar	217644.6	2017	51917	11299454698	43	0.0008282	03301	9358718	6033901
3301152901	03301	4	2017	Vallenar	217644.6	2017	51917	11299454698	124	0.0023884	03301	26987930	24988640
3302012002	03302	58	2017	Alto del Carmen	196109.9	2017	5299	1039186477	645	0.1217211	03302	126490900	98074410
3302012005	03302	2	2017	Alto del Carmen	196109.9	2017	5299	1039186477	48	0.0090583	03302	9413276	13895240
3302012029	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	117	0.0220796	03302	22944861	6033901
3302022005	03302	20	2017	Alto del Carmen	196109.9	2017	5299	1039186477	540	0.1019060	03302	105899358	63211611
3302032005	03302	34	2017	Alto del Carmen	196109.9	2017	5299	1039186477	821	0.1549349	03302	161006246	79633228
3302032018	03302	25	2017	Alto del Carmen	196109.9	2017	5299	1039186477	512	0.0966220	03302	100408280	69886561
3302042005	03302	17	2017	Alto del Carmen	196109.9	2017	5299	1039186477	560	0.1056803	03302	109821556	58560959
3302052005	03302	10	2017	Alto del Carmen	196109.9	2017	5299	1039186477	419	0.0790715	03302	82170057	44613597
3302062032	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	54	0.0101906	03302	10589936	6033901
3302062901	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	13	0.0024533	03302	2549429	6033901
3302072009	03302	2	2017	Alto del Carmen	196109.9	2017	5299	1039186477	30	0.0056614	03302	5883298	13895240
3302072031	03302	3	2017	Alto del Carmen	196109.9	2017	5299	1039186477	96	0.0181166	03302	18826553	19992121
3302072034	03302	25	2017	Alto del Carmen	196109.9	2017	5299	1039186477	229	0.0432157	03302	44909172	69886561
3302072901	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	52	0.0098132	03302	10197716	6033901
3302082901	03302	3	2017	Alto del Carmen	196109.9	2017	5299	1039186477	74	0.0139649	03302	14512134	19992121
3302092004	03302	2	2017	Alto del Carmen	196109.9	2017	5299	1039186477	15	0.0028307	03302	2941649	13895240
3302092013	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	58	0.0109455	03302	11374375	6033901
3302092033	03302	3	2017	Alto del Carmen	196109.9	2017	5299	1039186477	149	0.0281185	03302	29220378	19992121
3302092901	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	10	0.0018871	03302	1961099	6033901
3302102027	03302	68	2017	Alto del Carmen	196109.9	2017	5299	1039186477	299	0.0564257	03302	58636867	103937559
3302102030	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	115	0.0217022	03302	22552641	6033901
3302112015	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	53	0.0100019	03302	10393826	6033901

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
3101092006	03101	3	2017	Copiapó	251396.0	2017	153937	38699138722	14	0.0000909	03101	3519543	19992121	1428008.62
3101102901	03101	7	2017	Copiapó	251396.0	2017	153937	38699138722	26	0.0001689	03101	6536295	36303026	1396270.21
3101122007	03101	8	2017	Copiapó	251396.0	2017	153937	38699138722	22	0.0001429	03101	5530711	39314101	1787004.60
3101122013	03101	109	2017	Copiapó	251396.0	2017	153937	38699138722	332	0.0021567	03101	83463456	122330944	368466.70
3101122047	03101	4	2017	Copiapó	251396.0	2017	153937	38699138722	183	0.0011888	03101	46005459	24988640	136549.94
3101122901	03101	3	2017	Copiapó	251396.0	2017	153937	38699138722	54	0.0003508	03101	13575381	19992121	370224.46
3101132901	03101	1	2017	Copiapó	251396.0	2017	153937	38699138722	14	0.0000909	03101	3519543	6033901	430992.93
3101162006	03101	1	2017	Copiapó	251396.0	2017	153937	38699138722	8	0.0000520	03101	2011168	6033901	754237.62
3101162050	03101	4	2017	Copiapó	251396.0	2017	153937	38699138722	33	0.0002144	03101	8296066	24988640	757231.51
3101172013	03101	36	2017	Copiapó	251396.0	2017	153937	38699138722	194	0.0012603	03101	48770815	81515144	420181.15
3101172017	03101	30	2017	Copiapó	251396.0	2017	153937	38699138722	121	0.0007860	03101	30418910	75589044	624702.84
3101172021	03101	5	2017	Copiapó	251396.0	2017	153937	38699138722	74	0.0004807	03101	18603300	29247644	395238.44
3101172026	03101	54	2017	Copiapó	251396.0	2017	153937	38699138722	340	0.0022087	03101	85474624	95495829	280870.09
3101172035	03101	34	2017	Copiapó	251396.0	2017	153937	38699138722	293	0.0019034	03101	73659014	79633228	271785.76
3101172037	03101	46	2017	Copiapó	251396.0	2017	153937	38699138722	859	0.0055802	03101	215949123	89834916	104580.81
3101172901	03101	3	2017	Copiapó	251396.0	2017	153937	38699138722	29	0.0001884	03101	7290483	19992121	689383.47
3101192014	03101	26	2017	Copiapó	251396.0	2017	153937	38699138722	97	0.0006301	03101	24385407	71094392	732931.88
3101192901	03101	1	2017	Copiapó	251396.0	2017	153937	38699138722	30	0.0001949	03101	7541879	6033901	201130.03
3101212032	03101	1	2017	Copiapó	251396.0	2017	153937	38699138722	25	0.0001624	03101	6284899	6033901	241356.04
3101212901	03101	2	2017	Copiapó	251396.0	2017	153937	38699138722	41	0.0002663	03101	10307234	13895240	338908.29
3101222015	03101	1	2017	Copiapó	251396.0	2017	153937	38699138722	97	0.0006301	03101	24385407	6033901	62205.16
3101222048	03101	1	2017	Copiapó	251396.0	2017	153937	38699138722	62	0.0004028	03101	15586549	6033901	97320.98
3103012002	03103	2	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	30	0.0021400	03103	8634583	13895240	463174.66
3103012003	03103	8	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	74	0.0052786	03103	21298639	39314101	531271.64
3103012022	03103	24	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	553	0.0394465	03103	159164154	68640409	124123.70
3103012029	03103	6	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	86	0.0061345	03103	24752472	32976155	383443.67
3103032006	03103	102	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	476	0.0339539	03103	137002056	119652973	251371.79
3103032014	03103	7	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	41	0.0029246	03103	11800597	36303026	885439.65
3103032019	03103	58	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	224	0.0159783	03103	64471556	98074410	437832.19
3103032026	03103	10	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	181	0.0129110	03103	52095320	44613597	246483.96
3103032027	03103	3	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	27	0.0019260	03103	7771125	19992121	740448.92
3103042001	03103	15	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	401	0.0286040	03103	115415598	55100396	137407.47
3103042010	03103	3	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	41	0.0029246	03103	11800597	19992121	487612.70
3103042028	03103	2	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	195	0.0139097	03103	56124792	13895240	71257.64
3103042031	03103	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	19	0.0013553	03103	5468569	6033901	317573.73
3103042901	03103	2	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	77	0.0054925	03103	22162097	13895240	180457.66
3103052020	03103	17	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	952	0.0679078	03103	274004113	58560959	61513.61
3103052901	03103	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	53	0.0037806	03103	15254431	6033901	113847.19
3103062011	03103	5	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	119	0.0084885	03103	34250514	29247644	245778.52
3103062016	03103	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	7	0.0004993	03103	2014736	6033901	861985.85
3103062901	03103	4	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	122	0.0087025	03103	35113972	24988640	204824.92
3103072004	03103	2	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	42	0.0029959	03103	12088417	13895240	330839.04
3103072012	03103	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	27	0.0019260	03103	7771125	6033901	223477.81
3103072023	03103	3	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	74	0.0052786	03103	21298639	19992121	270163.79
3103072901	03103	1	2017	Tierra Amarilla	287819.4	2017	14019	4034940816	77	0.0054925	03103	22162097	6033901	78362.35
3202022901	03202	5	2017	Diego de Almagro	326439.0	2017	13925	4545663075	294	0.0211131	03202	95973066	29247644	99481.78
3202042008	03202	16	2017	Diego de Almagro	326439.0	2017	13925	4545663075	319	0.0229084	03202	104134041	56871624	178280.95
3301032005	03301	4	2017	Vallenar	217644.6	2017	51917	11299454698	49	0.0009438	03301	10664585	24988640	509972.24
3301032017	03301	12	2017	Vallenar	217644.6	2017	51917	11299454698	386	0.0074349	03301	84010816	49192251	127441.07
3301032901	03301	1	2017	Vallenar	217644.6	2017	51917	11299454698	9	0.0001734	03301	1958801	6033901	670433.44
3301042005	03301	19	2017	Vallenar	217644.6	2017	51917	11299454698	184	0.0035441	03301	40046606	61724609	335459.83
3301042060	03301	19	2017	Vallenar	217644.6	2017	51917	11299454698	172	0.0033130	03301	37434871	61724609	358864.01
3301042901	03301	3	2017	Vallenar	217644.6	2017	51917	11299454698	23	0.0004430	03301	5005826	19992121	869222.64
3301052002	03301	39	2017	Vallenar	217644.6	2017	51917	11299454698	574	0.0110561	03301	124928000	84187452	146668.04
3301052006	03301	7	2017	Vallenar	217644.6	2017	51917	11299454698	55	0.0010594	03301	11970453	36303026	660055.01
3301052008	03301	2	2017	Vallenar	217644.6	2017	51917	11299454698	17	0.0003274	03301	3699958	13895240	817367.05
3301052010	03301	15	2017	Vallenar	217644.6	2017	51917	11299454698	343	0.0066067	03301	74652098	55100396	160642.55
3301052014	03301	5	2017	Vallenar	217644.6	2017	51917	11299454698	37	0.0007127	03301	8052850	29247644	790476.88
3301052028	03301	53	2017	Vallenar	217644.6	2017	51917	11299454698	523	0.0100738	03301	113828126	94826996	181313.57
3301052036	03301	101	2017	Vallenar	217644.6	2017	51917	11299454698	537	0.0103434	03301	116875150	119257998	222081.93
3301052038	03301	9	2017	Vallenar	217644.6	2017	51917	11299454698	87	0.0016758	03301	18935080	42069621	483558.87
3301052063	03301	13	2017	Vallenar	217644.6	2017	51917	11299454698	90	0.0017335	03301	19588014	51273005	569700.06
3301082012	03301	35	2017	Vallenar	217644.6	2017	51917	11299454698	796	0.0153322	03301	173245102	80584883	101237.29
3301082901	03301	7	2017	Vallenar	217644.6	2017	51917	11299454698	177	0.0034093	03301	38523094	36303026	205101.84
3301092043	03301	2	2017	Vallenar	217644.6	2017	51917	11299454698	60	0.0011557	03301	13058676	13895240	231587.33
3301092901	03301	3	2017	Vallenar	217644.6	2017	51917	11299454698	141	0.0027159	03301	30687889	19992121	141788.09
3301122004	03301	3	2017	Vallenar	217644.6	2017	51917	11299454698	44	0.0008475	03301	9576362	19992121	454366.38
3301122009	03301	7	2017	Vallenar	217644.6	2017	51917	11299454698	198	0.0038138	03301	43093631	36303026	183348.61
3301122025	03301	19	2017	Vallenar	217644.6	2017	51917	11299454698	264	0.0050850	03301	57458174	61724609	233805.34
3301122030	03301	4	2017	Vallenar	217644.6	2017	51917	11299454698	159	0.0030626	03301	34605491	24988640	157161.26
3301122032	03301	6	2017	Vallenar	217644.6	2017	51917	11299454698	43	0.0008282	03301	9358718	32976155	766887.34
3301122033	03301	8	2017	Vallenar	217644.6	2017	51917	11299454698	108	0.0020802	03301	23505617	39314101	364019.46
3301122901	03301	2	2017	Vallenar	217644.6	2017	51917	11299454698	46	0.0008860	03301	10011652	13895240	302070.43
3301132026	03301	2	2017	Vallenar	217644.6	2017	51917	11299454698	146	0.0028122	03301	31776112	13895240	95172.88
3301132901	03301	3	2017	Vallenar	217644.6	2017	51917	11299454698	33	0.0006356	03301	7182272	19992121	605821.84
3301152010	03301	4	2017	Vallenar	217644.6	2017	51917	11299454698	218	0.0041990	03301	47446523	24988640	114626.79
3301152024	03301	1	2017	Vallenar	217644.6	2017	51917	11299454698	43	0.0008282	03301	9358718	6033901	140323.28
3301152901	03301	4	2017	Vallenar	217644.6	2017	51917	11299454698	124	0.0023884	03301	26987930	24988640	201521.29
3302012002	03302	58	2017	Alto del Carmen	196109.9	2017	5299	1039186477	645	0.1217211	03302	126490900	98074410	152053.35
3302012005	03302	2	2017	Alto del Carmen	196109.9	2017	5299	1039186477	48	0.0090583	03302	9413276	13895240	289484.16
3302012029	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	117	0.0220796	03302	22944861	6033901	51571.80
3302022005	03302	20	2017	Alto del Carmen	196109.9	2017	5299	1039186477	540	0.1019060	03302	105899358	63211611	117058.54
3302032005	03302	34	2017	Alto del Carmen	196109.9	2017	5299	1039186477	821	0.1549349	03302	161006246	79633228	96995.41
3302032018	03302	25	2017	Alto del Carmen	196109.9	2017	5299	1039186477	512	0.0966220	03302	100408280	69886561	136497.19
3302042005	03302	17	2017	Alto del Carmen	196109.9	2017	5299	1039186477	560	0.1056803	03302	109821556	58560959	104573.14
3302052005	03302	10	2017	Alto del Carmen	196109.9	2017	5299	1039186477	419	0.0790715	03302	82170057	44613597	106476.37
3302062032	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	54	0.0101906	03302	10589936	6033901	111738.91
3302062901	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	13	0.0024533	03302	2549429	6033901	464146.23
3302072009	03302	2	2017	Alto del Carmen	196109.9	2017	5299	1039186477	30	0.0056614	03302	5883298	13895240	463174.66
3302072031	03302	3	2017	Alto del Carmen	196109.9	2017	5299	1039186477	96	0.0181166	03302	18826553	19992121	208251.26
3302072034	03302	25	2017	Alto del Carmen	196109.9	2017	5299	1039186477	229	0.0432157	03302	44909172	69886561	305181.49
3302072901	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	52	0.0098132	03302	10197716	6033901	116036.56
3302082901	03302	3	2017	Alto del Carmen	196109.9	2017	5299	1039186477	74	0.0139649	03302	14512134	19992121	270163.79
3302092004	03302	2	2017	Alto del Carmen	196109.9	2017	5299	1039186477	15	0.0028307	03302	2941649	13895240	926349.32
3302092013	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	58	0.0109455	03302	11374375	6033901	104032.78
3302092033	03302	3	2017	Alto del Carmen	196109.9	2017	5299	1039186477	149	0.0281185	03302	29220378	19992121	134175.31
3302092901	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	10	0.0018871	03302	1961099	6033901	603390.10
3302102027	03302	68	2017	Alto del Carmen	196109.9	2017	5299	1039186477	299	0.0564257	03302	58636867	103937559	347617.25
3302102030	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	115	0.0217022	03302	22552641	6033901	52468.70
3302112015	03302	1	2017	Alto del Carmen	196109.9	2017	5299	1039186477	53	0.0100019	03302	10393826	6033901	113847.19

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "P15/region_03_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