Expansión de la CASEN sobre el CENSO (Región 04)

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 04.

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

unique(tabla_con_clave$AREA)

## [1] 2 1

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 04 y con la zona = 2:

tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$REGION == 4) 
tabla_con_clave <- filter(tabla_con_clave, tabla_con_clave$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	4101022001	12	4101	200	2017
2	4101052007	12	4101	31	2017
3	4101062001	12	4101	97	2017
4	4101062004	12	4101	215	2017
5	4101062006	12	4101	54	2017
6	4101062024	12	4101	54	2017
7	4101062030	12	4101	7	2017
8	4101062049	12	4101	37	2017
9	4101062901	12	4101	22	2017
10	4101072002	12	4101	106	2017
11	4101072027	12	4101	76	2017
12	4101072029	12	4101	1	2017
13	4101072051	12	4101	47	2017
14	4101072054	12	4101	6	2017
15	4101082011	12	4101	8	2017
16	4101082018	12	4101	46	2017
17	4101082021	12	4101	62	2017
18	4101082022	12	4101	1	2017
19	4101082023	12	4101	74	2017
20	4101082029	12	4101	10	2017
21	4101082043	12	4101	32	2017
22	4101082051	12	4101	8	2017
23	4101082052	12	4101	12	2017
24	4101082901	12	4101	6	2017
25	4101092003	12	4101	150	2017
26	4101092009	12	4101	2	2017
27	4101092034	12	4101	83	2017
28	4101092038	12	4101	4	2017
29	4101092039	12	4101	5	2017
30	4101102003	12	4101	17	2017
31	4101102013	12	4101	93	2017
32	4101102015	12	4101	46	2017
33	4101102020	12	4101	276	2017
34	4101102032	12	4101	1	2017
35	4101102035	12	4101	2	2017
36	4101112010	12	4101	1	2017
37	4101122010	12	4101	2	2017
38	4101122901	12	4101	1	2017
39	4101132025	12	4101	35	2017
40	4101132032	12	4101	17	2017
41	4101132046	12	4101	1	2017
42	4101132901	12	4101	4	2017
43	4101142012	12	4101	5	2017
44	4101142014	12	4101	62	2017
45	4101142019	12	4101	3	2017
46	4101142036	12	4101	16	2017
47	4101142040	12	4101	9	2017
48	4101142056	12	4101	140	2017
49	4101142057	12	4101	9	2017
50	4101142901	12	4101	69	2017
51	4101172030	12	4101	1	2017
555	4102062009	12	4102	28	2017
556	4102062014	12	4102	3	2017
557	4102062024	12	4102	343	2017
558	4102062901	12	4102	29	2017
559	4102072002	12	4102	74	2017
560	4102072011	12	4102	29	2017
561	4102072021	12	4102	9	2017
562	4102072023	12	4102	14	2017
563	4102072024	12	4102	96	2017
564	4102082004	12	4102	46	2017
565	4102082007	12	4102	6	2017
566	4102082016	12	4102	1	2017
567	4102082020	12	4102	4	2017
568	4102082032	12	4102	28	2017
569	4102082901	12	4102	11	2017
570	4102092026	12	4102	7	2017
571	4102112017	12	4102	52	2017
572	4102112025	12	4102	20	2017
573	4102112029	12	4102	69	2017
574	4102112901	12	4102	3	2017
575	4102122018	12	4102	5	2017
576	4102132001	12	4102	1	2017
577	4102142012	12	4102	6	2017
578	4102152012	12	4102	3	2017
579	4102152030	12	4102	11	2017
580	4102162013	12	4102	32	2017
581	4102162901	12	4102	2	2017
582	4102182004	12	4102	1	2017
1086	4103012011	12	4103	1	2017
1087	4103012014	12	4103	5	2017
1088	4103012901	12	4103	5	2017
1089	4103032005	12	4103	26	2017
1090	4103032013	12	4103	3	2017
1091	4103032901	12	4103	1	2017
1595	4104012013	12	4104	2	2017
1596	4104022002	12	4104	22	2017
1597	4104022006	12	4104	2	2017
1598	4104022901	12	4104	1	2017
1599	4104032014	12	4104	1	2017
1600	4104042003	12	4104	6	2017
1601	4104042028	12	4104	1	2017
1602	4104042901	12	4104	2	2017
1603	4104052004	12	4104	28	2017
1604	4104052017	12	4104	13	2017
1605	4104052023	12	4104	10	2017
1606	4104062004	12	4104	1	2017
1607	4104062901	12	4104	2	2017
1608	4104072015	12	4104	7	2017
1609	4104072018	12	4104	2	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	4101022001	200	2017	04101
2	4101052007	31	2017	04101
3	4101062001	97	2017	04101
4	4101062004	215	2017	04101
5	4101062006	54	2017	04101
6	4101062024	54	2017	04101
7	4101062030	7	2017	04101
8	4101062049	37	2017	04101
9	4101062901	22	2017	04101
10	4101072002	106	2017	04101
11	4101072027	76	2017	04101
12	4101072029	1	2017	04101
13	4101072051	47	2017	04101
14	4101072054	6	2017	04101
15	4101082011	8	2017	04101
16	4101082018	46	2017	04101
17	4101082021	62	2017	04101
18	4101082022	1	2017	04101
19	4101082023	74	2017	04101
20	4101082029	10	2017	04101
21	4101082043	32	2017	04101
22	4101082051	8	2017	04101
23	4101082052	12	2017	04101
24	4101082901	6	2017	04101
25	4101092003	150	2017	04101
26	4101092009	2	2017	04101
27	4101092034	83	2017	04101
28	4101092038	4	2017	04101
29	4101092039	5	2017	04101
30	4101102003	17	2017	04101
31	4101102013	93	2017	04101
32	4101102015	46	2017	04101
33	4101102020	276	2017	04101
34	4101102032	1	2017	04101
35	4101102035	2	2017	04101
36	4101112010	1	2017	04101
37	4101122010	2	2017	04101
38	4101122901	1	2017	04101
39	4101132025	35	2017	04101
40	4101132032	17	2017	04101
41	4101132046	1	2017	04101
42	4101132901	4	2017	04101
43	4101142012	5	2017	04101
44	4101142014	62	2017	04101
45	4101142019	3	2017	04101
46	4101142036	16	2017	04101
47	4101142040	9	2017	04101
48	4101142056	140	2017	04101
49	4101142057	9	2017	04101
50	4101142901	69	2017	04101
51	4101172030	1	2017	04101
555	4102062009	28	2017	04102
556	4102062014	3	2017	04102
557	4102062024	343	2017	04102
558	4102062901	29	2017	04102
559	4102072002	74	2017	04102
560	4102072011	29	2017	04102
561	4102072021	9	2017	04102
562	4102072023	14	2017	04102
563	4102072024	96	2017	04102
564	4102082004	46	2017	04102
565	4102082007	6	2017	04102
566	4102082016	1	2017	04102
567	4102082020	4	2017	04102
568	4102082032	28	2017	04102
569	4102082901	11	2017	04102
570	4102092026	7	2017	04102
571	4102112017	52	2017	04102
572	4102112025	20	2017	04102
573	4102112029	69	2017	04102
574	4102112901	3	2017	04102
575	4102122018	5	2017	04102
576	4102132001	1	2017	04102
577	4102142012	6	2017	04102
578	4102152012	3	2017	04102
579	4102152030	11	2017	04102
580	4102162013	32	2017	04102
581	4102162901	2	2017	04102
582	4102182004	1	2017	04102
1086	4103012011	1	2017	04103
1087	4103012014	5	2017	04103
1088	4103012901	5	2017	04103
1089	4103032005	26	2017	04103
1090	4103032013	3	2017	04103
1091	4103032901	1	2017	04103
1595	4104012013	2	2017	04104
1596	4104022002	22	2017	04104
1597	4104022006	2	2017	04104
1598	4104022901	1	2017	04104
1599	4104032014	1	2017	04104
1600	4104042003	6	2017	04104
1601	4104042028	1	2017	04104
1602	4104042901	2	2017	04104
1603	4104052004	28	2017	04104
1604	4104052017	13	2017	04104
1605	4104052023	10	2017	04104
1606	4104062004	1	2017	04104
1607	4104062901	2	2017	04104
1608	4104072015	7	2017	04104
1609	4104072018	2	2017	04104

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
04101	4101022001	200	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101052007	31	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101062001	97	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101062004	215	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101062006	54	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101062024	54	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101062030	7	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101062049	37	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101062901	22	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101072002	106	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101072027	76	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101072029	1	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101072051	47	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101072054	6	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101082011	8	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101082018	46	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101082021	62	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101082022	1	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101082023	74	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101082029	10	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101082043	32	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101082051	8	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101082052	12	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101082901	6	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101092003	150	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101092009	2	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101092034	83	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101092038	4	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101092039	5	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101102003	17	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101102013	93	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101102015	46	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101102020	276	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101102032	1	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101102035	2	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101112010	1	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101122010	2	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101122901	1	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101132025	35	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101132032	17	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101132046	1	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101132901	4	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101142012	5	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101142014	62	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101142019	3	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101142036	16	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101142040	9	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101142056	140	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101142057	9	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101142901	69	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101172030	1	2017	La Serena	200287.4	2017	221054	44274327972
04102	4102062009	28	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102062014	3	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102062024	343	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102062901	29	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102072002	74	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102072011	29	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102072021	9	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102072023	14	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102072024	96	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102082004	46	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102082007	6	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102082016	1	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102082020	4	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102082032	28	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102082901	11	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102092026	7	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102112017	52	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102112025	20	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102112029	69	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102112901	3	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102122018	5	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102132001	1	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102142012	6	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102152012	3	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102152030	11	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102162013	32	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102162901	2	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102182004	1	2017	Coquimbo	206027.8	2017	227730	46918711304
04103	4103012011	1	2017	Andacollo	217096.4	2017	11044	2397612293
04103	4103012014	5	2017	Andacollo	217096.4	2017	11044	2397612293
04103	4103012901	5	2017	Andacollo	217096.4	2017	11044	2397612293
04103	4103032005	26	2017	Andacollo	217096.4	2017	11044	2397612293
04103	4103032013	3	2017	Andacollo	217096.4	2017	11044	2397612293
04103	4103032901	1	2017	Andacollo	217096.4	2017	11044	2397612293
04104	4104012013	2	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104022002	22	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104022006	2	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104022901	1	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104032014	1	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104042003	6	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104042028	1	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104042901	2	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104052004	28	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104052017	13	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104052023	10	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104062004	1	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104062901	2	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104072015	7	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104072018	2	2017	La Higuera	231674.2	2017	4241	982530309

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
04101	4101022001	200	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101052007	31	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101062001	97	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101062004	215	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101062006	54	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101062024	54	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101062030	7	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101062049	37	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101062901	22	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101072002	106	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101072027	76	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101072029	1	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101072051	47	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101072054	6	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101082011	8	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101082018	46	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101082021	62	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101082022	1	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101082023	74	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101082029	10	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101082043	32	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101082051	8	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101082052	12	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101082901	6	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101092003	150	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101092009	2	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101092034	83	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101092038	4	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101092039	5	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101102003	17	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101102013	93	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101102015	46	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101102020	276	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101102032	1	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101102035	2	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101112010	1	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101122010	2	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101122901	1	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101132025	35	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101132032	17	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101132046	1	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101132901	4	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101142012	5	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101142014	62	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101142019	3	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101142036	16	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101142040	9	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101142056	140	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101142057	9	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101142901	69	2017	La Serena	200287.4	2017	221054	44274327972
04101	4101172030	1	2017	La Serena	200287.4	2017	221054	44274327972
04102	4102062009	28	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102062014	3	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102062024	343	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102062901	29	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102072002	74	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102072011	29	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102072021	9	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102072023	14	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102072024	96	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102082004	46	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102082007	6	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102082016	1	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102082020	4	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102082032	28	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102082901	11	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102092026	7	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102112017	52	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102112025	20	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102112029	69	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102112901	3	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102122018	5	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102132001	1	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102142012	6	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102152012	3	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102152030	11	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102162013	32	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102162901	2	2017	Coquimbo	206027.8	2017	227730	46918711304
04102	4102182004	1	2017	Coquimbo	206027.8	2017	227730	46918711304
04103	4103012011	1	2017	Andacollo	217096.4	2017	11044	2397612293
04103	4103012014	5	2017	Andacollo	217096.4	2017	11044	2397612293
04103	4103012901	5	2017	Andacollo	217096.4	2017	11044	2397612293
04103	4103032005	26	2017	Andacollo	217096.4	2017	11044	2397612293
04103	4103032013	3	2017	Andacollo	217096.4	2017	11044	2397612293
04103	4103032901	1	2017	Andacollo	217096.4	2017	11044	2397612293
04104	4104012013	2	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104022002	22	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104022006	2	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104022901	1	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104032014	1	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104042003	6	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104042028	1	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104042901	2	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104052004	28	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104052017	13	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104052023	10	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104062004	1	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104062901	2	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104072015	7	2017	La Higuera	231674.2	2017	4241	982530309
04104	4104072018	2	2017	La Higuera	231674.2	2017	4241	982530309

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
4101022001	04101	200	2017	La Serena	200287.4	2017	221054	44274327972	1157	0.0052340	04101
4101052007	04101	31	2017	La Serena	200287.4	2017	221054	44274327972	125	0.0005655	04101
4101062001	04101	97	2017	La Serena	200287.4	2017	221054	44274327972	345	0.0015607	04101
4101062004	04101	215	2017	La Serena	200287.4	2017	221054	44274327972	740	0.0033476	04101
4101062006	04101	54	2017	La Serena	200287.4	2017	221054	44274327972	224	0.0010133	04101
4101062024	04101	54	2017	La Serena	200287.4	2017	221054	44274327972	2392	0.0108209	04101
4101062030	04101	7	2017	La Serena	200287.4	2017	221054	44274327972	41	0.0001855	04101
4101062049	04101	37	2017	La Serena	200287.4	2017	221054	44274327972	115	0.0005202	04101
4101062901	04101	22	2017	La Serena	200287.4	2017	221054	44274327972	284	0.0012848	04101
4101072002	04101	106	2017	La Serena	200287.4	2017	221054	44274327972	394	0.0017824	04101
4101072027	04101	76	2017	La Serena	200287.4	2017	221054	44274327972	461	0.0020855	04101
4101072029	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	119	0.0005383	04101
4101072051	04101	47	2017	La Serena	200287.4	2017	221054	44274327972	179	0.0008098	04101
4101072054	04101	6	2017	La Serena	200287.4	2017	221054	44274327972	114	0.0005157	04101
4101082011	04101	8	2017	La Serena	200287.4	2017	221054	44274327972	304	0.0013752	04101
4101082018	04101	46	2017	La Serena	200287.4	2017	221054	44274327972	348	0.0015743	04101
4101082021	04101	62	2017	La Serena	200287.4	2017	221054	44274327972	748	0.0033838	04101
4101082022	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	13	0.0000588	04101
4101082023	04101	74	2017	La Serena	200287.4	2017	221054	44274327972	733	0.0033159	04101
4101082029	04101	10	2017	La Serena	200287.4	2017	221054	44274327972	140	0.0006333	04101
4101082043	04101	32	2017	La Serena	200287.4	2017	221054	44274327972	692	0.0031305	04101
4101082051	04101	8	2017	La Serena	200287.4	2017	221054	44274327972	36	0.0001629	04101
4101082052	04101	12	2017	La Serena	200287.4	2017	221054	44274327972	52	0.0002352	04101
4101082901	04101	6	2017	La Serena	200287.4	2017	221054	44274327972	37	0.0001674	04101
4101092003	04101	150	2017	La Serena	200287.4	2017	221054	44274327972	740	0.0033476	04101
4101092009	04101	2	2017	La Serena	200287.4	2017	221054	44274327972	144	0.0006514	04101
4101092034	04101	83	2017	La Serena	200287.4	2017	221054	44274327972	742	0.0033566	04101
4101092038	04101	4	2017	La Serena	200287.4	2017	221054	44274327972	329	0.0014883	04101
4101092039	04101	5	2017	La Serena	200287.4	2017	221054	44274327972	63	0.0002850	04101
4101102003	04101	17	2017	La Serena	200287.4	2017	221054	44274327972	70	0.0003167	04101
4101102013	04101	93	2017	La Serena	200287.4	2017	221054	44274327972	1201	0.0054331	04101
4101102015	04101	46	2017	La Serena	200287.4	2017	221054	44274327972	364	0.0016467	04101
4101102020	04101	276	2017	La Serena	200287.4	2017	221054	44274327972	2212	0.0100066	04101
4101102032	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	48	0.0002171	04101
4101102035	04101	2	2017	La Serena	200287.4	2017	221054	44274327972	46	0.0002081	04101
4101112010	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	66	0.0002986	04101
4101122010	04101	2	2017	La Serena	200287.4	2017	221054	44274327972	44	0.0001990	04101
4101122901	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	63	0.0002850	04101
4101132025	04101	35	2017	La Serena	200287.4	2017	221054	44274327972	987	0.0044650	04101
4101132032	04101	17	2017	La Serena	200287.4	2017	221054	44274327972	824	0.0037276	04101
4101132046	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	23	0.0001040	04101
4101132901	04101	4	2017	La Serena	200287.4	2017	221054	44274327972	36	0.0001629	04101
4101142012	04101	5	2017	La Serena	200287.4	2017	221054	44274327972	175	0.0007917	04101
4101142014	04101	62	2017	La Serena	200287.4	2017	221054	44274327972	519	0.0023478	04101
4101142019	04101	3	2017	La Serena	200287.4	2017	221054	44274327972	194	0.0008776	04101
4101142036	04101	16	2017	La Serena	200287.4	2017	221054	44274327972	225	0.0010179	04101
4101142040	04101	9	2017	La Serena	200287.4	2017	221054	44274327972	92	0.0004162	04101
4101142056	04101	140	2017	La Serena	200287.4	2017	221054	44274327972	383	0.0017326	04101
4101142057	04101	9	2017	La Serena	200287.4	2017	221054	44274327972	45	0.0002036	04101
4101142901	04101	69	2017	La Serena	200287.4	2017	221054	44274327972	324	0.0014657	04101
4101172030	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	113	0.0005112	04101
4102062009	04102	28	2017	Coquimbo	206027.8	2017	227730	46918711304	395	0.0017345	04102
4102062014	04102	3	2017	Coquimbo	206027.8	2017	227730	46918711304	128	0.0005621	04102
4102062024	04102	343	2017	Coquimbo	206027.8	2017	227730	46918711304	4341	0.0190620	04102
4102062901	04102	29	2017	Coquimbo	206027.8	2017	227730	46918711304	111	0.0004874	04102
4102072002	04102	74	2017	Coquimbo	206027.8	2017	227730	46918711304	816	0.0035832	04102
4102072011	04102	29	2017	Coquimbo	206027.8	2017	227730	46918711304	156	0.0006850	04102
4102072021	04102	9	2017	Coquimbo	206027.8	2017	227730	46918711304	106	0.0004655	04102
4102072023	04102	14	2017	Coquimbo	206027.8	2017	227730	46918711304	565	0.0024810	04102
4102072024	04102	96	2017	Coquimbo	206027.8	2017	227730	46918711304	1893	0.0083125	04102
4102082004	04102	46	2017	Coquimbo	206027.8	2017	227730	46918711304	296	0.0012998	04102
4102082007	04102	6	2017	Coquimbo	206027.8	2017	227730	46918711304	52	0.0002283	04102
4102082016	04102	1	2017	Coquimbo	206027.8	2017	227730	46918711304	14	0.0000615	04102
4102082020	04102	4	2017	Coquimbo	206027.8	2017	227730	46918711304	8	0.0000351	04102
4102082032	04102	28	2017	Coquimbo	206027.8	2017	227730	46918711304	307	0.0013481	04102
4102082901	04102	11	2017	Coquimbo	206027.8	2017	227730	46918711304	77	0.0003381	04102
4102092026	04102	7	2017	Coquimbo	206027.8	2017	227730	46918711304	40	0.0001756	04102
4102112017	04102	52	2017	Coquimbo	206027.8	2017	227730	46918711304	760	0.0033373	04102
4102112025	04102	20	2017	Coquimbo	206027.8	2017	227730	46918711304	233	0.0010231	04102
4102112029	04102	69	2017	Coquimbo	206027.8	2017	227730	46918711304	1312	0.0057612	04102
4102112901	04102	3	2017	Coquimbo	206027.8	2017	227730	46918711304	45	0.0001976	04102
4102122018	04102	5	2017	Coquimbo	206027.8	2017	227730	46918711304	460	0.0020199	04102
4102132001	04102	1	2017	Coquimbo	206027.8	2017	227730	46918711304	61	0.0002679	04102
4102142012	04102	6	2017	Coquimbo	206027.8	2017	227730	46918711304	288	0.0012647	04102
4102152012	04102	3	2017	Coquimbo	206027.8	2017	227730	46918711304	50	0.0002196	04102
4102152030	04102	11	2017	Coquimbo	206027.8	2017	227730	46918711304	122	0.0005357	04102
4102162013	04102	32	2017	Coquimbo	206027.8	2017	227730	46918711304	252	0.0011066	04102
4102162901	04102	2	2017	Coquimbo	206027.8	2017	227730	46918711304	19	0.0000834	04102
4102182004	04102	1	2017	Coquimbo	206027.8	2017	227730	46918711304	19	0.0000834	04102
4103012011	04103	1	2017	Andacollo	217096.4	2017	11044	2397612293	14	0.0012677	04103
4103012014	04103	5	2017	Andacollo	217096.4	2017	11044	2397612293	55	0.0049801	04103
4103012901	04103	5	2017	Andacollo	217096.4	2017	11044	2397612293	83	0.0075154	04103
4103032005	04103	26	2017	Andacollo	217096.4	2017	11044	2397612293	537	0.0486237	04103
4103032013	04103	3	2017	Andacollo	217096.4	2017	11044	2397612293	72	0.0065194	04103
4103032901	04103	1	2017	Andacollo	217096.4	2017	11044	2397612293	31	0.0028070	04103
4104012013	04104	2	2017	La Higuera	231674.2	2017	4241	982530309	53	0.0124971	04104
4104022002	04104	22	2017	La Higuera	231674.2	2017	4241	982530309	839	0.1978307	04104
4104022006	04104	2	2017	La Higuera	231674.2	2017	4241	982530309	43	0.0101391	04104
4104022901	04104	1	2017	La Higuera	231674.2	2017	4241	982530309	19	0.0044801	04104
4104032014	04104	1	2017	La Higuera	231674.2	2017	4241	982530309	66	0.0155624	04104
4104042003	04104	6	2017	La Higuera	231674.2	2017	4241	982530309	267	0.0629568	04104
4104042028	04104	1	2017	La Higuera	231674.2	2017	4241	982530309	52	0.0122613	04104
4104042901	04104	2	2017	La Higuera	231674.2	2017	4241	982530309	18	0.0042443	04104
4104052004	04104	28	2017	La Higuera	231674.2	2017	4241	982530309	144	0.0339543	04104
4104052017	04104	13	2017	La Higuera	231674.2	2017	4241	982530309	231	0.0544683	04104
4104052023	04104	10	2017	La Higuera	231674.2	2017	4241	982530309	311	0.0733318	04104
4104062004	04104	1	2017	La Higuera	231674.2	2017	4241	982530309	3	0.0007074	04104
4104062901	04104	2	2017	La Higuera	231674.2	2017	4241	982530309	52	0.0122613	04104
4104072015	04104	7	2017	La Higuera	231674.2	2017	4241	982530309	21	0.0049517	04104
4104072018	04104	2	2017	La Higuera	231674.2	2017	4241	982530309	72	0.0169771	04104

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
4101022001	04101	200	2017	La Serena	200287.4	2017	221054	44274327972	1157	0.0052340	04101	231732506.4
4101052007	04101	31	2017	La Serena	200287.4	2017	221054	44274327972	125	0.0005655	04101	25035923.3
4101062001	04101	97	2017	La Serena	200287.4	2017	221054	44274327972	345	0.0015607	04101	69099148.4
4101062004	04101	215	2017	La Serena	200287.4	2017	221054	44274327972	740	0.0033476	04101	148212666.1
4101062006	04101	54	2017	La Serena	200287.4	2017	221054	44274327972	224	0.0010133	04101	44864374.6
4101062024	04101	54	2017	La Serena	200287.4	2017	221054	44274327972	2392	0.0108209	04101	479087428.9
4101062030	04101	7	2017	La Serena	200287.4	2017	221054	44274327972	41	0.0001855	04101	8211782.9
4101062049	04101	37	2017	La Serena	200287.4	2017	221054	44274327972	115	0.0005202	04101	23033049.5
4101062901	04101	22	2017	La Serena	200287.4	2017	221054	44274327972	284	0.0012848	04101	56881617.8
4101072002	04101	106	2017	La Serena	200287.4	2017	221054	44274327972	394	0.0017824	04101	78913230.3
4101072027	04101	76	2017	La Serena	200287.4	2017	221054	44274327972	461	0.0020855	04101	92332485.3
4101072029	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	119	0.0005383	04101	23834199.0
4101072051	04101	47	2017	La Serena	200287.4	2017	221054	44274327972	179	0.0008098	04101	35851442.2
4101072054	04101	6	2017	La Serena	200287.4	2017	221054	44274327972	114	0.0005157	04101	22832762.1
4101082011	04101	8	2017	La Serena	200287.4	2017	221054	44274327972	304	0.0013752	04101	60887365.5
4101082018	04101	46	2017	La Serena	200287.4	2017	221054	44274327972	348	0.0015743	04101	69700010.6
4101082021	04101	62	2017	La Serena	200287.4	2017	221054	44274327972	748	0.0033838	04101	149814965.2
4101082022	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	13	0.0000588	04101	2603736.0
4101082023	04101	74	2017	La Serena	200287.4	2017	221054	44274327972	733	0.0033159	04101	146810654.4
4101082029	04101	10	2017	La Serena	200287.4	2017	221054	44274327972	140	0.0006333	04101	28040234.1
4101082043	04101	32	2017	La Serena	200287.4	2017	221054	44274327972	692	0.0031305	04101	138598871.6
4101082051	04101	8	2017	La Serena	200287.4	2017	221054	44274327972	36	0.0001629	04101	7210345.9
4101082052	04101	12	2017	La Serena	200287.4	2017	221054	44274327972	52	0.0002352	04101	10414944.1
4101082901	04101	6	2017	La Serena	200287.4	2017	221054	44274327972	37	0.0001674	04101	7410633.3
4101092003	04101	150	2017	La Serena	200287.4	2017	221054	44274327972	740	0.0033476	04101	148212666.1
4101092009	04101	2	2017	La Serena	200287.4	2017	221054	44274327972	144	0.0006514	04101	28841383.7
4101092034	04101	83	2017	La Serena	200287.4	2017	221054	44274327972	742	0.0033566	04101	148613240.9
4101092038	04101	4	2017	La Serena	200287.4	2017	221054	44274327972	329	0.0014883	04101	65894550.2
4101092039	04101	5	2017	La Serena	200287.4	2017	221054	44274327972	63	0.0002850	04101	12618105.4
4101102003	04101	17	2017	La Serena	200287.4	2017	221054	44274327972	70	0.0003167	04101	14020117.1
4101102013	04101	93	2017	La Serena	200287.4	2017	221054	44274327972	1201	0.0054331	04101	240545151.4
4101102015	04101	46	2017	La Serena	200287.4	2017	221054	44274327972	364	0.0016467	04101	72904608.7
4101102020	04101	276	2017	La Serena	200287.4	2017	221054	44274327972	2212	0.0100066	04101	443035699.3
4101102032	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	48	0.0002171	04101	9613794.6
4101102035	04101	2	2017	La Serena	200287.4	2017	221054	44274327972	46	0.0002081	04101	9213219.8
4101112010	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	66	0.0002986	04101	13218967.5
4101122010	04101	2	2017	La Serena	200287.4	2017	221054	44274327972	44	0.0001990	04101	8812645.0
4101122901	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	63	0.0002850	04101	12618105.4
4101132025	04101	35	2017	La Serena	200287.4	2017	221054	44274327972	987	0.0044650	04101	197683650.6
4101132032	04101	17	2017	La Serena	200287.4	2017	221054	44274327972	824	0.0037276	04101	165036806.6
4101132046	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	23	0.0001040	04101	4606609.9
4101132901	04101	4	2017	La Serena	200287.4	2017	221054	44274327972	36	0.0001629	04101	7210345.9
4101142012	04101	5	2017	La Serena	200287.4	2017	221054	44274327972	175	0.0007917	04101	35050292.7
4101142014	04101	62	2017	La Serena	200287.4	2017	221054	44274327972	519	0.0023478	04101	103949153.7
4101142019	04101	3	2017	La Serena	200287.4	2017	221054	44274327972	194	0.0008776	04101	38855753.0
4101142036	04101	16	2017	La Serena	200287.4	2017	221054	44274327972	225	0.0010179	04101	45064662.0
4101142040	04101	9	2017	La Serena	200287.4	2017	221054	44274327972	92	0.0004162	04101	18426439.6
4101142056	04101	140	2017	La Serena	200287.4	2017	221054	44274327972	383	0.0017326	04101	76710069.1
4101142057	04101	9	2017	La Serena	200287.4	2017	221054	44274327972	45	0.0002036	04101	9012932.4
4101142901	04101	69	2017	La Serena	200287.4	2017	221054	44274327972	324	0.0014657	04101	64893113.3
4101172030	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	113	0.0005112	04101	22632474.7
4102062009	04102	28	2017	Coquimbo	206027.8	2017	227730	46918711304	395	0.0017345	04102	81380981.7
4102062014	04102	3	2017	Coquimbo	206027.8	2017	227730	46918711304	128	0.0005621	04102	26371558.6
4102062024	04102	343	2017	Coquimbo	206027.8	2017	227730	46918711304	4341	0.0190620	04102	894366687.6
4102062901	04102	29	2017	Coquimbo	206027.8	2017	227730	46918711304	111	0.0004874	04102	22869086.0
4102072002	04102	74	2017	Coquimbo	206027.8	2017	227730	46918711304	816	0.0035832	04102	168118686.3
4102072011	04102	29	2017	Coquimbo	206027.8	2017	227730	46918711304	156	0.0006850	04102	32140337.1
4102072021	04102	9	2017	Coquimbo	206027.8	2017	227730	46918711304	106	0.0004655	04102	21838947.0
4102072023	04102	14	2017	Coquimbo	206027.8	2017	227730	46918711304	565	0.0024810	04102	116405708.0
4102072024	04102	96	2017	Coquimbo	206027.8	2017	227730	46918711304	1893	0.0083125	04102	390010628.8
4102082004	04102	46	2017	Coquimbo	206027.8	2017	227730	46918711304	296	0.0012998	04102	60984229.3
4102082007	04102	6	2017	Coquimbo	206027.8	2017	227730	46918711304	52	0.0002283	04102	10713445.7
4102082016	04102	1	2017	Coquimbo	206027.8	2017	227730	46918711304	14	0.0000615	04102	2884389.2
4102082020	04102	4	2017	Coquimbo	206027.8	2017	227730	46918711304	8	0.0000351	04102	1648222.4
4102082032	04102	28	2017	Coquimbo	206027.8	2017	227730	46918711304	307	0.0013481	04102	63250535.2
4102082901	04102	11	2017	Coquimbo	206027.8	2017	227730	46918711304	77	0.0003381	04102	15864140.7
4102092026	04102	7	2017	Coquimbo	206027.8	2017	227730	46918711304	40	0.0001756	04102	8241112.1
4102112017	04102	52	2017	Coquimbo	206027.8	2017	227730	46918711304	760	0.0033373	04102	156581129.4
4102112025	04102	20	2017	Coquimbo	206027.8	2017	227730	46918711304	233	0.0010231	04102	48004477.8
4102112029	04102	69	2017	Coquimbo	206027.8	2017	227730	46918711304	1312	0.0057612	04102	270308476.0
4102112901	04102	3	2017	Coquimbo	206027.8	2017	227730	46918711304	45	0.0001976	04102	9271251.1
4102122018	04102	5	2017	Coquimbo	206027.8	2017	227730	46918711304	460	0.0020199	04102	94772788.8
4102132001	04102	1	2017	Coquimbo	206027.8	2017	227730	46918711304	61	0.0002679	04102	12567695.9
4102142012	04102	6	2017	Coquimbo	206027.8	2017	227730	46918711304	288	0.0012647	04102	59336006.9
4102152012	04102	3	2017	Coquimbo	206027.8	2017	227730	46918711304	50	0.0002196	04102	10301390.1
4102152030	04102	11	2017	Coquimbo	206027.8	2017	227730	46918711304	122	0.0005357	04102	25135391.8
4102162013	04102	32	2017	Coquimbo	206027.8	2017	227730	46918711304	252	0.0011066	04102	51919006.1
4102162901	04102	2	2017	Coquimbo	206027.8	2017	227730	46918711304	19	0.0000834	04102	3914528.2
4102182004	04102	1	2017	Coquimbo	206027.8	2017	227730	46918711304	19	0.0000834	04102	3914528.2
4103012011	04103	1	2017	Andacollo	217096.4	2017	11044	2397612293	14	0.0012677	04103	3039349.2
4103012014	04103	5	2017	Andacollo	217096.4	2017	11044	2397612293	55	0.0049801	04103	11940300.3
4103012901	04103	5	2017	Andacollo	217096.4	2017	11044	2397612293	83	0.0075154	04103	18018998.6
4103032005	04103	26	2017	Andacollo	217096.4	2017	11044	2397612293	537	0.0486237	04103	116580749.8
4103032013	04103	3	2017	Andacollo	217096.4	2017	11044	2397612293	72	0.0065194	04103	15630938.5
4103032901	04103	1	2017	Andacollo	217096.4	2017	11044	2397612293	31	0.0028070	04103	6729987.4
4104012013	04104	2	2017	La Higuera	231674.2	2017	4241	982530309	53	0.0124971	04104	12278732.9
4104022002	04104	22	2017	La Higuera	231674.2	2017	4241	982530309	839	0.1978307	04104	194374659.1
4104022006	04104	2	2017	La Higuera	231674.2	2017	4241	982530309	43	0.0101391	04104	9961990.9
4104022901	04104	1	2017	La Higuera	231674.2	2017	4241	982530309	19	0.0044801	04104	4401809.9
4104032014	04104	1	2017	La Higuera	231674.2	2017	4241	982530309	66	0.0155624	04104	15290497.6
4104042003	04104	6	2017	La Higuera	231674.2	2017	4241	982530309	267	0.0629568	04104	61857013.1
4104042028	04104	1	2017	La Higuera	231674.2	2017	4241	982530309	52	0.0122613	04104	12047058.7
4104042901	04104	2	2017	La Higuera	231674.2	2017	4241	982530309	18	0.0042443	04104	4170135.7
4104052004	04104	28	2017	La Higuera	231674.2	2017	4241	982530309	144	0.0339543	04104	33361085.7
4104052017	04104	13	2017	La Higuera	231674.2	2017	4241	982530309	231	0.0544683	04104	53516741.7
4104052023	04104	10	2017	La Higuera	231674.2	2017	4241	982530309	311	0.0733318	04104	72050678.2
4104062004	04104	1	2017	La Higuera	231674.2	2017	4241	982530309	3	0.0007074	04104	695022.6
4104062901	04104	2	2017	La Higuera	231674.2	2017	4241	982530309	52	0.0122613	04104	12047058.7
4104072015	04104	7	2017	La Higuera	231674.2	2017	4241	982530309	21	0.0049517	04104	4865158.3
4104072018	04104	2	2017	La Higuera	231674.2	2017	4241	982530309	72	0.0169771	04104	16680542.9

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 
## -278221496  -23193237  -13816238   12214913  365245216 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 26124236    2570012   10.16   <2e-16 ***
## Freq.x       1624407      68025   23.88   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 52130000 on 501 degrees of freedom
## Multiple R-squared:  0.5323, Adjusted R-squared:  0.5314 
## F-statistic: 570.2 on 1 and 501 DF,  p-value: < 2.2e-16

7.4 Gráfica de la recta de regresión lineal

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

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

8 Modelos alternativos

8.1 Modelo cuadrático

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

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

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x^2), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -278221496  -23193237  -13816238   12214913  365245216 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 26124236    2570012   10.16   <2e-16 ***
## Freq.x       1624407      68025   23.88   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 52130000 on 501 degrees of freedom
## Multiple R-squared:  0.5323, Adjusted R-squared:  0.5314 
## F-statistic: 570.2 on 1 and 501 DF,  p-value: < 2.2e-16

8.2 Modelo cúbico

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

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

## 
## Call:
## lm(formula = multi_pob ~ (Freq.x^3), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -278221496  -23193237  -13816238   12214913  365245216 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 26124236    2570012   10.16   <2e-16 ***
## Freq.x       1624407      68025   23.88   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 52130000 on 501 degrees of freedom
## Multiple R-squared:  0.5323, Adjusted R-squared:  0.5314 
## F-statistic: 570.2 on 1 and 501 DF,  p-value: < 2.2e-16

8.3 Modelo logarítmico

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

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

## 
## Call:
## lm(formula = multi_pob ~ log(Freq.x), data = h_y_m_comuna_corr_01)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -99736405 -25667017  -3975274  20226060 690916003 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -12318916    4108910  -2.998  0.00285 ** 
## log(Freq.x)  36961213    1849360  19.986  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 56860000 on 501 degrees of freedom
## Multiple R-squared:  0.4436, Adjusted R-squared:  0.4425 
## F-statistic: 399.4 on 1 and 501 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 
## -170843488  -15896802   -1304602   11173235  497424443 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -16735388    3509727  -4.768 2.44e-06 ***
## sqrt(Freq.x)  22336493     874233  25.550  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 50230000 on 501 degrees of freedom
## Multiple R-squared:  0.5658, Adjusted R-squared:  0.5649 
## F-statistic: 652.8 on 1 and 501 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 
## -7673.7 -1572.5  -334.8  1503.5 10761.9 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    2430.2      172.2   14.11   <2e-16 ***
## sqrt(Freq.x)   1183.4       42.9   27.59   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2465 on 501 degrees of freedom
## Multiple R-squared:  0.603,  Adjusted R-squared:  0.6022 
## F-statistic:   761 on 1 and 501 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 
## -4.2270 -0.5840  0.0542  0.6973  1.6676 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  16.00095    0.06390  250.42   <2e-16 ***
## sqrt(Freq.x)  0.33792    0.01592   21.23   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9145 on 501 degrees of freedom
## Multiple R-squared:  0.4736, Adjusted R-squared:  0.4726 
## F-statistic: 450.8 on 1 and 501 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 
## -5613.3 -1587.2   -25.1  1324.4 14781.3 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2224.20     178.30   12.47   <2e-16 ***
## log(Freq.x)  2209.85      80.25   27.54   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2467 on 501 degrees of freedom
## Multiple R-squared:  0.6021, Adjusted R-squared:  0.6014 
## F-statistic: 758.3 on 1 and 501 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 
## -3.7092 -0.5068  0.0602  0.5777  2.0457 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 15.82112    0.05886  268.77   <2e-16 ***
## log(Freq.x)  0.70024    0.02649   26.43   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8146 on 501 degrees of freedom
## Multiple R-squared:  0.5823, Adjusted R-squared:  0.5815 
## F-statistic: 698.6 on 1 and 501 DF,  p-value: < 2.2e-16

9 Modelo raíz-raíz (raíz-raíz)

Es éste el modelo que nos entrega el mayor coeficiente de determinación de todos (0.6022).

9.1 Diagrama de dispersión sobre raíz-raíz

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

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

9.2 Modelo raíz-raíz

Observemos nuevamente el resultado sobre raíz-raíz.

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 
## -7673.7 -1572.5  -334.8  1503.5 10761.9 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    2430.2      172.2   14.11   <2e-16 ***
## sqrt(Freq.x)   1183.4       42.9   27.59   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2465 on 501 degrees of freedom
## Multiple R-squared:  0.603,  Adjusted R-squared:  0.6022 
## F-statistic:   761 on 1 and 501 DF,  p-value: < 2.2e-16

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

9.3 Análisis de residuos

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

9.4 Ecuación del modelo

\[ \hat Y = { 2430.2}^2 + 2 \cdot 2430.2 \cdot 1183.4 \sqrt{X}+ 1183.4^2 \cdot X \]

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

Esta nueva variable se llamará: est_ing

h_y_m_comuna_corr_01$est_ing <- 2430.2^2 + 2 *  2430.2 * 1183.4 *sqrt(h_y_m_comuna_corr_01$Freq.x)+  1183.4^2* (h_y_m_comuna_corr_01$Freq.x) 

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

zona	código.x	Freq.x	anio	comuna.x	promedio_i	año	personas	Ingresos_expandidos	Freq.y	p_poblacional	código.y	multi_pob	est_ing
4101022001	04101	200	2017	La Serena	200287.4	2017	221054	44274327972	1157	0.0052340	04101	231732506.4	367335682
4101052007	04101	31	2017	La Serena	200287.4	2017	221054	44274327972	125	0.0005655	04101	25035923.3	81344027
4101062001	04101	97	2017	La Serena	200287.4	2017	221054	44274327972	345	0.0015607	04101	69099148.4	198396756
4101062004	04101	215	2017	La Serena	200287.4	2017	221054	44274327972	740	0.0033476	04101	148212666.1	391337422
4101062006	04101	54	2017	La Serena	200287.4	2017	221054	44274327972	224	0.0010133	04101	44864374.6	123796298
4101062024	04101	54	2017	La Serena	200287.4	2017	221054	44274327972	2392	0.0108209	04101	479087428.9	123796298
4101062030	04101	7	2017	La Serena	200287.4	2017	221054	44274327972	41	0.0001855	04101	8211782.9	30926746
4101062049	04101	37	2017	La Serena	200287.4	2017	221054	44274327972	115	0.0005202	04101	23033049.5	92708805
4101062901	04101	22	2017	La Serena	200287.4	2017	221054	44274327972	284	0.0012848	04101	56881617.8	63693775
4101072002	04101	106	2017	La Serena	200287.4	2017	221054	44274327972	394	0.0017824	04101	78913230.3	213570420
4101072027	04101	76	2017	La Serena	200287.4	2017	221054	44274327972	461	0.0020855	04101	92332485.3	162481981
4101072029	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	119	0.0005383	04101	23834199.0	13058105
4101072051	04101	47	2017	La Serena	200287.4	2017	221054	44274327972	179	0.0008098	04101	35851442.2	111158679
4101072054	04101	6	2017	La Serena	200287.4	2017	221054	44274327972	114	0.0005157	04101	22832762.1	28397454
4101082011	04101	8	2017	La Serena	200287.4	2017	221054	44274327972	304	0.0013752	04101	60887365.5	33377896
4101082018	04101	46	2017	La Serena	200287.4	2017	221054	44274327972	348	0.0015743	04101	69700010.6	109336495
4101082021	04101	62	2017	La Serena	200287.4	2017	221054	44274327972	748	0.0033838	04101	149814965.2	138022574
4101082022	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	13	0.0000588	04101	2603736.0	13058105
4101082023	04101	74	2017	La Serena	200287.4	2017	221054	44274327972	733	0.0033159	04101	146810654.4	159016935
4101082029	04101	10	2017	La Serena	200287.4	2017	221054	44274327972	140	0.0006333	04101	28040234.1	38099008
4101082043	04101	32	2017	La Serena	200287.4	2017	221054	44274327972	692	0.0031305	04101	138598871.6	83256889
4101082051	04101	8	2017	La Serena	200287.4	2017	221054	44274327972	36	0.0001629	04101	7210345.9	33377896
4101082052	04101	12	2017	La Serena	200287.4	2017	221054	44274327972	52	0.0002352	04101	10414944.1	42635909
4101082901	04101	6	2017	La Serena	200287.4	2017	221054	44274327972	37	0.0001674	04101	7410633.3	28397454
4101092003	04101	150	2017	La Serena	200287.4	2017	221054	44274327972	740	0.0033476	04101	148212666.1	286416049
4101092009	04101	2	2017	La Serena	200287.4	2017	221054	44274327972	144	0.0006514	04101	28841383.7	16841013
4101092034	04101	83	2017	La Serena	200287.4	2017	221054	44274327972	742	0.0033566	04101	148613240.9	174543391
4101092038	04101	4	2017	La Serena	200287.4	2017	221054	44274327972	329	0.0014883	04101	65894550.2	23011209
4101092039	04101	5	2017	La Serena	200287.4	2017	221054	44274327972	63	0.0002850	04101	12618105.4	25769460
4101102003	04101	17	2017	La Serena	200287.4	2017	221054	44274327972	70	0.0003167	04101	14020117.1	53428545
4101102013	04101	93	2017	La Serena	200287.4	2017	221054	44274327972	1201	0.0054331	04101	240545151.4	191614704
4101102015	04101	46	2017	La Serena	200287.4	2017	221054	44274327972	364	0.0016467	04101	72904608.7	109336495
4101102020	04101	276	2017	La Serena	200287.4	2017	221054	44274327972	2212	0.0100066	04101	443035699.3	487982121
4101102032	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	48	0.0002171	04101	9613794.6	13058105
4101102035	04101	2	2017	La Serena	200287.4	2017	221054	44274327972	46	0.0002081	04101	9213219.8	16841013
4101112010	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	66	0.0002986	04101	13218967.5	13058105
4101122010	04101	2	2017	La Serena	200287.4	2017	221054	44274327972	44	0.0001990	04101	8812645.0	16841013
4101122901	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	63	0.0002850	04101	12618105.4	13058105
4101132025	04101	35	2017	La Serena	200287.4	2017	221054	44274327972	987	0.0044650	04101	197683650.6	88949209
4101132032	04101	17	2017	La Serena	200287.4	2017	221054	44274327972	824	0.0037276	04101	165036806.6	53428545
4101132046	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	23	0.0001040	04101	4606609.9	13058105
4101132901	04101	4	2017	La Serena	200287.4	2017	221054	44274327972	36	0.0001629	04101	7210345.9	23011209
4101142012	04101	5	2017	La Serena	200287.4	2017	221054	44274327972	175	0.0007917	04101	35050292.7	25769460
4101142014	04101	62	2017	La Serena	200287.4	2017	221054	44274327972	519	0.0023478	04101	103949153.7	138022574
4101142019	04101	3	2017	La Serena	200287.4	2017	221054	44274327972	194	0.0008776	04101	38855753.0	20069584
4101142036	04101	16	2017	La Serena	200287.4	2017	221054	44274327972	225	0.0010179	04101	45064662.0	51320030
4101142040	04101	9	2017	La Serena	200287.4	2017	221054	44274327972	92	0.0004162	04101	18426439.6	35765184
4101142056	04101	140	2017	La Serena	200287.4	2017	221054	44274327972	383	0.0017326	04101	76710069.1	270023035
4101142057	04101	9	2017	La Serena	200287.4	2017	221054	44274327972	45	0.0002036	04101	9012932.4	35765184
4101142901	04101	69	2017	La Serena	200287.4	2017	221054	44274327972	324	0.0014657	04101	64893113.3	150313943
4101172030	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	113	0.0005112	04101	22632474.7	13058105
4102062009	04102	28	2017	Coquimbo	206027.8	2017	227730	46918711304	395	0.0017345	04102	81380981.7	75553719
4102062014	04102	3	2017	Coquimbo	206027.8	2017	227730	46918711304	128	0.0005621	04102	26371558.6	20069584
4102062024	04102	343	2017	Coquimbo	206027.8	2017	227730	46918711304	4341	0.0190620	04102	894366687.6	592780047
4102062901	04102	29	2017	Coquimbo	206027.8	2017	227730	46918711304	111	0.0004874	04102	22869086.0	77492880
4102072002	04102	74	2017	Coquimbo	206027.8	2017	227730	46918711304	816	0.0035832	04102	168118686.3	159016935
4102072011	04102	29	2017	Coquimbo	206027.8	2017	227730	46918711304	156	0.0006850	04102	32140337.1	77492880
4102072021	04102	9	2017	Coquimbo	206027.8	2017	227730	46918711304	106	0.0004655	04102	21838947.0	35765184
4102072023	04102	14	2017	Coquimbo	206027.8	2017	227730	46918711304	565	0.0024810	04102	116405708.0	47033225
4102072024	04102	96	2017	Coquimbo	206027.8	2017	227730	46918711304	1893	0.0083125	04102	390010628.8	196703560
4102082004	04102	46	2017	Coquimbo	206027.8	2017	227730	46918711304	296	0.0012998	04102	60984229.3	109336495
4102082007	04102	6	2017	Coquimbo	206027.8	2017	227730	46918711304	52	0.0002283	04102	10713445.7	28397454
4102082016	04102	1	2017	Coquimbo	206027.8	2017	227730	46918711304	14	0.0000615	04102	2884389.2	13058105
4102082020	04102	4	2017	Coquimbo	206027.8	2017	227730	46918711304	8	0.0000351	04102	1648222.4	23011209
4102082032	04102	28	2017	Coquimbo	206027.8	2017	227730	46918711304	307	0.0013481	04102	63250535.2	75553719
4102082901	04102	11	2017	Coquimbo	206027.8	2017	227730	46918711304	77	0.0003381	04102	15864140.7	40387217
4102092026	04102	7	2017	Coquimbo	206027.8	2017	227730	46918711304	40	0.0001756	04102	8241112.1	30926746
4102112017	04102	52	2017	Coquimbo	206027.8	2017	227730	46918711304	760	0.0033373	04102	156581129.4	120205322
4102112025	04102	20	2017	Coquimbo	206027.8	2017	227730	46918711304	233	0.0010231	04102	48004477.8	59637403
4102112029	04102	69	2017	Coquimbo	206027.8	2017	227730	46918711304	1312	0.0057612	04102	270308476.0	150313943
4102112901	04102	3	2017	Coquimbo	206027.8	2017	227730	46918711304	45	0.0001976	04102	9271251.1	20069584
4102122018	04102	5	2017	Coquimbo	206027.8	2017	227730	46918711304	460	0.0020199	04102	94772788.8	25769460
4102132001	04102	1	2017	Coquimbo	206027.8	2017	227730	46918711304	61	0.0002679	04102	12567695.9	13058105
4102142012	04102	6	2017	Coquimbo	206027.8	2017	227730	46918711304	288	0.0012647	04102	59336006.9	28397454
4102152012	04102	3	2017	Coquimbo	206027.8	2017	227730	46918711304	50	0.0002196	04102	10301390.1	20069584
4102152030	04102	11	2017	Coquimbo	206027.8	2017	227730	46918711304	122	0.0005357	04102	25135391.8	40387217
4102162013	04102	32	2017	Coquimbo	206027.8	2017	227730	46918711304	252	0.0011066	04102	51919006.1	83256889
4102162901	04102	2	2017	Coquimbo	206027.8	2017	227730	46918711304	19	0.0000834	04102	3914528.2	16841013
4102182004	04102	1	2017	Coquimbo	206027.8	2017	227730	46918711304	19	0.0000834	04102	3914528.2	13058105
4103012011	04103	1	2017	Andacollo	217096.4	2017	11044	2397612293	14	0.0012677	04103	3039349.2	13058105
4103012014	04103	5	2017	Andacollo	217096.4	2017	11044	2397612293	55	0.0049801	04103	11940300.3	25769460
4103012901	04103	5	2017	Andacollo	217096.4	2017	11044	2397612293	83	0.0075154	04103	18018998.6	25769460
4103032005	04103	26	2017	Andacollo	217096.4	2017	11044	2397612293	537	0.0486237	04103	116580749.8	71645724
4103032013	04103	3	2017	Andacollo	217096.4	2017	11044	2397612293	72	0.0065194	04103	15630938.5	20069584
4103032901	04103	1	2017	Andacollo	217096.4	2017	11044	2397612293	31	0.0028070	04103	6729987.4	13058105
4104012013	04104	2	2017	La Higuera	231674.2	2017	4241	982530309	53	0.0124971	04104	12278732.9	16841013
4104022002	04104	22	2017	La Higuera	231674.2	2017	4241	982530309	839	0.1978307	04104	194374659.1	63693775
4104022006	04104	2	2017	La Higuera	231674.2	2017	4241	982530309	43	0.0101391	04104	9961990.9	16841013
4104022901	04104	1	2017	La Higuera	231674.2	2017	4241	982530309	19	0.0044801	04104	4401809.9	13058105
4104032014	04104	1	2017	La Higuera	231674.2	2017	4241	982530309	66	0.0155624	04104	15290497.6	13058105
4104042003	04104	6	2017	La Higuera	231674.2	2017	4241	982530309	267	0.0629568	04104	61857013.1	28397454
4104042028	04104	1	2017	La Higuera	231674.2	2017	4241	982530309	52	0.0122613	04104	12047058.7	13058105
4104042901	04104	2	2017	La Higuera	231674.2	2017	4241	982530309	18	0.0042443	04104	4170135.7	16841013
4104052004	04104	28	2017	La Higuera	231674.2	2017	4241	982530309	144	0.0339543	04104	33361085.7	75553719
4104052017	04104	13	2017	La Higuera	231674.2	2017	4241	982530309	231	0.0544683	04104	53516741.7	44849935
4104052023	04104	10	2017	La Higuera	231674.2	2017	4241	982530309	311	0.0733318	04104	72050678.2	38099008
4104062004	04104	1	2017	La Higuera	231674.2	2017	4241	982530309	3	0.0007074	04104	695022.6	13058105
4104062901	04104	2	2017	La Higuera	231674.2	2017	4241	982530309	52	0.0122613	04104	12047058.7	16841013
4104072015	04104	7	2017	La Higuera	231674.2	2017	4241	982530309	21	0.0049517	04104	4865158.3	30926746
4104072018	04104	2	2017	La Higuera	231674.2	2017	4241	982530309	72	0.0169771	04104	16680542.9	16841013

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
4101022001	04101	200	2017	La Serena	200287.4	2017	221054	44274327972	1157	0.0052340	04101	231732506.4	367335682	317489.79
4101052007	04101	31	2017	La Serena	200287.4	2017	221054	44274327972	125	0.0005655	04101	25035923.3	81344027	650752.21
4101062001	04101	97	2017	La Serena	200287.4	2017	221054	44274327972	345	0.0015607	04101	69099148.4	198396756	575063.06
4101062004	04101	215	2017	La Serena	200287.4	2017	221054	44274327972	740	0.0033476	04101	148212666.1	391337422	528834.35
4101062006	04101	54	2017	La Serena	200287.4	2017	221054	44274327972	224	0.0010133	04101	44864374.6	123796298	552662.05
4101062024	04101	54	2017	La Serena	200287.4	2017	221054	44274327972	2392	0.0108209	04101	479087428.9	123796298	51754.31
4101062030	04101	7	2017	La Serena	200287.4	2017	221054	44274327972	41	0.0001855	04101	8211782.9	30926746	754310.89
4101062049	04101	37	2017	La Serena	200287.4	2017	221054	44274327972	115	0.0005202	04101	23033049.5	92708805	806163.52
4101062901	04101	22	2017	La Serena	200287.4	2017	221054	44274327972	284	0.0012848	04101	56881617.8	63693775	224273.86
4101072002	04101	106	2017	La Serena	200287.4	2017	221054	44274327972	394	0.0017824	04101	78913230.3	213570420	542056.90
4101072027	04101	76	2017	La Serena	200287.4	2017	221054	44274327972	461	0.0020855	04101	92332485.3	162481981	352455.49
4101072029	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	119	0.0005383	04101	23834199.0	13058105	109731.97
4101072051	04101	47	2017	La Serena	200287.4	2017	221054	44274327972	179	0.0008098	04101	35851442.2	111158679	620998.21
4101072054	04101	6	2017	La Serena	200287.4	2017	221054	44274327972	114	0.0005157	04101	22832762.1	28397454	249100.47
4101082011	04101	8	2017	La Serena	200287.4	2017	221054	44274327972	304	0.0013752	04101	60887365.5	33377896	109795.71
4101082018	04101	46	2017	La Serena	200287.4	2017	221054	44274327972	348	0.0015743	04101	69700010.6	109336495	314185.33
4101082021	04101	62	2017	La Serena	200287.4	2017	221054	44274327972	748	0.0033838	04101	149814965.2	138022574	184522.16
4101082022	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	13	0.0000588	04101	2603736.0	13058105	1004469.61
4101082023	04101	74	2017	La Serena	200287.4	2017	221054	44274327972	733	0.0033159	04101	146810654.4	159016935	216939.88
4101082029	04101	10	2017	La Serena	200287.4	2017	221054	44274327972	140	0.0006333	04101	28040234.1	38099008	272135.77
4101082043	04101	32	2017	La Serena	200287.4	2017	221054	44274327972	692	0.0031305	04101	138598871.6	83256889	120313.42
4101082051	04101	8	2017	La Serena	200287.4	2017	221054	44274327972	36	0.0001629	04101	7210345.9	33377896	927163.78
4101082052	04101	12	2017	La Serena	200287.4	2017	221054	44274327972	52	0.0002352	04101	10414944.1	42635909	819921.33
4101082901	04101	6	2017	La Serena	200287.4	2017	221054	44274327972	37	0.0001674	04101	7410633.3	28397454	767498.76
4101092003	04101	150	2017	La Serena	200287.4	2017	221054	44274327972	740	0.0033476	04101	148212666.1	286416049	387048.72
4101092009	04101	2	2017	La Serena	200287.4	2017	221054	44274327972	144	0.0006514	04101	28841383.7	16841013	116951.48
4101092034	04101	83	2017	La Serena	200287.4	2017	221054	44274327972	742	0.0033566	04101	148613240.9	174543391	235233.68
4101092038	04101	4	2017	La Serena	200287.4	2017	221054	44274327972	329	0.0014883	04101	65894550.2	23011209	69942.88
4101092039	04101	5	2017	La Serena	200287.4	2017	221054	44274327972	63	0.0002850	04101	12618105.4	25769460	409039.04
4101102003	04101	17	2017	La Serena	200287.4	2017	221054	44274327972	70	0.0003167	04101	14020117.1	53428545	763264.92
4101102013	04101	93	2017	La Serena	200287.4	2017	221054	44274327972	1201	0.0054331	04101	240545151.4	191614704	159545.97
4101102015	04101	46	2017	La Serena	200287.4	2017	221054	44274327972	364	0.0016467	04101	72904608.7	109336495	300374.99
4101102020	04101	276	2017	La Serena	200287.4	2017	221054	44274327972	2212	0.0100066	04101	443035699.3	487982121	220606.75
4101102032	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	48	0.0002171	04101	9613794.6	13058105	272043.85
4101102035	04101	2	2017	La Serena	200287.4	2017	221054	44274327972	46	0.0002081	04101	9213219.8	16841013	366108.98
4101112010	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	66	0.0002986	04101	13218967.5	13058105	197850.08
4101122010	04101	2	2017	La Serena	200287.4	2017	221054	44274327972	44	0.0001990	04101	8812645.0	16841013	382750.30
4101122901	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	63	0.0002850	04101	12618105.4	13058105	207271.51
4101132025	04101	35	2017	La Serena	200287.4	2017	221054	44274327972	987	0.0044650	04101	197683650.6	88949209	90120.78
4101132032	04101	17	2017	La Serena	200287.4	2017	221054	44274327972	824	0.0037276	04101	165036806.6	53428545	64840.47
4101132046	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	23	0.0001040	04101	4606609.9	13058105	567743.69
4101132901	04101	4	2017	La Serena	200287.4	2017	221054	44274327972	36	0.0001629	04101	7210345.9	23011209	639200.25
4101142012	04101	5	2017	La Serena	200287.4	2017	221054	44274327972	175	0.0007917	04101	35050292.7	25769460	147254.06
4101142014	04101	62	2017	La Serena	200287.4	2017	221054	44274327972	519	0.0023478	04101	103949153.7	138022574	265939.45
4101142019	04101	3	2017	La Serena	200287.4	2017	221054	44274327972	194	0.0008776	04101	38855753.0	20069584	103451.46
4101142036	04101	16	2017	La Serena	200287.4	2017	221054	44274327972	225	0.0010179	04101	45064662.0	51320030	228089.02
4101142040	04101	9	2017	La Serena	200287.4	2017	221054	44274327972	92	0.0004162	04101	18426439.6	35765184	388752.00
4101142056	04101	140	2017	La Serena	200287.4	2017	221054	44274327972	383	0.0017326	04101	76710069.1	270023035	705020.98
4101142057	04101	9	2017	La Serena	200287.4	2017	221054	44274327972	45	0.0002036	04101	9012932.4	35765184	794781.87
4101142901	04101	69	2017	La Serena	200287.4	2017	221054	44274327972	324	0.0014657	04101	64893113.3	150313943	463931.92
4101172030	04101	1	2017	La Serena	200287.4	2017	221054	44274327972	113	0.0005112	04101	22632474.7	13058105	115558.45
4102062009	04102	28	2017	Coquimbo	206027.8	2017	227730	46918711304	395	0.0017345	04102	81380981.7	75553719	191275.24
4102062014	04102	3	2017	Coquimbo	206027.8	2017	227730	46918711304	128	0.0005621	04102	26371558.6	20069584	156793.62
4102062024	04102	343	2017	Coquimbo	206027.8	2017	227730	46918711304	4341	0.0190620	04102	894366687.6	592780047	136553.80
4102062901	04102	29	2017	Coquimbo	206027.8	2017	227730	46918711304	111	0.0004874	04102	22869086.0	77492880	698134.05
4102072002	04102	74	2017	Coquimbo	206027.8	2017	227730	46918711304	816	0.0035832	04102	168118686.3	159016935	194873.70
4102072011	04102	29	2017	Coquimbo	206027.8	2017	227730	46918711304	156	0.0006850	04102	32140337.1	77492880	496749.23
4102072021	04102	9	2017	Coquimbo	206027.8	2017	227730	46918711304	106	0.0004655	04102	21838947.0	35765184	337407.40
4102072023	04102	14	2017	Coquimbo	206027.8	2017	227730	46918711304	565	0.0024810	04102	116405708.0	47033225	83244.65
4102072024	04102	96	2017	Coquimbo	206027.8	2017	227730	46918711304	1893	0.0083125	04102	390010628.8	196703560	103911.02
4102082004	04102	46	2017	Coquimbo	206027.8	2017	227730	46918711304	296	0.0012998	04102	60984229.3	109336495	369380.05
4102082007	04102	6	2017	Coquimbo	206027.8	2017	227730	46918711304	52	0.0002283	04102	10713445.7	28397454	546104.89
4102082016	04102	1	2017	Coquimbo	206027.8	2017	227730	46918711304	14	0.0000615	04102	2884389.2	13058105	932721.78
4102082020	04102	4	2017	Coquimbo	206027.8	2017	227730	46918711304	8	0.0000351	04102	1648222.4	23011209	2876401.12
4102082032	04102	28	2017	Coquimbo	206027.8	2017	227730	46918711304	307	0.0013481	04102	63250535.2	75553719	246103.32
4102082901	04102	11	2017	Coquimbo	206027.8	2017	227730	46918711304	77	0.0003381	04102	15864140.7	40387217	524509.31
4102092026	04102	7	2017	Coquimbo	206027.8	2017	227730	46918711304	40	0.0001756	04102	8241112.1	30926746	773168.66
4102112017	04102	52	2017	Coquimbo	206027.8	2017	227730	46918711304	760	0.0033373	04102	156581129.4	120205322	158164.90
4102112025	04102	20	2017	Coquimbo	206027.8	2017	227730	46918711304	233	0.0010231	04102	48004477.8	59637403	255954.52
4102112029	04102	69	2017	Coquimbo	206027.8	2017	227730	46918711304	1312	0.0057612	04102	270308476.0	150313943	114568.55
4102112901	04102	3	2017	Coquimbo	206027.8	2017	227730	46918711304	45	0.0001976	04102	9271251.1	20069584	445990.76
4102122018	04102	5	2017	Coquimbo	206027.8	2017	227730	46918711304	460	0.0020199	04102	94772788.8	25769460	56020.56
4102132001	04102	1	2017	Coquimbo	206027.8	2017	227730	46918711304	61	0.0002679	04102	12567695.9	13058105	214067.29
4102142012	04102	6	2017	Coquimbo	206027.8	2017	227730	46918711304	288	0.0012647	04102	59336006.9	28397454	98602.27
4102152012	04102	3	2017	Coquimbo	206027.8	2017	227730	46918711304	50	0.0002196	04102	10301390.1	20069584	401391.68
4102152030	04102	11	2017	Coquimbo	206027.8	2017	227730	46918711304	122	0.0005357	04102	25135391.8	40387217	331042.76
4102162013	04102	32	2017	Coquimbo	206027.8	2017	227730	46918711304	252	0.0011066	04102	51919006.1	83256889	330384.48
4102162901	04102	2	2017	Coquimbo	206027.8	2017	227730	46918711304	19	0.0000834	04102	3914528.2	16841013	886369.10
4102182004	04102	1	2017	Coquimbo	206027.8	2017	227730	46918711304	19	0.0000834	04102	3914528.2	13058105	687268.68
4103012011	04103	1	2017	Andacollo	217096.4	2017	11044	2397612293	14	0.0012677	04103	3039349.2	13058105	932721.78
4103012014	04103	5	2017	Andacollo	217096.4	2017	11044	2397612293	55	0.0049801	04103	11940300.3	25769460	468535.63
4103012901	04103	5	2017	Andacollo	217096.4	2017	11044	2397612293	83	0.0075154	04103	18018998.6	25769460	310475.42
4103032005	04103	26	2017	Andacollo	217096.4	2017	11044	2397612293	537	0.0486237	04103	116580749.8	71645724	133418.48
4103032013	04103	3	2017	Andacollo	217096.4	2017	11044	2397612293	72	0.0065194	04103	15630938.5	20069584	278744.22
4103032901	04103	1	2017	Andacollo	217096.4	2017	11044	2397612293	31	0.0028070	04103	6729987.4	13058105	421229.19
4104012013	04104	2	2017	La Higuera	231674.2	2017	4241	982530309	53	0.0124971	04104	12278732.9	16841013	317754.96
4104022002	04104	22	2017	La Higuera	231674.2	2017	4241	982530309	839	0.1978307	04104	194374659.1	63693775	75916.30
4104022006	04104	2	2017	La Higuera	231674.2	2017	4241	982530309	43	0.0101391	04104	9961990.9	16841013	391651.46
4104022901	04104	1	2017	La Higuera	231674.2	2017	4241	982530309	19	0.0044801	04104	4401809.9	13058105	687268.68
4104032014	04104	1	2017	La Higuera	231674.2	2017	4241	982530309	66	0.0155624	04104	15290497.6	13058105	197850.08
4104042003	04104	6	2017	La Higuera	231674.2	2017	4241	982530309	267	0.0629568	04104	61857013.1	28397454	106357.51
4104042028	04104	1	2017	La Higuera	231674.2	2017	4241	982530309	52	0.0122613	04104	12047058.7	13058105	251117.40
4104042901	04104	2	2017	La Higuera	231674.2	2017	4241	982530309	18	0.0042443	04104	4170135.7	16841013	935611.83
4104052004	04104	28	2017	La Higuera	231674.2	2017	4241	982530309	144	0.0339543	04104	33361085.7	75553719	524678.60
4104052017	04104	13	2017	La Higuera	231674.2	2017	4241	982530309	231	0.0544683	04104	53516741.7	44849935	194155.56
4104052023	04104	10	2017	La Higuera	231674.2	2017	4241	982530309	311	0.0733318	04104	72050678.2	38099008	122504.85
4104062004	04104	1	2017	La Higuera	231674.2	2017	4241	982530309	3	0.0007074	04104	695022.6	13058105	4352701.65
4104062901	04104	2	2017	La Higuera	231674.2	2017	4241	982530309	52	0.0122613	04104	12047058.7	16841013	323865.63
4104072015	04104	7	2017	La Higuera	231674.2	2017	4241	982530309	21	0.0049517	04104	4865158.3	30926746	1472702.21
4104072018	04104	2	2017	La Higuera	231674.2	2017	4241	982530309	72	0.0169771	04104	16680542.9	16841013	233902.96

Guardamos:

saveRDS(h_y_m_comuna_corr_01, "P15/region_04_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