4_regresion_lineal_supuestos_2 - Zaida Moreno Páez

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2. Regresión Lineal: 
Supuestos y Propiedades
Pilar Alcalde
2 sem 2014
1. Estimación por MCO
Supuestos de Gauss-Markov:
1. Linealidad
2. Muestreo aleatorio simple 
3. No colinealidad perfecta
4. Media condicional nula
5. Homocedasticidad
6. Normalidad
1. Estimación por MCO
1. Linealidad
+
+
1	1.1000000000000001	1.2000000000000002	1.3000000000000003	1.4000000000000004	1.5000000000000004	1.6000000000000005	1.7000000000000006	1.8000000000000007	1.9000000000000008	2.0000000000000009	2.100000000000001	2.2000000000000011	2.3000000000000012	2.4000000000000012	2.5000000000000013	2.6000000000000014	2.7000000000000015	2.8000000000000016	2.9000000000000017	3.0000000000000018	3.1000000000000019	3.200000000000002	3.300000000000002	3.4000000000000021	3.5000000000000022	3.6000000000000023	3.7000000000000024	3.8000000000000025	3.9000000000000026	4.0000000000000027	4.1000000000000023	4.200000000000002	4.3000000000000016	4.400000000	0000012	4.5000000000000009	4.6000000000000005	4.7	4.8	4.8999999999999995	4.9999999999999991	5.0999999999999988	5.1999999999999984	5.299999999999998	5.399999999999997	7	5.4999999999999973	5.599999999999997	5.6999999999999966	5.7999999999999963	5.8999999999999959	5.9999999999999956	6.0999999999999952	6.1999999999999948	6.2999999999999945	6.3999999999999941	6.4999999999999938	6.5999999999999934	6.6999999999999931	6.7999999999999927	6.8999999999999924	6.999999999999992	7.0999999999999917	7.1999999999999913	7.2999999999999909	7.3999999999999906	7.4999999999999902	2.4	2.484	2.5760000000000001	2.6760000000000002	2.7840000000000007	2.9000000000000004	3.0240000000000009	3.1560000000000006	3.2960000000000012	3.4440000000000013	3.6000000000000014	3.7640000000000016	3.9360000000000017	4.1160000000000023	4.304000000000002	4.5000000000000036	4.7040000000000033	4.9160000000000039	5.1360000000000037	5.3640000000000043	5.600000000000005	5.8440000000000047	6.0960000000000054	6.3560000000000052	6.6240000000000059	6.9000000000000066	7.1840000000000073	7.4760000000000071	7.7760000000000078	8.0840000000000085	8.4000000000000092	8.7240000000000073	9.0560000000000063	9.3960000000000043	9.7440000000000033	10.100000000000003	10.464000000000002	10.836000000000002	11.215999999999999	11.603999999999999	11.999999999999998	12.403999999999996	12.815999999999995	13.235999999999992	13.663999999999991	14.099999999999989	14.543999999999988	14.995999999999984	15.455999999999984	15.923999999999982	16.399999999999977	16.883999999999979	17.375999999999976	17.875999999999976	18.383999999999968	18.89999999999997	19.423999999999964	19.955999999999964	20.495999999999963	21.043999999999958	21.599999999999955	22.163999999999955	22.735999999999951	23.315999999999949	23.903999999999947	24.499999999999943	1	1.1000000000000001	1.2000000000000002	1.3000000000000003	1.4000000000000004	1.5000000000000004	1.6000000000000005	1.7000000000000006	1.8000000000000007	1.9000000000000008	2.0000000000000009	2.100000000000001	2.2000000000000011	2.3000000000000012	2.4000000000000012	2.5000000000000013	2.6000000000000014	2.7000000000000015	2.8000000000000016	2.9000000000000017	3.0000000000000018	3.1000000000000019	3.200000000000002	3.300000000000002	3.4000000000000021	3.5000000000000022	3.6000000000000023	3.7000000000000024	3.8000000000000025	3.9000000000000026	4.0000000000000027	4.1000000000000023	4.200000000000002	4.3000000000000016	4.400000	0000000012	4.5000000000000009	4.6000000000000005	4.7	4.8	4.8999999999999995	4.9999999999999991	5.0999999999999988	5.1999999999999984	5.299999999999998	5.3999999999999977	5.499999999999997	3	5.599999999999997	5.6999999999999966	5.7999999999999963	5.8999999999999959	5.9999999999999956	6.0999999999999952	6.1999999999999948	6.2999999999999945	6.3999999999999941	6.4999999999999938	6.5999999999999934	6.6999999999999931	6.7999999999999927	6.8999999999999924	6.999999999999992	7.0999999999999917	7.1999999999999913	7.2999999999999909	7.3999999999999906	7.4999999999999902	5	4.7272727272727266	4.5	4.3076923076923066	4.1428571428571423	3.9999999999999991	3.8749999999999991	3.7647058823529402	3.6666666666666661	3.5789473684210522	3.4999999999999991	3.4285714285714279	3.3636363636363629	3.3043478260869561	3.2499999999999991	3.1999999999999993	3.1538461538461533	3.1111111111111107	3.0714285714285707	3.0344827586206891	2.9999999999999996	2.9677419354838701	2.9374999999999996	2.9090909090909083	2.8823529411764701	2.8571428571428568	2.833333333333333	2.8108108108108105	2.7894736842105257	2.7692307692307687	2.7499999999999996	2.7317073170731705	2.7142857142	85714	2.6976744186046511	2.6818181818181817	2.6666666666666665	2.652173913043478	2.6382978723404253	2.625	2.6122448979591839	2.6	2.5882352941176472	2.5769230769230771	2.5660377358490569	2.5555555555555558	2.5454545454545459	2.535714285714286	2.5263157894736845	2.5172413793103452	2.5084745762711869	2.5000000000000004	2.4918032786885251	2.4838709677419359	2.4761904761904767	2.4687500000000004	2.4615384615384621	2.454545454545455	2.4477611940298512	2.4411764705882359	2.4347826086956528	2.4285714285714288	2.422535211267606	2.416666666666667	2.4109589041095898	2.4054054054054061	2.4000000000000004	1. Estimación por MCO
(GP 2010)
4. Media Condicional Nula
1. Estimación por MCO
4. Media Condicional Nula
+
E+
1	1.1000000000000001	1.2000000000000002	1.3000000000000003	1.4000000000000004	1.5000000000000004	1.6000000000000005	1.7000000000000006	1.8000000000000007	1.9000000000000008	2.0000000000000009	2.100000000000001	2.2000000000000011	2.3000000000000012	2.4000000000000012	2.5000000000000013	2.6000000000000014	2.7000000000000015	2.8000000000000016	2.9000000000000017	3.0000000000000018	3.1000000000000019	3.200000000000002	3.300000000000002	3.4000000000000021	3.5000000000000022	3.6000000000000023	3.7000000000000024	3.8000000000000025	3.9000000000000026	4.0000000000000027	4.1000000000000023	4.200000000000002	4.3000000000000016	4.4000000000000012	4.5000000000000009	4.6000000000000005	4.7	4.8	4.8999999	999999995	4.9999999999999991	5.0999999999999988	5.1999999999999984	5.299999999999998	5.3999999999999977	5.4999999999999973	5.599999999999997	5.6999999999999966	5.7999999999999963	5.8999999999999959	5.9999999999999956	6.0999999999999952	6.1999999999999948	6.2999999999999945	6.3999999999999941	6.4999999999999938	6.5999999999999934	6.699999999999993	1	6.7999999999999927	6.8999999999999924	6.999999999999992	7.0999999999999917	7.1999999999999913	7.2999999999999909	7.3999999999999906	7.4999999999999902	12.182493960703473	12.807103782663029	13.463738035001692	14.154038645375808	14.879731724872837	15.642631884188171	16.444646771097055	17.28778184056765	18.174145369443067	19.105953728231651	20.085536923187675	21.115344422540627	22.197951281441647	23.336064580942722	24.532530197109363	25.790339917193084	27.112638920657908	28.502733643767293	29.964100047397036	31.500392308747966	33.11545195869234	34.813317487602049	36.598234443678024	38.474666049032173	40.447304360067434	42.521082000062819	44.701184493300872	46.993063231579349	49.402449105530238	51.935366834831491	54.598150033144336	57.397457045446238	60.340287597362057	63.434000298123344	66.686331040925211	70.105412346687856	73.699793699595844	77.478462925260828	81.450868664968141	85.626944002200517	90.017131300521811	94.632408314923978	99.484315641933776	104.58498557711404	109.94717245212343	115.58428452718745	121.51041751873476	127.74038984602858	134.2897796849353	141.17496392147649	148.41315910257634	156.02246448639457	164.02190729990139	172.43149031685374	181.27224187515074	190.56626845862931	200.33680997479112	210.60829786667361	221.40641620418637	232.75816590766107	244.69193226421953	257.2375559057736	270.42640742615157	284.29146582391945	298.86740096705898	314.1906602856925	1. Estimación por MCO
(GP 2010)
5. Homocedasticidad
1. Estimación por MCO
(GP 2010)
5. Homocedasticidad
1. Estimación por MCO
Sesgo
Varianzas Muestrales
Eficiencia
Teorema de Gauss-Markov: Bajo 1-5,
MCO es Mejor Estimador Lineal Insesgado.
- Bajo 1-6, MCO es Mejor Estimador Insesgado.
2. Regresión Lineal: 
Supuestos y PropiedadesPilar Alcalde
2 sem 2014