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CURSO DEESTADSTICASociedad Espaola Sociedad Espaola de Bioqumica Clnica y Patologa Molecular (SEQC),
Calle Padilla 268, despacho 68, 08025-Barcelona, http://www.seqc.es
Gua de estudio. 6.
Estadstica aplicada
(I) La calidad analtica
Conocimientos previos
Contenidos
Bibliografa
Introduccin
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Vol. 2, nm. 5, Pg. 2Curso de estadstica
Objetivos
Contenidos
Bibliografa acerca de la validacin de
http://www.ministeriodesalud.go.cr/protocolos/guiavalidacionmetodosanaliticos.pdfhttp://www.ministeriodesalud.go.cr/protocolos/guiavalidacionmetodosanaliticos.pdfhttp://www.eurachem.org/guides/valid.pdf7/17/2019 2008-05
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Vol. 2, nm. 5, Pg. 4Curso de estadstica
http://www.clinchem.org/cgi/reprint/54/3/612http://www.clinchem.org/cgi/reprint/44/11/2240http://www.clinchem.org/cgi/reprint/44/11/2240http://www.clinchem.org/cgi/reprint/44/11/2340http://www.clinchem.org/cgi/reprint/44/11/2340http://www.clinchem.org/cgi/reprint/43/11/2039http://www.clinchem.org/cgi/reprint/43/11/2039http://www.clinchem.org/cgi/reprint/30/5/751.pdfhttp://www.clinchem.org/cgi/reprint/30/5/751.pdfhttp://www.clinchem.org/cgi/reprint/46/1/100http://www.multiqc.com/MethodComparison.pdfhttp://www.multiqc.com/MethodComparison.pdfhttp://www.clinchem.org/cgi/reprint/45/2/314http://www.clinchem.org/cgi/reprint/45/6/882http://www.clinchem.org/cgi/reprint/48/6/9197/17/2019 2008-05
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Vol. 2, nm. 5, Pg. 5Curso de estadstica
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Vol. 2, nm. 5, Pg. 6Curso de estadstica
Nombres propios:
Harold Jeffreys (1891-1989)
The earth: Its origin, history andphysical constitution
Earthquakes andmountains.
Methods of mathematical physics
Mathematics,probability & miscellaneous other science
Theory ofprobability
Grammar ofScience
Statistical methods for research workers
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Vol. 2, nm. 5, Pg. 7Curso de estadstica
Tests de significacin parael coeficiente de correlacin
t
Objetivo
Limitaciones
( ),
Hiptesis
0
1
: 0
: 0
=
( ) ( ) ( ){ }1 1 2 2, , , , , ,
Procedimiento
( ) ( ) ( ){ }1 1 2 2, , , , , ,
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Vol. 2, nm. 5, Pg. 8Curso de estadstica
( )
( ) ( )
1
2 2
1 1
=
= =
=
1
1
=
=
=
=
22
1=
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Vol. 2, nm. 5, Pg. 10Curso de estadstica
n 27 24
r 0, 65 0, 74
Z 1 0, 65
ln 1, 5511 0, 65
+=
1 0, 74ln 1, 901
1 0, 74
+=
1 1
0,29927 3 24 3= + =
1, 551 1, 9011,17
0,299
= =
( )0,05 1,96=
Y X
n GGT edad GGT- media edad-media
(GGT-media) x
(edad-media)
(GGT-
media)2(edad-
media)2
50,4 15 -8,96 -3,52 31,538 80,274 12,390
63,6 24 4,25 5,48 23,277 18,043 30,030
66,6 12 7,28 -6,52 -47,477 53,024 42,510
64,6 22 5,22 3,48 18,181 27,294 12,110
52,7 17 -6,63 -1,52 10,082 43,998 2,310
61,3 21 1,93 2,48 4,788 3,727 6,150
49,2 19 -10,13 0,48 -4,861 102,549 0,230
68,7 13 9,37 -5,52 -51,715 87,772 30,470
52,1 15 -7,23 -3,52 25,464 52,331 12,390
58,8 13 -0,57 -5,52 3,163 0,328 30,47063,8 16 4,45 -2,52 -11,210 19,790 6,350
58,8 9 -0,54 -9,52 5,161 0,294 90,630
60,3 21 0,94 2,48 2,335 0,886 6,150
53,2 25 -6,15 6,48 -39,865 37,847 41,990
54,8 19 -4,51 0,48 -2,163 20,300 0,230
66,5 22 7,15 3,48 24,896 51,181 12,110
53,2 15 -6,12 -3,52 21,543 37,457 12,390
56,8 14 -2,51 -4,52 11,341 6,296 20,430
57,0 13 -2,28 -5,52 12,571 5,187 30,470
69,3 25 9,95 6,48 64,453 98,931 41,990
56,5 26 -2,83 7,48 -21,182 8,019 55,950
63,8 21 4,50 2,48 11,170 20,286 6,150
54,2 25 -5,09 6,48 -32,988 25,916 41,990
74,2 14 14,86 -4,52 -67,159 220,764 20,430
53,0 27 -6,35 8,48 -53,850 40,326 71,910
-2 23
suma 1483,2 463 -62,5058 1062,8188 638,2400
media 59,3 18,5
-0,076
-0,365
crtica 1,714
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Vol. 2, nm. 5, Pg. 11Curso de estadstica
Problemas
1 2 3 4
56 221 259 110 646
= + + +
+ + + =
2 2 2 2 2
1 2 3 4
2 2 2 2
2
9 8 6 7 230
15,166
= + + +
+ + + =
= =
2
11 0,80
12,236
0,20
=
= =
( )( )( )( )
( )( )
Pr , 0,80
Pr 646 2, 236 15,166 , 646 2, 236 15,166 0, 80
Pr 612 , 680 0,80
+
+
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Vol. 2, nm. 5, Pg. 12Curso de estadstica
14,3=
646 14, 3 9237, 8
=
= =
2 2 2
2
230 204, 49 47032, 7
216,87
=
= =
=
( )( )( )( )
( )( )
Pr , 0,80
Pr 9237, 8 2, 236 216, 87 , 9237, 8 2, 236 216, 87 0, 80
Pr 8752, 9 , 9722, 7 0,80
+
+
( )
( )
( ) 0
( ) 1+
=
( )
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( ) ( ) ( )Pr
= =
( ) ( )lim 0
= =
( ) ( )lim 1+
+ = =
( )
( ) ( ) ( )Pr =
( ) ( ) ( )
=
( )
( ) ( )+
= = E
( ) ( ) ( )( ) ( )( ) ( )( ) ( )( ) = + +E E E E
( ) =E
( )( ) ( )( )=E E
( )=
( ) ( )( ) ( ) ( )
( )
+
+
= = =
=
E E
( )var
( ) ( )( ) ( ) ( )2 2var +
= =
( )
( )
( ) 0 ( )
1
( )( ) ( )( )1
Pr E
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Vol. 2, nm. 5, Pg. 14Curso de estadstica
Demostracin
( )
( )
( )
( )( ) ( ) ( )+
=
( ) ( ) ( )= ( )( ) ( )
( ) ( ) ( ) ( )+
( ) 1+
=
( ) ( )( )Pr=
( )( ) ( ) ( ) ( ) ( ) ( ) ( )( )( )Pr
+
=
(x)
k
(x)
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Vol. 2, nm. 5, Pg. 15Curso de estadstica
( )( ) ( )( )1
Pr E
( )( ) ( )( )Pr 1< E
( ) ( )( ) ( )2 2
= = E
2 2
( )( ) ( )( )22 2 2
2 2Pr 1
<
E
( )( )2E
( )( )2 2 =E
( )( )2
2
2 2 2 2 2
11 1 1
= =
( )( )2 2 2 21
Pr 1 <
( )( ) 21
Pr 1 <
( ) ( )( )
( )
2 2 2
2 2
2 2
Pr Pr
Pr
Pr
< = < < =
< < +=
< < +
2
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Vol. 2, nm. 5, Pg. 16Curso de estadstica
( )2
1100 1
Introduccin a : Regresin en R (1)
lm( ) y glm( )
lm( )
glm( )
glm(formula, family = gaussian, data, weights, subset, na.action, start = NULL,
etastart, mustart, offset, control = glm.control(...), model = TRUE, method ="glm.fit", x = FALSE, y = TRUE, contrasts = NULL, ...)
data data frame
environment(formula), que habitualmente
es el entorno donde se est realizando el procedimiento glm
family
family = binomial
family = poisson
family = gaussian
family = Gamma
family = inverse.gaussian
formula " "
method glm.fit)
model.frame que no realiza realizar ajuste
na.action NA
na.omit ( ) Si se omite este argumento, R toma
el valor options (por defecto, na.fail
NULL (no hacer nada), o na.exclude.
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subset
...
weights NULL
1. Datos
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dettach( )
3. Regresin
4. Almacenamiento del resultado
5. Informacin adicional
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6. Residuales
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5 10 15 20 25
-10
-5
0
5
10
15
Index
res
-10
-5
0
5
10
15
Histogram of res
res
Frequency
-15 -10 -5 0 5 10 15
0
1
2
3
4
5
6
7
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7. Valores ajustados
fitted.values() glm( ) glm.lineal
pred
59.25 59.30 59.35 59.40
-10
-5
0
5
10
15
pred
res
8. Consideraciones finales
glm
glm.linear