TALLER 7 EVENTO: COMPARACIÓN DE PERFILES DE DISOLUCIÓNOBJETIVO ESTABLECER EQUIVALENCIA GLOBAL PARA 2 PERFILES DE DISOLUCIÓN UTILIZANDO

EL MÉTODO MULTIVARIADO BASADO EN LA DISTANCIA ESTADÍSTICA MULTIVARIADATIEMPO

LOTE 15 20 45 601 PRUEBA 54.83 62.84 93.94 102.921 56.16 65.59 91.96 99.821 58.85 69.27 96.32 105.921 55.39 65.06 89.18 99.311 55.09 65.01 97.22 104.731 53.98 62.51 89.15 98.921 51.06 59.92 89.71 96.801 54.85 63.36 90.60 98.341 67.84 77.46 103.98 104.771 53.51 63.06 89.84 99.161 53.07 62.84 89.39 96.541 54.36 63.75 89.55 99.485 REFERENCIA 50.98 60.80 93.24 94.525 50.50 67.39 97.14 98.295 59.35 71.06 99.14 101.675 47.71 62.77 91.13 94.335 50.09 64.30 92.89 95.365 41.05 53.44 84.92 89.875 47.59 62.45 92.16 94.165 54.10 67.15 97.78 99.725 57.19 77.44 98.79 100.775 50.76 66.15 93.88 95.755 52.85 78.62 100.89 101.655 37.25 51.45 88.00 91.67

n1 12 12 12 12n2 12 12 12 12m1 55.75 65.06 92.57 100.56m2 49.95 65.25 94.16 96.48

m1-m2 5.80 -0.20 -1.59 4.08s21 17.94808106 20.286572 20.9142545 10.2331538s22 38.1396697 66.7927424 22.959697 15.3004364

matriz de covarianza m1 LOTE 1x 54.83 62.84 93.94 102.92

56.16 65.59 91.96 99.82 58.85 69.27 96.32 105.92 55.39 65.06 89.18 99.31 55.09 65.01 97.22 104.73 53.98 62.51 89.15 98.92 51.06 59.92 89.71 96.80 54.85 63.36 90.60 98.34 67.84 77.46 103.98 104.77 53.51 63.06 89.84 99.16 53.07 62.84 89.39 96.54 54.36 63.75 89.55 99.48

xt54.83 56.16 58.85 55.39 55.0962.84 65.59 69.27 65.06 65.0193.94 91.96 96.32 89.18 97.22

102.92 99.82 105.92 99.31 104.73

m mt 55.7555.7565.06 transponer de mt92.57

100.56

xt*x37493.0639 43729.3622 62114.6923 67373.76143729.3622 51010.2897 72463.5424 78612.875962114.6923 72463.5424 103060.516 111841.196467373.761 78612.8759 111841.1964 121458.317

m*mT3107.969584 3626.8085 5160.70036 5606.089743626.808495 4232.26145 6022.21849 6541.960395160.700358 6022.21849 8569.2049 9308.762065606.089742 6541.96039 9308.76206 10112.146

m1 matriz var cov((xt*x)-M*Mt*n)/(n-1)

17.94808106 18.8782053 16.9352727 9.15309924 18.8782053 20.286572 17.9018636 9.94102348 16.93527273 17.9018636 20.9142545 12.3683364 9.153099242 9.94102348 12.3683364 10.2331538

matriz de covarianza m2 LOTE 5

x 50.98 60.80 93.24 94.5250.50 67.39 97.14 98.2959.35 71.06 99.14 101.6747.71 62.77 91.13 94.3350.09 64.30 92.89 95.3641.05 53.44 84.92 89.8747.59 62.45 92.16 94.1654.10 67.15 97.78 99.7257.19 77.44 98.79 100.7750.76 66.15 93.88 95.7552.85 78.62 100.89 101.6537.25 51.45 88.00 91.67

xt50.98 50.50 59.35 47.71 50.0960.80 67.39 71.06 62.77 64.3093.24 97.14 99.14 91.13 92.8994.52 98.29 101.67 94.33 95.36

m mt 49.9549.9565.2594.1696.48

xt*x30361.5644 39592.4033 56730.5204 58068.76239592.4033 51828.0802 74132.6773 75872.7756730.5204 74132.6773 106653.357 109221.45758068.762 75872.77 109221.457 111868.9896

m*mT2495.169003 3259.4295 4703.61544 4819.33683259.429503 4257.78 6144.31444 6295.48084703.615439 6144.31444 8866.73334 9084.8784

4819.3368 6295.4808 9084.8784 9308.3904

m2 matriz var cov((xt*x)-M*Mt*n)/(n-1)

38.1396697 43.5681152 26.1031939 21.520036443.56811515 66.7927424 36.4458212 29.727309126.10319394 36.4458212 22.959697 18.446918221.52003636 29.7273091 18.4469182 15.3004364

MATRIZ DE CORRELACIÓN PARCIAL MC

15 20 45 60

15 28.04387538 31.2231602 21.5192333 15.336567820 31.22316023 43.5396572 27.1738424 19.834166345 21.51923333 27.1738424 21.9369758 15.407627360 15.3365678 19.8341663 15.4076273 12.7667951

INV MC

Spool inv 0.207675915 -0.09703471 -0.09307029 0.01359458 -0.097034713 0.14960467 -0.05749455 -0.04646819 -0.093070291 -0.05749455 0.43870778 -0.32832913 0.013594581 -0.04646819 -0.32832913 0.53043354

DM

(xt-xr) idem a (m1-m2) 10

1010

10

(xt-xr) T 10 10 10 10idem a (m1-m2) T

(xt-xr)T *Spool INV(m1-m2)T *INV MC 0.3116549225 -0.51392781 -0.40186186 1.69230809

(xt-xr) *Spool INV*DMT-(xt-xr)

10.88173342

DM 3.30 distancia de mahalanobish

PRUEBA DE HIPÓTESIS DE EQUIVALENCIA ESTADÍSTICA GLOBAL

n 12 2n-p-1 19p 4 K 1.2954545455

CR 14.0967910154Fp,2n-p-1,0.9 o Ftab 2.27

DM 4.88 ANTERIORDM 3.3 ACTUAL

NO SON EQUIVALENTES NI SIMILARES CON UN 10%

(m1-m2)T *INV MC*(m1-m2) o DM2

NO SON SIMILARES YA QUE CR EXCEDE A Ftab

EVENTO: COMPARACIÓN DE PERFILES DE DISOLUCIÓNESTABLECER EQUIVALENCIA GLOBAL PARA 2 PERFILES DE DISOLUCIÓN UTILIZANDOEL MÉTODO MULTIVARIADO BASADO EN LA DISTANCIA ESTADÍSTICA MULTIVARIADA

se actualizase calcula

2 perfiles son equivalentes estadisticos si el producto de kpor DM al cuadrado son menores de F

region de confianza siexcede, no son eqquivalentes

Donde K:

promedio de pruebapromedio de referenciavectores de referenciavarianza de pruebavarianza de refereencia

se copian los datos de pruebamatriz x o y

matriz X transpuesta

DM=√ [ xP−xR ]TS pond

−1 [ xP−xR ]

CR=K×DM2≤FP,2 n−p−1,0.9

K=n2 (2n−p−1 )

2np (2n−2 )

53.98 51.06 54.85 67.84 53.51 53.07 54.3662.51 59.92 63.36 77.46 63.06 62.84 63.7589.15 89.71 90.60 103.98 89.84 89.39 89.5598.92 96.80 98.34 104.77 99.16 96.54 99.48

65.06 92.57 100.56 vector traspuesto de las medias aritmeticases importante pegar como valores

se selecciona el area para la matriz (4 tiemopos)4x4se digita =mmult( y se selecciona la matriz xt se digita , y se selecciona la matriz m1 de covarianza se cierra el parentesis y seoprimen al mismotiempo sift ctrl enter

varianzas al tiempo...

covarianzas

41.05 47.59 54.10 57.19 50.76 52.85 37.2553.44 62.45 67.15 77.44 66.15 78.62 51.4584.92 92.16 97.78 98.79 93.88 100.89 88.0089.87 94.16 99.72 100.77 95.75 101.65 91.67

65.25 94.16 96.48

matriz de corelacion parcial

CR=K×DM2≤FP,2 n−p−1,0.9

K=n2 (2n−p−1 )

2np (2n−2 )

2 perfiles son equivalentes estadisticos si el producto de kpor DM al cuadrado son menores de F

se digita , y se selecciona la matriz m1 de covarianza se cierra el parentesis y seoprimen al mismotiempo sift ctrl enter