[b]Datos del enunciado.-[br][/b]Fábrica A: Proporción de defectuosas: [i]p[/i][i]A[/i]=0.17[br]Fábrica B: Proporción de defectuosas:[i] p[/i][i]B[/i]=0.15[br]Tamaño de muestra en cada fábrica: [i]n[/i]=200[br]Queremos calcular la probabilidad de que la diferencia muestral en proporciones defectuosas sea mayor a 3%, es decir: [i]P[/i](∣[i]p[/i]^[i]A[/i]−[i]p[/i]^[i]B[/i]∣>0.03)[br][b][br]1. Distribución de la diferencia entre proporciones muestrales.-[/b][br]Distribuciones de las proporciones muestrales 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[/img][br][br]Y la diferencia:[img]data:image/png;base64,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[/img][br][br]Tiene una distribución aproximadamente normal (por el teorema del límite central), con: [img]data:image/png;base64,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[/img][br]Calculamos las 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[/img][br][br][br]Por lo tanto, la varianza conjunta:[br]Var([i]D[/i])≈0.0007055+0.0006375=0.001343[br][br]Y la desviación estándar:[br][i]σ[/i][i]D[/i]=0.001343=0.0366[br][br][b]2. Calcular la probabilidad solicitada.-[br][br][/b]Queremos calcular: [i]P[/i](∣[i]D[/i]−0.02∣>0.03)[br]Esto equivale a: [i]P[/i]([i]D[/i]<0.02−0.03)+[i]P[/i]([i]D[/i]>0.02+0.03)=[i]P[/i]([i]D[/i]<−0.01)+[i]P[/i]([i]D[/i]>0.05)[br][br]Convertimos a la variable normal estándar:[br][br][img]data:image/png;base64,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[/img][br][br][br]Para [i]D[/i]<−0.01:[br][img]data:image/png;base64,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[/img][br][br][br]Para [i]D[/i]>0.05:[br][img]data:image/png;base64,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[/img][br][br]Por la simetría de la distribución normal:[br][i]P[/i](∣[i]D[/i]−0.02∣>0.03)≈[i]P[/i]([i]Z[/i]<−0.82)+[i]P[/i]([i]Z[/i]>0.82)[br][br]Usando tablas de la normal estándar:[br][i]P[/i]([i]Z[/i]<−0.82)=0.2061[i]P[/i]([i]Z[/i]>0.82)=0.2061[br][br]Por lo Tanto:[br][i]P[/i](∣[i]D[/i]−0.02∣>0.03)=0.2061+0.2061=0.4122[b][br][br]Respuesta.- [/b]La probabilidad de que las dos muestras revelen una diferencia superior al 3% es aproximadamente [b]41.22%[/b].[br][br]