Resolucion ejercicio 70

Ejercicio 70
[b]70) [/b]En promedio, los estudiantes de la Universidad A se levantan 50 minutos después de la salida del sol, con una desviación estándar de 15 minutos. Los estudiantes de la Universidad B se levantan 60 minutos después de la salida del sol, con una desviación estándar de 18 minutos. Un grupo de 25 estudiantes de la Universidad A realiza un viaje junto con 20 alumnos de la B. Encontrar la probabilidad de que la hora media de levantada del grupo de la Universidad B sea más temprana que la del grupo de la Universidad A.[br]Anotamos los datos e incógnitas del problema:[br]Calculamos la probabilidad solicitada.[br][b]Datos:[br][/b][b]-Universidad A:[br][/b]Media (μA​): 50 minutosDesviación estándar (σA​): 15 minutos[br]Tamaño de la muestra (nA​): 25 estudiantes[br][b]-Universidad B:[/b][br]Media (μB​): 60 minutos[br]Desviación estándar (σB​): 18 minutos[br]Tamaño de la muestra (nB​): 20 estudiantes[br][b]Calcular la media de las diferencias [/b][math]\left(\mu_{X_A-X_B}\right)[/math]Primero encontramos las varianzas de las medias muestrales:[math]\sigma^2_{x_A}=\frac{\sigma_A^2}{\eta_A}=\frac{15^2}{25}=\frac{225}{25}=9[/math][math]\sigma^2_{x_B}=\frac{\sigma_B^2}{\eta_B}=\frac{18^2}{20}=\frac{324}{20}=16.2[/math][br][br][b]La varianza de la diferencia de las medias muestrales es:[br][br][math]\sigma_{X_A-X_B}^2=\sigma^2_{X_A}_+\sigma_{X_B}^2=9+16.2=25.2[/math][br][br]La desviacion estándar de la diferencia es:[br][br][/b][b][math]\sigma_{_X_{_A-X_B}}=\sqrt{\sigma^{2_{_{X_A}-_{X_B}}}}=\sqrt{25.2}=5.02[/math][br][/b][br]Queremos encontrar la probabilidad de que la media de la Universidad B ([math]X_B[/math]) sea mas temprana que la Universidad A([math]_X_{_A}[/math]) es decir [math]_X_{_A-X_B}[/math]>0[br]Estandarizamos esta diferencia utilizando la distribucion normal estándar [math]Z[/math][br][math]Z=\frac{_X_{_A-X_{B^{-\mu_{X_A-X_B}}}}}{\sigma_{_X_{_A-X_B}}}=\frac{0-\left(-10\right)}{5.02}=\frac{10}{5.02}\approx1.99[/math][br][br]Encontramos la probabilidad correspondienteal valor [math]Z[/math][br]Utilizando la tabla de la distribución normal estándar, encontramos la probabilidad asociada con [math]Z=1.99[/math][br]La probabilidad acumulada para [math]Z=1.99[/math] es aproximadamente 0.9767.[br]Esto representa la probabilidad de que [math]_X_{_A-X_B}[/math]≤0.[br]Dado que buscamos la probabilidad de que [math]X_B[/math] sea mas temprana que [math]X_A[/math] que es [math]_X_{_A-X_B}[/math]>0[br][img]data:image/png;base64,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[/img][br][b]Respuesta: La probabilidad de que la hora media de levantarse del grupo de la Universidad B sea más temprana que la del grupo de la Universidad A es aproximadamente 0.0233 o 2.33%.[br][/b][br][b]Gráfica:[/b][br][img]data:image/png;base64,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[/img][br]

Information: Resolucion ejercicio 70