Korrik 11, 2023
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Mishkat Al Masabih (Arabic: مشكاة المصابيح, lit. 'The Niche of Lanterns') is an expanded and revised version of an earlier book of hadiths named Masabih as-Sunnah by Imam al-Baghawi. Imam Khatib at-Tabrizi added more hadiths, reclassified some of them, and made the book more accessible to those who are not experts in the science of hadith. He divided the book into 29 chapters, each dealing with a specific topic, such as faith, knowledge, purification, prayer, zakat, fasting, pilgrimage, marriage, jihad, etc.
Mishkat Al Masabih is considered to be one of the most important and authentic collections of hadiths by Sunni Muslims. It contains hadiths from the Six Books (Sahih al-Bukhari, Sahih Muslim, Sunan Abu Dawood, Sunan al-Nasa'i, Sunan Ibn Majah, and Jami' al-Tirmidhi), as well as from Musnad Ahmad and other sources. It also includes some hadiths that are not found in other books.
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Mishkat Al Masabih Urdu is a translation of Mishkat Al Masabih Arabic into Urdu language. Urdu is a widely spoken language in Pakistan, India, and other countries. Many Muslims who are not fluent in Arabic may prefer to read Mishkat Al Masabih Urdu to understand the hadiths better.
Mishkat Al Masabih Urdu was translated by Abu Anas Muhammad Sarwar Gohar and reviewed by Shaqeq Al-Rahman Deya’ Allah Al-Madani and Nasser Mahmoud Anwar. The translation is based on the original text of Imam Khatib at-Tabrizi and follows the same structure and division of chapters. The translation also includes the grading and commentary of the hadiths by Hafiz Zubair Ali Zai, a renowned scholar of hadith.
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Mishkat Al Masabih English is a translation of Mishkat Al Masabih Arabic into English language. English is a widely spoken and understood language in the world. Many Muslims who are not fluent in Arabic may prefer to read Mishkat Al Masabih English to understand the hadiths better.
Mishkat Al Masabih English was translated by James Robson and published by Sh. Muhammad Ashraf Publishers in Lahore, Pakistan. The translation is based on the original text of Imam Khatib at-Tabrizi and follows the same structure and division of chapters. The translation also includes some footnotes and references to explain the hadiths.
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Mishkat Al Masabih is a valuable and comprehensive book of hadiths that contains the words and deeds of Prophet Muhammad (peace be upon him) and his companions. It covers almost all aspects of Islamic belief, jurisprudence, and virtues. It is a source of guidance and wisdom for Muslims of all times and places.
If you want to read Mishkat Al Masabih, you can download it in Arabic, Urdu, or English language as a PDF file. This can help you to access the book easily and conveniently on your device. However, you should also be careful and responsible when downloading or sharing PDF files. You should also check the authenticity and reliability of the hadiths and consult other sources or scholars if needed.
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¿Te gustaría aprender sobre el muestreo estadístico y sus aplicaciones en diferentes campos? ¿Quieres practicar y mejorar tus habilidades con ejercicios resueltos y explicados paso a paso? Entonces, este artículo es para ti.
En este artículo, te presentamos algunos de los ejercicios resueltos del libro de niebel muestreo de 374, una obra clásica y reconocida en el campo de la estadística, que aborda temas como el diseño de muestras, los métodos de estimación, el análisis de errores, las pruebas de hipótesis y la inferencia bayesiana.
El libro de niebel muestreo de 374 contiene más de 400 ejercicios, con soluciones detalladas y explicadas paso a paso, que te ayudarán a entender y aplicar el muestreo estadístico en diferentes contextos y situaciones. Estos ejercicios te servirán para reforzar tus conocimientos sobre el tema, así como para prepararte para exámenes o proyectos académicos o profesionales.
En este artículo, vamos a resolver algunos de los ejercicios más relevantes y útiles del libro de niebel muestreo de 374, que abarcan diferentes tipos de muestreo y técnicas de estimación. Estos ejercicios te permitirán aprender a seleccionar muestras representativas, calcular estimadores puntuales e intervalos de confianza, realizar pruebas de hipótesis y hacer inferencias sobre la población.
El primer ejercicio que vamos a resolver es el siguiente:
Se desea estimar la proporción de personas que aprueban un examen en una población de 10.000 estudiantes. Para ello, se selecciona una muestra aleatoria simple de 100 estudiantes y se observa que 60 de ellos aprueban el examen. Calcular el estimador puntual y el intervalo de confianza al 95% para la proporción poblacional.
Para resolver este ejercicio, debemos aplicar la fórmula del estimador puntual y del intervalo de confianza para una proporción en una muestra aleatoria simple, que son las siguientes:
\hat{p} = \frac{x}{n}
\hat{p} \pm z_{\alpha/2} \sqrt{\frac{\hat{p}(1-\hat{p})}{n}}
Donde \hat{p} es el estimador puntual, x es el número de éxitos en la muestra, n es el tamaño de la muestra, z_{\alpha/2} es el valor crítico de la distribución normal estándar para un nivel de confianza dado (1-\alpha), y \alpha es el nivel de significancia.
Sustituyendo los datos del problema en las fórmulas anteriores, obtenemos:
\hat{p} = \frac{60}{100} = 0.6
\hat{p} \pm z_{0.025} \sqrt{\frac{0.6(1-0.6)}{100}}
\hat{p} \pm 1.96 \sqrt{\frac{0.24}{100}}
\hat{p} \pm 0.096
Por lo tanto, el estimador puntual es 0.6 y el intervalo de confianza al 95% es (0.504, 0.696). Esto significa que podemos afirmar con un 95% de confianza que la proporción real de personas que aprueban el examen en la población está entre el
El segundo ejercicio que vamos a resolver es el siguiente:
Se desea estimar el ingreso medio de una población de 10.000 familias. Para ello, se divide la población en cuatro estratos según el nivel de educación del jefe de familia: primaria, secundaria, bachillerato y universitario. Se sabe que los tamaños de los estratos son 3000, 4000, 2000 y 1000 respectivamente, y que las varianzas de los ingresos dentro de cada estrato son 400, 900, 1600 y 2500 respectivamente. Se decide tomar una muestra proporcional al tamaño de cada estrato, con un total de 100 familias. Calcular el estimador puntual y el intervalo de confianza al 95% para el ingreso medio poblacional.
Para resolver este ejercicio, debemos aplicar la fórmula del estimador puntual y del intervalo de confianza para una media en una muestra estratificada, que son las siguientes:
\bar{y}_{str} = \sum_{h=1}^{H} W_h \bar{y}_h
\bar{y}_{str} \pm z_{\alpha/2} \sqrt{\sum_{h=1}^{H} W_h^2 \frac{s_h^2}{n_h}}
Donde \bar{y}_{str} es el estimador puntual, H es el número de estratos, W_h es el peso o la proporción del estrato h en la población, \bar{y}_h es la media muestral del estrato h, s_h^2 es la varianza muestral del estrato h, n_h es el tamaño muestral del estrato h, z_{\alpha/2} es el valor crítico de la distribución normal estándar para un nivel de confianza dado (1-\alpha), y \alpha es el nivel de significancia.
Sustituyendo los datos del problema en las fórmulas anteriores, obtenemos:
\bar{y}_{str} = \sum_{h=1}^{4} W_h \bar{y}_h
\bar{y}_{str} \pm z_{0.025} \sqrt{\sum_{h=1}^{4} W_h^2 \frac{s_h^2}{n_h}}
Para calcular el estimador puntual, necesitamos conocer las medias muestrales de cada estrato. Supongamos que se obtienen los siguientes valores:
\bar{y}_1 = 500
\bar{y}_2 = 800
\bar{y}_3 = 1200
\bar{y}_4 = 1800
Entonces, el estimador puntual es:
\bar{y}_{str} = (0.3)(500) + (0.4)(800) + (0.2)(1200) + (0.1)(1800)
\bar{y}_{str} = 150 + 320 + 240 + 180
\bar{y}_{str} = 890
Para calcular el intervalo de confianza, necesitamos conocer los tamaños muestrales y las varianzas muestrales de cada estrato. Supongamos que se obtienen los siguientes valores:
n_1 = 30
n_2 = 40
n_3 = 20
n_4 = 10
s_1^2 = 400
s_2^2 = 900
s_3^2 = 1600
s_4^2 = 2500
Entonces, el intervalo de confianza es:
\bar{y}_{str} \pm z_{0.025} \sqrt{\sum_{h=1}^{4} W_h^2 \frac{s_h^2}{n_h}}
\bar{y}_{str} \pm 1.96 \sqrt{(0.3)^2 \frac{400}{30} + (0.4)^2 \frac{900}{40} + (0.2)^2 \frac{1600}{20} + (0.1)^2 \frac{2500}{10}}
\bar{y}_{str} \pm 1.96 \sqrt{12 + 36 + 32 + 25}
\bar{y}_{str} \pm 1.96 \sqrt{105}
\bar{y}_{str} \pm 20.08
Por lo tanto, el intervalo de confianza al 95% es (869.92, 910.08). Esto significa que podemos afirmar con un 95% de confianza que el ingreso medio real de las familias en la población está entre 869.92 y 910.08.
El tercer ejercicio que vamos a resolver es el siguiente:
Se desea estimar el número de personas que practican algún deporte en una ciudad de 100.000 habitantes. Para ello, se divide la ciudad en 100 sectores de 1000 habitantes cada uno, y se selecciona una muestra aleatoria simple de 10 sectores. Dentro de cada sector seleccionado, se entrevista a todas las personas y se les pregunta si practican algún deporte. Se obtienen los siguientes resultados:
Sector Número de personas entrevistadas Número de personas que practican algún deporte 1 1000 300 2 1000 250 3 1000 400 4 1000 350 5 1000 200 6 1000 450 7 1000 300 8 1000 500 9 1000 150 10 1000 350 Total 10.000 3250 Calcular el estimador puntual y el intervalo de confianza al 95% para el número total de personas que practican algún deporte en la ciudad.
Para resolver este ejercicio, debemos aplicar la fórmula del estimador puntual y del intervalo de confianza para una total en una muestra por conglomerados, que son las siguientes:
T_{clu} = N \bar{t}_c
T_{clu} \pm z_{\alpha/2} N \sqrt{\frac{s_c^2}{n_c}}
Donde T_{clu} es el estimador puntual, N es el número total de conglomerados en la población, \bar{t}_c es la media muestral de los totales de cada conglomerado, s_c^2 es la varianza muestral de los totales de cada conglomerado, n_c es el número de conglomerados en la muestra, z_{\alpha/2} es el valor crítico de la distribución normal estándar para un nivel de confianza dado (1-\alpha), y \alpha es el nivel de significancia.
Sustituyendo los datos del problema en las fórmulas anteriores, obtenemos:
T_{clu} = 100 \bar{t}_c
T_{clu} \pm z_{0.025} 100 \sqrt{\frac{s_c^2}{10}}
Para calcular el estimador puntual, necesitamos conocer la media muestral de los totales de cada conglomerado. Para ello, sumamos el número de personas que practican algún deporte en cada sector seleccionado, y luego dividimos entre el número de sectores. Obtenemos:
\bar{t}_c = \frac{300 + 250 + 400 + 350 + 200 + 450 + 300 + 500 + 150 + 350}{10}
\bar{t}_c = \frac{3250}{10}
\bar{t}_c = 325
Entonces, el estimador puntual es:
T_{clu} = 100 (325)
T_{clu} = 32500
Para calcular el intervalo de confianza, necesitamos conocer la varianza muestral de los totales de cada conglomerado. Para ello, restamos la media muestral de los totales a cada total observado, elevamos al cuadrado, sumamos y dividimos entre el número de sectores menos uno. Obtenemos:
s_c^2 = \frac{(300 - 325)^2 + (250 - 325)^2 + (400 - 325)^2 + (350 - 325)^2 + (200 - 325)^2 + (450 - 325)^2 + (300 - 325)^2 + (500 - 325)^2 + (150 - 325)^2 + (350 - 325)^2}{10 - 1}
s_c^2 = \frac{625 + 5625 + 5625 + 625 + 15625 + 15625 + 625 + 30625 + 30625 + 625}{9}
s_c^2 = \frac{112500}{9}
s_c^2 = 12500
Entonces, el intervalo de confianza es:
T_{clu} \pm z_{0.025} 100 \sqrt{\frac{s_c^2}{10}}
T_{clu} \pm 1.96 (100) \sqrt{\frac{12500}{10}}
T_{clu} \pm 196 \sqrt{1250}
T_{clu} \pm
\bar{y}_{sys} \pm 24.5
Por lo tanto, el intervalo de confianza al 95% es (78, 127). Esto significa que podemos afirmar con un 95% de confianza que el peso medio real de los cerdos en la población está entre 78 y 127 kg.
El quinto ejercicio que vamos a resolver es el siguiente:
Se desea estimar el número de libros que hay en una biblioteca pública. Para ello, se divide la biblioteca en 50 secciones, y se selecciona una muestra aleatoria simple de 10 secciones. Dentro de cada sección seleccionada, se divide en 20 estanterías, y se selecciona una muestra aleatoria simple de 5 estanterías. Dentro de cada estantería seleccionada, se cuenta el número de libros que hay. Se obtienen los siguientes resultados:
Sección Estantería Número de libros 1 1 50 1 2 60 1 3 40 1 4 55 1 5 45 ... Total: 12500 libros Media: 50 libros por estantería Varianza: 100 libros^2 por estantería^2 Calcular el estimador puntual y el intervalo de confianza al 95% para el número total de libros en la biblioteca.
Para resolver este ejercicio, debemos aplicar la fórmula del estimador puntual y del intervalo de confianza para un total en una muestra por etapas, que son las siguientes:
T_{sta} = N_1 \bar{t}_s
T_{sta} \pm z_{\alpha/2} N_1 \sqrt{\frac{s_s^2}{n_s} + \frac{\bar{s}_e^2}{n_e}}
Donde T_{sta} es el estimador puntual, N_1 es el número total de unidades primarias en la población (secciones), \bar{t}_s es la media muestral de los totales de cada unidad primaria (sección), s_s^2 es la varianza muestral de los totales de cada unidad primaria (sección), n_s es el número de unidades primarias en la muestra (secciones), \bar{s}_e^2 es la media muestral de las varianzas de cada unidad secundaria (estantería) dentro de cada unidad primaria (sección), n_e es el número de unidades secundarias en la muestra dentro de cada unidad primaria (estanterías), z_{\alpha/2} es el valor crítico de la distribución normal estándar para un nivel de confianza dado (1-\alpha), y \alpha es el nivel de significancia.
Sustituyendo los datos del problema en las fórmulas anteriores, obtenemos:
En este artículo, hemos resuelto algunos de los ejercicios del libro de niebel muestreo de 374, que nos han permitido aprender y practicar diferentes tipos de muestreo y técnicas de estimación. Hemos visto cómo aplicar las fórmulas adecuadas para calcular estimadores puntuales e intervalos de confianza para proporciones, medias y totales en muestras aleatorias simples, estratificadas, por conglomerados y por etapas. Hemos comprobado que el muestreo estadístico es una herramienta muy útil y poderosa para hacer inferencias sobre una población a partir de una muestra representativa.
Esperamos que este artículo te haya sido de utilidad y que hayas disfrutado resolviendo los ejercicios. Si quieres aprender más sobre el muestreo estadístico y sus aplicaciones, te recomendamos que consultes el libro de niebel muestreo de 374, donde encontrarás más ejercicios y soluciones, así como explicaciones teóricas y ejemplos prácticos. También puedes visitar nuestra página web, donde encontrarás más recursos y artículos sobre estadística y otros temas de interés.
Gracias por leer este artículo y hasta la próxima.
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Film Need for Speed subtitle Indonesia Ganool Indonesia is a 2014 American action film directed by Scott Waugh and written by George Gatins. It stars Aaron Paul as Tobey Marshall, a street racer who joins a cross-country race to avenge his friend's death at the hands of his former partner, Dino Brewster (Dominic Cooper). Along the way, he meets Julia Maddon (Imogen Poots), a car broker who becomes his ally and love interest.
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Nvidia Iray Server is a distributed rendering solution that allows you to use multiple GPUs and CPUs across your network or cloud to speed up your rendering process and reduce your waiting time. It also integrates seamlessly with popular 3D applications such as Autodesk Maya, 3ds Max, Cinema 4D and more.
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Nvidia Iray Server v2.5 is a significant improvement over the previous version in terms of performance, quality and functionality. Here are some of the main differences between Nvidia Iray Server v2.5 and the previous version:
Feature | Nvidia Iray Server v2.4 | Nvidia Iray Server v2.5 |
---|---|---|
GPU Support | Pascal, Volta, Turing | Pascal, Volta, Turing, RTX |
Material Support | Iray Material Node (IMN) | Iray Material Node (IMN), MDL |
Denoising Support | No | Yes |
Cloud Rendering Support | No | Yes |
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Theatre of War 2: Africa 1943 is a game that will satisfy your craving for a realistic and challenging WWII strategy game. The game lets you relive the historical battles of North Africa, where you can command various units and vehicles from different nations and factions. The game also gives you the freedom to choose your own strategy and tactics, as well as to influence the outcome of the war with your actions and decisions.
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An activation key is a unique code that verifies your ownership of the game and allows you to install and run it on your device. An activation key is usually composed of letters and numbers, such as XXXX-XXXX-XXXX-XXXX. An activation key is also known as a product key, a serial key, or a CD key.
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