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# Referencias {-}
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---
title: "Fundamentos de Estadística con Remuestreo"
author: "Teresa Ortiz, Felipe González, Alfredo Garbuno"
site: bookdown::bookdown_site
documentclass: book
bibliography:
- bibs/book.bib
- bibs/packages.bib
biblio-style: apalike
link-citations: yes
github-repo: "tereom/fundamentos-2024"
description: Curso de Fundamentos de Estadística con Remuestreo, maestría en Ciencia
de Datos, ITAM, Otoño 2024.
url: "https://tereom.github.io/fundamentos-2024"
---

# Información del curso {.unnumbered}

Notas del curso *Fundamentos de Estadística con Remuestreo*, este curso busca explicar los principios básicos de la estadística y su papel en el análisis de datos. Nuestro punto de vista es uno de fundamentos, con menos énfasis en recetas o técnicas particulares.

#### Ligas {.unnumbered}

- Notas: <https://tereom.github.io/fundamentos-2024/>\
- Repositorio con material: <https://github.com/tereom/fundamentos-2024>\
- Correo: [teresa.ortiz.mancera\@gmail.com](mailto:[email protected])\
- Zoom clase (miércoles 4:00-7:00 pm): <https://itam.zoom.us/j/92745909276>\
- Zoom sesiones de dudas (viernes 3:00-3:45 pm): <https://itam.zoom.us/j/92518922348>\
- Canvas: <https://itam.instructure.com/courses/13328>\

</br>

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img src="https://i.creativecommons.org/l/by/4.0/88x31.png" alt="Licencia Creative Commons" style="border-width:0"/></a><br />Este trabajo está bajo una <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Licencia Creative Commons Atribución 4.0 Internacional</a>.

# Temario {-}

## Plan semanal {-}

#### Datos y análisis exploratorio {.unnumbered}

Referencias: [@cleveland94], [@Chihara]

1. Visualización[^index-1]

2. Análisis exploratorio

3. Tipos de datos o estudios
- Muestras diseñadas y muestras naturales
- Experimentos y datos observacionales

[^index-1]: Material disponible en apéndice.

#### Introducción a Pruebas de Hipótesis {.unnumbered}

Referencias: [@Chihara]

4. Introducción a pruebas de hipótesis. Pruebas de permutaciones
5. Muestras pareadas y otros ejemplos

#### Estimación y distribución de muestreo {.unnumbered}

Referencias: [@Chihara], [@Hesterberg]

6. Estimadores y su distribución de muestreo
7. Repaso de probabilidad y Teorema del límite central

#### Introducción a estimación por intervalos {.unnumbered}

Referencias: [@Chihara], [@Efron], [@Hesterberg]

8. El método plugin y el boostrap
9. Bootstrap e Intervalos de confianza. Ejemplos.

#### Estimación {.unnumbered}

Referencias: [@Chihara], [@Wasserman]

10. Estimación por máxima verosimilitud
11. Ejemplos de estimación por máxima verosimilitud y Bootstrap paramétrico
12. Propiedades de estimadores de máxima verosimilitud

#### Más de pruebas de hipótesis {.unnumbered}

Referencias: [@Chihara], [@Wasserman]

13. Pruebas de hipótesis para medias y proporciones: una y dos poblaciones.


#### Introducción a inferencia bayesiana {.unnumbered}

Referencias: [@Kruschke]

14. Introducción a inferencia bayesiana
15. Ejemplos de distribuciones conjugadas
16. Introducción a métodos computacionales básicos: Muestreadores Metrópolis y Gibbs
17.


## Evaluación {-}

Se evaluará mediante tareas semanales y dos exámenes:

* Tareas semanales (20%)
* Examen parcial en clase y a casa (40%)
* Examen final a casa (40%)


## Referencias

<div id="refs"></div>
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