Statistical Inference
Johns Hopkins University
Coursera
list 4 séquences
assignment Niveau : Introductif
chat_bubble_outline Langue : Anglais
language Sous titrage : Vietnamien
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Avis de la communauté
3.1
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184 avis

Les infos clés

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En résumé

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.

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Le programme

  • Week 1 - Week 1: Probability & Expected Values
    This week, we'll focus on the fundamentals including probability, random variables, expectations and more.
  • Week 2 - Week 2: Variability, Distribution, & Asymptotics
    We're going to tackle variability, distributions, limits, and confidence intervals.
  • Week 3 - Week: Intervals, Testing, & Pvalues
    We will be taking a look at intervals, testing, and pvalues in this lesson.
  • Week 4 - Week 4: Power, Bootstrapping, & Permutation Tests
    We will begin looking into power, bootstrapping, and permutation tests.
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Les intervenants

Brian Caffo, PhD
Professor, Biostatistics
Bloomberg School of Public Health

Roger D. Peng, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health

Jeff Leek, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health

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Le concepteur

The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
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La plateforme

Coursera est une entreprise numérique proposant des formations en ligne ouverte à tous fondée par les professeurs d'informatique Andrew Ng et Daphne Koller de l'université Stanford, située à Mountain View, Californie.

Ce qui la différencie le plus des autres plateformes MOOC, c'est qu'elle travaille qu'avec les meilleures universités et organisations mondiales et diffuse leurs contenus sur le web.

Avis de la communauté
3.1 /5 Moyenne
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Le meilleur avis

The course was at times difficult, I found that extra research was needed to fully understand what was going on. The extra questions related to the homework questions are a great way to test your understanding of the class.

le 2 mars 2018
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le 3 mars 2018
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If you have taken Statistics before it MAY help you move through this course and meet deadlines, or you will need to set aside a VERY large amount of time daily to learn Inferential Statistics and then come back and take this course. The forums are a NECESSARY supplement to understanding the details of the project assignments and quizzes. At times my questions have gone unanswered though, so YMMV. May the odds be ever in your favor.

le 2 mars 2018
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The course was at times difficult, I found that extra research was needed to fully understand what was going on. The extra questions related to the homework questions are a great way to test your understanding of the class.

le 28 février 2018
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The course is not meant for beginners, but seems to be advertised as such. Knowledge of Elementary Statistics is a must. The course is fast-paced and most people would not be able to finish it in 4 weeks or understand all the concepts in the course without outside help. Use of Discussion Forums and Mentors such as Leonard Greski is invaluable for completing the course successfully. There are several minor flaws in the videos and textbook that need to be addressed. This course would be much better off broken into two (Elementary + Inferential Statistics) and buffered with longer videos and step-by-step instruction and help.

le 28 février 2018
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The lectures are very theoretic with a few practical examples. The only way I could finish up the course and understand the

le 22 février 2018
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I found that the materials given or the lectures never allow you to clearly follow a structure. I understand that are so many contents to present, but jumping around from one to another is not the way.Quite frequently a lot of the slides are just useless. Not all of us have the time to go behind every mathematics, so I would like to see more real examples of how to use the contents you teach us, than knowing all the mathematics and have a lot of slides to show how to deduct mathematically the probability of something to happen. But might be my opinion because I had other expectations.