1. Introduction to Statistics

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2017-10-30に共有
*NOTE: This video was recorded in Fall 2017. The rest of the lectures were recorded in Fall 2016, but video of Lecture 1 was not available.

MIT 18.650 Statistics for Applications, Fall 2016
View the complete course: ocw.mit.edu/18-650F16
Instructor: Philippe Rigollet

In this lecture, Prof. Rigollet talked about the importance of the mathematical theory behind statistical methods and built a mathematical model to understand the accuracy of the statistical procedure.

License: Creative Commons BY-NC-SA
More information at ocw.mit.edu/terms
More courses at ocw.mit.edu/

コメント (21)
  • A statistics class focused on understanding statistics, not just scribbling equations. Very refreshing.
  • A good professor gets down to the level of a student and stops assuming things. A great professor gets down to the level of the weakest (new to the field) student in the class and makes them feel that they are not alone. And he is an excellent professor. Thank you MIT.
  • List of lectures (for some reason, lecture 10 doesn't exist so I re-ordered the numbering, which is why there's actually only 23 videos) Lecture 1: Part 1 - Intro to Statistics Lecture 2: Part 2 - Intro to Statistics Lecture 3: Parametric Inference Lecture 4: Parametric Inference and Likelihood of Estimation Lecture 5: Maximum of Likelihood Estimation Lecture 6: Maximum Likelihood of Estimation and Methods of Moments Lecture 7: Part 1 - Parametric Hypothesis Testing Lecture 8: Part 2 - Parametric Hypothesis Testing Lecture 9: Part 3 - Parametric Hypothesis Testing Lecture 10: Parametric Hypothesis Testing and Testing Goodness of Fit Lecture 11: Testing Goodness of Fit Lecture 12: Part 1 - Regression Lecture 14: Part 2 - Regression Lecture 15: Part 3 - Regression Lesson 16: Part 1 - Bayesian Statistics Lesson 17: Part 2 - Bayesian Statistics Lesson 18: Part 1 - Principal Component Analysis Lesson 19: Part 2 - Principal Component Analysis Lesson 20: Part 1 - Generalized Linear Models Lesson 21: Part 2 - Generalized Linear Models Lesson 22: Part 3 - Generalized Linear Models Lesson 23: Part 4 - Generalized Linear Models Good luck with your studies~
  • i am listening this course again because i am now PhD student. To refresh basics is a vital step. thank u MIT
  • "Statistics is about replacing expectations with averages. That is what all of statistics is about."
  • In bachelor class I fail in statistics unexpectedly. Now after 15 yrs I listen this like I am in university again. ❤
  • this guy knows his stuff. i enjoyed his class very much. Fair grader too, not tough, but fair.
  • i like the way he prepares to introduce a topic. very thoughtful and enjoyable.
  • @chTomokz
    I got moved and rocked. Thanks for uploading!! Watching from Tokyo, Japan. (In my shabby apartment room. I am a low income Tokyo city's resident.) As Dr.Regollet mentioned, Statistics is, z=a+b For me, Let, Live=Audiences+Performers also, z^=a^+b^ is an equation of a circle. Life^=Your heartbeat^ + My heartbeat^ Each square means that A heartbeat consists of movement of two ventricles. The kiss brings me to enter my body, mind and soul. This lecture has given me a new life. How wonderful! Thank you for inspiring me. I've so refreshed!
  • @mkos1111
    Awesome video. Using it to get ahead for fall! Thank you for providing this to the public. In the age of the internet, knowledge is as cheap as the wifi!
  • 10:20 prerequisites 14:50 explanation of the scientific process a bit before this. On the collection of data --> hypothesis, which will then be proven or disproven by more data
  • @nomad4k
    I am extremely grateful for this entire series. Thank you. I keep revisiting every time I forget a concept or need to brush up. Having taught in a classroom myself, and having gotten many thanks and compliments from my students over the years, I can tell that he has that same attitude that I learned to embrace - making sure that students understand the concepts and do not just blindly memorize equations. I used to teach computer science (data structures, algorithms and oop languages) at Steven’s inst. in Hoboken.
  • "Randomness is a big rug in which we sweep everything, we don't understand." Leap of faith. 51:12 - because Real-time observation feels better.
  • Loved that you captured your audience with the "kissing Statue" in relation to stats..... very engaging :) since statistics in a Phd program is so painful lol
  • This course is excellent, so much needed in the modern world.
  • I took stats last year, it was tough! Luckily my school used Lumen which helped a lot! It had practice problems so you can know what you do wrong, so u can go back and work on it before going on to your official home work. I was able to get an A but it was tough and had to put a lot of extra time aside dedicated just to my homework etc because i am very very bad at math. The lumen program really helped a lot
  • This course should be the standard for every statistics across all levels in education. My current statistics course in community colleg is structured ineffectively and goes straight into the math. While this applies a lot more real word situations & lore that makes the class exciting.
  • I wish this was available when I was an undergrad. I would have came to OCW for every single friggin course. But glad its available now, certainly helps with grad school.