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Course Title

Statistical Inference

Course Instructor(s)

The primary instructor of this class is Brian Caffo

Brian is a professor at Johns Hopkins Biostatistics and co-directs the SMART working group

This class is co-taught by Roger Peng and Jeff Leek. In addition, Sean Kross and Nick Carchedi have been helping greatly.

Course Description

In this class students will learn the fundamentals of statistical inference. Students will receive a broad overview of the goals, assumptions and modes of performing statistical inference. Students will be able to perform inferential tasks in highly targeted settings and will be able to use the skills developed as a roadmap for more complex inferential challenges.

Course Content

This class is taught in three modules

  1. Probability and probability distributions
  2. Basics of inference
  3. More advanced inference techniques

Each module has sub modules, labeled such as 01_03. Videos within submodules are broken up so that 01_03_a is the first video in sub-module 3 in module 1 while 01_03_b is the second video.

For convenience we post the broken up videos, and then also the full videos for each sub-module on the site.

The full list of topics are as follows

Module 1, probability and probability distributions

  • 01_01 Introduction
  • 01_02 Probability
  • 01_03 Expectations
  • 01_04 Independence
  • 01_05 Conditional probability

Module 2, basics of inference

  • 02_01 Common Distributions
  • 02_02 Asymptopia
  • 02_03 t confidence intervals
  • 02_04 Likelihood
  • 02_05 Beginning Bayes Inference

Module 3, more advanced inference

  • 03_01 Independent group intervals
  • 03_02 Hypothesis testing
  • 03_03 P-values
  • 03_04 Power
  • 03_05 Multiple Testing
  • 03_06 resampled inference

Github repository

The most up to date information on the course lecture notes will always be in the Github repository

https://github.com/DataScienceSpecialization/courses

Please issue pull requests so that we may improve the materials.

Lecture Materials

Lecture videos will be released weekly and will be available for the week and thereafter. You are welcome to view them at your convenience. Accompanying each video lecture will be a PDF copy of the slides and a link to an HTML5 version of the slides.

The lecture videos are released in a weekly fashion. They do not correspond to the modules (as there's three modules and four weeks).

Weekly quizzes

The weekly quizzes will cover the material from that week.

Quiz 1

Assigned: Class open (1st of Month) Due: 7th of the Month 12:00 AM UTC

Quiz 2

Assigned: 8th of the Month 12:01 AM UTC Due: 14th of the Month 12:00 AM UTC

Quiz 3

Assigned: 15th of the Month 12:01 AM UTC Due: 21st of the Month 12:00 AM UTC

Quiz 4

Assigned: 22nd of the Month 12:01 AM UTC Due: 28th of the Month 12:00 AM UTC

Quiz Scoring

You may attempt each quiz up to 2 times. Only the score from your final attempt will count toward your grade.

Hard deadlines and soft deadlines

The reported due date is the soft deadline for each quiz. You may turn in quizzes up to two days after the soft deadline. The hard deadline is the Tuesday after the Quiz is due at 23:30 UTC-5:00. Each day late will incur a 10% penalty, but if you use a late day, the penalty will not be applied to that day.

Late Days for Quizzes

You are permitted 5 late days for quizzes in the course. If you use a late day, your quiz grade will not be affected.

Dates for the project

This class has no project unlike the other classes in the Data Science Series. (The content doesn't lend itself well to a project.) So be warned that there are more quiz questions here than in the other classes in the Data Science series.

Typos

  • We are prone to a typo or two - please report them and we will try
  • to update the notes accordingly. In some cases, the videos may
  • still contain typos that have been fixed in the lecture notes. The
  • lecture notes represent the most up-to-date version of the course
  • material.

Differences of opinion

Keep in mind that currently data analysis is as much art as it is science - so we may have a difference of opinion - and that is ok! Please refrain from angry, sarcastic, or abusive comments on the message boards. Our goal is to create a supportive community that helps the learning of all students, from the most advanced to those who are just seeing this material for the first time.

Technical Information

Regardless of your platform (Windows or Mac) you will need a high-speed Internet connection in order to watch the videos on the Coursera web site. It is possible to download the video files and watch them on your computer rather than stream them from Coursera and this may be preferable for some of you.

Here is some platform-specific information:

Windows

The Coursera web site seems to work best with either the Chrome or the Firefox web browsers. In particular, you may run into trouble if you use Internet Explorer. The Chrome and Firefox browsers can be downloaded from: _Chrome: http://www.google.com/chrome _ Firefox: http://www.mozilla.org

Mac

The Coursera site appears to work well with Safari, Chrome, or Firefox, so any of these browsers should be fine.