al-aly / statistical-inference-coursera Goto Github PK

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 The course is taught via 13 lectures

Introduction Probability Conditional Probability Expectations Variance Common Distributions Asymptotics T confidence intervals Hypothesis testing P-values Power Multiple Testing Resampling These new lectures reduce quite a bit of content over older versions of the class. However, the older full lectures are included as a courtesy.

They have a different naming convention and are quite a bit more detailed. They are not necessary to complete the course.

01_01 Introduction 01_02 Probability 01_03 Expectations 01_04 Independence 01_05 Conditional probability 02_01 Common Distributions 02_02 Asymptopia 02_03 t confidence intervals 02_04 Likelihood 02_05 Beginning Bayes 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).

Assessments and Grading Policy Quiz 1 = 15% Quiz 2 = 15% Quiz 3 = 15% Quiz 4 = 15% Course Project = 40% 70% or more of the total points is a pass for the class. 90% or more of the total points is a pass with distinction.

Weekly quizzes The weekly quizzes will cover the material from that week. The quizzes don't always exactly correspond to the material for that week. However, the material is always covered before the quiz is due. Here's the rough makeup

Quiz 1 covers lectures 1 - 4 Quiz 2 covers lectures 5-7 Quiz 3 covers lectures 8 - 10 Quiz 4 covers lectures 8 - 13 Thus, Quiz 3 and 4 cover a lot of overlapping material. Course Project The Course Project is an opportunity to demonstrate the skills you have learned during the course. It is graded through peer assessment.

Details of the Course Project are available from the first day of the course session, and your work will be due BEFORE 11:30 PM UTC on the Sunday at the end of Week 3. The deadline for Course Project submission is absolutely firm, and Late Days MAY NOT be used for the Course Project.

To access the Course Project interface, click the Course Project link in the left navigation bar.

After the submission window closes, the evaluation phase will open. During the evaluation phase, you will evaluate and grade at least four submissions from your classmates. All four evaluations are due BEFORE 11:30 PM UTC on the Sunday at the end of Week 4. If you don't complete all four evaluations by the end of the evaluation phase, your own Course Project score will be reduced by 20%.

Quiz timing

The class always starts on a Monday and last four weeks. There are four quizzes, one for each week. All quizzes are released on Day 1 of the course Quizzes are due weekly at 11:30 PM UTC on Sundays (Quiz 1 is due on Sunday at the end of Week 1, Quiz 2 is due on Sunday at the end of Week 2, etc.) Go to the quiz itself for the exact times. Quiz Scoring You may attempt each quiz up to 3 times. The score from your most successful attempt will count toward your final grade.

Homework There is some optional homework that can be accessed here. More information can be found in the navbar homework tab. These exercises are optional and perhaps a little more difficult than the quizzes. They also may not exactly correspond to the quiz and lecture schedules.

http://bcaffo.github.io/courses/06_StatisticalInference/homework/hw1.html#1 http://bcaffo.github.io/courses/06_StatisticalInference/homework/hw2.html#1 http://bcaffo.github.io/courses/06_StatisticalInference/homework/hw3.html#1 http://bcaffo.github.io/courses/06_StatisticalInference/homework/hw4.html#1

Hard deadlines and soft deadlines The reported due date is the soft deadline for each quiz. You may turn in quizzes for partial credit up to five days after the soft deadline. You will incur a 10% penalty for every day after the soft deadline, but if you use a late day, the penalty will not be applied to that day. The hard deadline is the Friday after the Quiz is due at 23:30 UTC. If you submit your quiz after the hard deadline passes, you will not receive any credit.

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.

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

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