Course: Statistical Inference

  • Course Home
  • Syllabus
  • Lecture Materials
  • Want to stay in touch?





    2 + 1 =  

Syllabus

Except where otherwise indicated, this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike License . Your use of this material constitutes acceptance of that license and the conditions of use of materials on this site.

To sign up to take the course online, please visit the Johns Hopkins Data Science Specialization.

Course Description

n 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.

Schedule

The course is taught via 13 lectures

    1. Introduction
    2. Probability
    3. Conditional Probability
    4. Expectations
    5. Variance
    6. Common Distributions
    7. Asymptotics
    8. T confidence intervals
    9. Hypothesis testing
    10. P-values
    11. Power
    12. Multiple Testing
    13. Resampling

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.

Quiz 1 covers lectures 1-4
Quiz 2 covers lectures 5-7
Quiz 3 covers lectures 8-10
Quiz 4 covers lectures 8-13

Quizzes 3 and 4 cover overlapping material.

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.

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.
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%.