关键信息
关于内容
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.
课程大纲
- 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.
教师
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
内容设计师
平台
Coursera是一家数字公司，提供由位于加利福尼亚州山景城的计算机教师Andrew Ng和达芙妮科勒斯坦福大学创建的大型开放式在线课程。
Coursera与顶尖大学和组织合作，在线提供一些课程，并提供许多科目的课程，包括：物理，工程，人文，医学，生物学，社会科学，数学，商业，计算机科学，数字营销，数据科学 和其他科目。
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.
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.
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.
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.
The lectures are very theoretic with a few practical examples. The only way I could finish up the course and understand the
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.