# 学生对 约翰霍普金斯大学 提供的 统计推断 的评价和反馈

4.2
4,217 个评分
853 条评论

## 课程概述

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....

## 热门审阅

JA
Oct 25, 2018

Course is compressed with lots of statistical concepts. Which is very good as most must know concepts are imparted. Lots of extra reading is required to gain all insights. Very good motivating start .

MI
Sep 24, 2020

the teachers were awesome in this course. I liked this course a lot.Understood it properly.Thanks to all the beloved teachers and mentors who toiled hard to make these course easy to handle.Gracious!

## 601 - 统计推断 的 625 个评论（共 820 个）

Sep 21, 2020

So in my opinion information that ws given in this course was not exlained well, lucky for me I was just refreshing these things, so I knew where I could lok for additional explanation. But if you don't have any background in this scope, it would be rather hard to understand why given formulas are working.

Feb 4, 2019

The course contains a lot I want to learn, but as someone with a limited background in statistics - I found many of the lectures not to provide clear explanations for concepts. I had to use a lot of outside material to try to learn and understand the concepts. The course lectures seem incomplete to me.

Dec 28, 2017

The class contents are good I guess. But I don't think the professor knew how to teach and enjoyed the teaching process. Based on my experience, all the concepts are not that hard for everyone if they would be explained in a good way. I finished this course only because I want to do the course capstone.

Jul 15, 2017

A good class, but I think there are some missing pieces. For example, there was a lecture on the basics of knitr, but nothing related to creating a pdf from R. In the Regression Models class there is a lecture on basic notation. I think it would have been more helpful to have that lecture in this class.

Mar 23, 2018

I really learned a lot in this course, but I find that I got most out of the lectures in week 3/4 when Brian actually stopped reading the slides out loud and explained the concepts i his own words. I believe the course could be improved by taking that approach in the other weeks

Dec 13, 2017

The lectures are really hard to understand, while the material itself is really not that hard. The lecturer talks as if he is just reminding us everything we've already learned. Had to go to other MOOC (specifically Khan Academy) to obtain proper understanding of the topic.

Mar 7, 2016

The course was very dry compared to the other courses I have taken. Though there was a lot to cover in the four weeks but this was not best way to do it. The course covers a lot of concepts in far too little a time span. It should have been spread into at-least two modules.

Apr 11, 2017

To tackle such key concepts and tools of statistics, you need the appropriate time. Too much material covered in this course. I tend to think that revising the approach to this course as two 4 weeks modules would allow to both go more in depth at a more appropriate pace.

Jun 20, 2017

ok course. They provide examples that make sense, although assignments don't really touch all of the material covered. The examples as well as assignments tend to be quite helpful, although I dislike how they force the specific format, which for some could be advantage.

Dec 24, 2020

Course is reasonably well taught. I have gotten used to stale online courses. You would think that, given the apparent work to correct errors or respond to feedback should be minimal compared to the vast number of students taking this course. Not so much, I guess.

Feb 28, 2020

This is a decent overview of statistical inference techniques. Make sure you understand each lecture before moving on to the next since they build on each other. The lecture notes are decent but not great. I found it cleaner and easier to take my own notes.

Aug 6, 2019

This course was better than the others in the program because there was much less of a gap between the lectures and the graded sections in terms of expectations. Still, I knew this material going in and would not recommend this as a way to learn it.

Jan 3, 2017

It felt like there were a lot of jumps between basic statistical formulae and abstractions thereof. While I don't think it was inappropriate for a course on statistics in itself, it felt rather out of place in the rest of the sequence of this course.

Feb 17, 2021

I have taken almost 10 classes from Coursera and this is the first one that I was not pleased with. I had to do much additional work studying examples and explanations on the Internet because those given by the instructor were not asdequate.

Nov 28, 2016

The sections on bootstrapping and permutations were great! The instructor does much better, information is easier to follow (better and slower explanations), and the instructor is more engaging when he is not reading from his notes.

Dec 20, 2019

The content of the course is really good and so the practices. But the teacher does not know how to explain things and easy subjects are transformed into a difficult ones. I had to study other books to really understand the subject

Mar 30, 2020

Although the topic was very interesting, the way of teaching was troublesome. Teacher spoke often in a way as if he talked to specialists. So it was often hard to understand, and for understanding I needed to consult other sources

Feb 15, 2018

The subject of the course is very interesting and the professor is very competent. I had the feeling that some subjects were explained in a way that is not very convenient for someone coming from a non-statistical background.

Mar 28, 2016

The material in the class is solid, but is poorly described. These are the foundations of statistical analysis, and unfortunately there's a lot of statistics jargon that students aren't going to be familiar with in here.

Nov 28, 2016

Nicely outlined and broad in scope, but Brian's presentation is kind of dry. It often appears that he is reading off a script, and sometimes his emphasis on technical details takes away from ease of understanding.

Mar 1, 2016

I think the theory is too dense, but with a weak link with R. I understood better with swirl than with the videos. I'd suggest a more organized video with less draws and annotations. They confused me sometimes.

Mar 31, 2018

Statistics & Probability being two of the toughest subjects, this course could have been taught a bit more novice friendly way, so that learners with no background in maths can also grab the lectures easily

Sep 29, 2017

I had some difficult to follow the lessons, because the professor is kind of reading the material and not building the concepts during class time. I had to look for other videos and texts out of coursera.

Feb 11, 2016

As someone who isn't from math background many of concepts thought in here weren't quiet clear or intuitive. Could use more details or pointers to reading materials to help understand the concepts better

Jun 4, 2020

Disappointed with the way the course has been taught. The instructor just reads out from the slides. Had to refer lot of other material to understand the topics being taught in this course.