Amazing!

good one

awesome

amazing

5-star!

Great

Cool

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O

Some parts are needed more clarification. In other words, as a student of the course you need to go beyond the materials, since the materials are not self-sufficient. Specially about simulation methods. However, this is not the reason that I give the course 4 out of 5. The absence of any help from TAs, based on my experience, is the reason. I expected some official replies to my question while there are only a few question for each week of the course.

Great material although I will like to know more about the practical side of statistical inference. For instance, I have more of less an idea of how to use chi-squared test with categorical variables in a dataset however, for the other statistical inference methods such as p-values and confidence intervals I still don't see where can I use this methods when doing data analysis. Can we use this to detect outliers in our dataset for instance?

The course is well designed, and the examples given in each lesson are informative and interesting.

For the final project, I wanted to group some categories from one variable together in a new variable, but I did not have the code I needed to do it. It would have been very helpful to have that information in one of the labs prior to doing the final project.

Thorough treatment of the topics with great examples using real data. On the down side, the treatment of the mathematics behind the formulas is a little light. Great use of simulation to support the theory or to use when theoretical assumptions are not met. Strongly recommended!

I loved this course. As with the previous course a lot of things that weren't clear to me before are now. I totally recommend it to anybody new to statistics or anybody who is struggling with statistics (like I have for a very long time).

Very good material which gives practical knowledge supported by interesting examples. The only concern is that it is slightly shallow - lacking some mathematical justification for the given "rules of thumb" and theorems.

Very good introductory course for inferential statistics. It is wise to complete the first course Introduction to Probability and Data of this specialization before enrolling into this one to grasp the concepts.

This course seemed unbalanced compared to some of the other courses and was very work-heavy. I felt it could have, or maybe should have been broken up into two courses, or that other courses should be longer.

The course is very well explained I had to refer other materials for ANOVA technique to understand it better hence that part can be either improved OR more reference material be provided

Very useful course about statistics. May need some fundamental understanding of statistics before, but through the clear explanation and examples, I've learnt a lot from this course

The teaching is good, the course is a little heavy and a lot to take in in the later weeks. But, as a further grounding for statistics and R, I would very much recommend it.

It is a very nice course, I have learned a lot. However, it is convenient to take the previous one of the specialization, as they base some examples or R knowledge on it.

This course gave me the toughest time of my life. I did the course for 3 months, i failed project once but i thank God, i proved toughest by passing with 100%.

The inference function and hypotheses tests are really useful. Permutation tests need more explaining and examples; otherwise they should not be included.

A good course for learning statistical inference, though I found that more than a week per module was required to really absorb the content.

It is a great course, while some underlying logics are not clearly explained. And the quiz has some unexplained context, which is confused.