Chevron Left
Zurück zu Inferenzstatistik

Bewertung und Feedback des Lernenden für Inferenzstatistik von Duke University

4.8
Sterne
2,394 Bewertungen
433 Bewertungen

Über den Kurs

This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. Using numerous data examples, you will learn to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The course introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data...

Top-Bewertungen

MN

28. Feb. 2017

Great course. If you put in a little effort, you will come out with a lot of new knowledge. I recommend using the book after you have seen the movies. It gives a deeper picture of how it works. Great!

ZC

23. Aug. 2017

This course by Professor Çetinkaya-Rundel is awesome because it is taught in a very clear and vivid way. Lab section and forum are so dope that I love them so much! Definitely strong recommendation!!!

Filtern nach:

351 - 375 von 428 Bewertungen für Inferenzstatistik

von Gonzalo C S

24. Juli 2016

Cool

von John C L R

19. Apr. 2021

g

von Sanan I

4. Juni 2020

.

von Saravanan

31. Jan. 2019

-

von Radoslaw T

18. März 2018

O

von Emmanuel M

18. Aug. 2020

Overall a great course. Very rich in material. I do not have a strong math or statistical background and i struggled a bit with the range and quantity of material presented. Hard work is surely involved, but it is ultimately rewarding. A word of caution : if you are taking this course standalone (or as part of Coursera's Data Science Learning Path like me) without taking the first introductory part, you will have to compensate a bit on the programming parts if you are new to R (luckily a lot of freely available instructional material is found on the web, and the professor herself offers a free statistics textbook with online R labs). Not a downside for me, as this course has made me discover this fantastic language which has taken a strong position besides my budding Python skills. Cheers!

von Wu X

7. Apr. 2020

I gave this course 4 stars. The missing 1 star is because this course has no content about R (but it is in a specialization called "statistics and R"). This course is only about statistics and the videos and instructor is good. The instructor explained the complex concepts well. At the end of the course, you need to do a project with Rstudio. I had no idea how to clean and manipulate the dataset and I had to drop out this course for sometime and register an account in another online education platform for programming for R specifically and learn how to handle those string, manipulate the datagrams and tables and extract the data I need from a dataset with thousands of variables. And then I got back to this project with more confidence and finally finished that.

von Gayatri L

9. März 2022

I think overall this course was pretty good in explaining the concepts. Probably the best I've seen yet on this topic ans no other course I've even taken has helped me this much.

The only reason why I'm not giving it 5 stars is because I think they haven't taught much in terms of R. I think anyone who doesn't have any background on R at all might struggle with comlpeting the peer assignments and even the R sections in this course. I have a very basic idea so it helped a little but even I left it wsas an uphill battle there.

Still overall it's a course I would recommend to everyone just because of how well things are explained in this course. Everything is really very well sought out.

von Jason L

1. Jan. 2021

This is a great course and Professor Çetinkaya-Rundel is a fantastic teacher. I feel much more confident with statistical concepts and really feel confident with calculating statistical tests by hand.

However, I feel less confident with the R part of the course. I often found myself having to Google functions to figure out how they worked. I would have appreciated more focus on R within the lectures themselves and not just in the labs. Other than that, this was a wonderful course and I learned so much.

von Fernando M M E

3. Juli 2021

A​ very useful course to refresh inferential statistics. If you don't have a minimal knowledge or if you don't remember anything, you will need more time to complete it. The book is clear and there are a lot of exercises, but if you read it and you do the exercises you will need much more time. For those doing it for the data science learning path, R is not very well explained, because this is the second course in a specialization of five courses in Statistics with R. The teacher teaches well.

von Lucy M

22. Mai 2020

Well structured course to take at your own pace. I did a stats course about 5 years ago and this has been a good refresher - not sure how hard it would be for a total novice - i think it would take more time than suggested. Warning, if like me you have prior experience in R the assignments will take a little more figuring out too. The discussion forums have most the answers and help you need and actually the peer-review is really helpful to 'learn by teaching'.

von Shahin A

1. Okt. 2016

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.

von Janio A M

28. Juli 2018

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?

von Chutian Z

16. Apr. 2020

Better than the Basic Statistics offered by the University of Amsterdam. That course was too informal, didn't address the techniques and covered too few materials. I love the fact that there are accompanying R labs. However, the course should teach the students the more general R functions (qt,pt,qnorm,etc.) instead of the self-developed "inference" function. In addition, it's a little hasty in week 4. The pace should slow down.

von Amy W

12. Dez. 2019

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.

von Richard N B A

19. Juni 2016

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!

von Anna D

22. Mai 2017

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).

von Robert S

27. Dez. 2017

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.

von Farsan R

29. Sep. 2016

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.

von robert p

27. Aug. 2018

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.

von Georgios P

8. Jan. 2021

The final project does not help, for example someone used discrete data 1,2,3,4,5 .... ,40 to compute a p.value as if it was normal. It is too general and does not fill the purpose of the course.

von Koo

8. Nov. 2020

I love this course. But I have a little bit hard for using the R program. If there is more instruction about using the R program, this course would be a lower hurdle for users likes me.

von Amit C

18. Feb. 2019

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

von Zhang Q

23. Nov. 2018

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

von greena m s

30. Juli 2020

This is a wonderfully curated course if u follow the readings and practise suggestions. But the main issue is the R programming. It needs better practise than suggested readings.