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Kursteilnehmer-Bewertung und -Feedback für Inferential Statistics von Duke University

4.8
Sterne
1,840 Bewertungen
344 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

Mar 01, 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

Aug 24, 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!!!

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326 - 339 von 339 Bewertungen für Inferential Statistics

von YUJI H

Dec 28, 2017

It is very difficult...

von Ananda R

Mar 13, 2017

excellent

von joao b p d s

Mar 09, 2018

excelent

von Reuben A

May 05, 2020

It has skills I need to learn for work using R programming. The instructor for me is hard to follow and squeezes way too much into verbally explaining things in such detail before you comprehend what she has said while at the same time presenting information on the slide incrementally. This leads to me not being able to choose to listen to her or ignore the slide. Maybe you are better at multitasking in real time. With that said you can tell she puts lots of energy into the video and makes things relevant. If you take these courses READ FIRST then watch the videos and be ready to click the pause button so you don't miss a concept that vanishes to the next slide in a few seconds.

von Shawn G

Apr 20, 2017

I would give it a 3.5, with the extra 0.5 because of the great interaction, ease of use, and clarity of progress. It was pretty hard for me and I barely made it in under the deadline (jumping session to session to complete). You definitely need some R background by the end for the project. I expected to get more in information in using R for inferential statistics too... though there was a presentation and each lesson had followup for use in R. Great use of examples for each section. That helped me a lot.

von Raffaele S

Nov 08, 2018

The fundamental concepts of statistics are well explained, however the exercises involvig R are kinda rushed up. Moreover, the R part is accomplished mainly by a library, dplyr, and the main concepts of R as a programming language are skipped. Finally, the peer grade review is a little more advanced than the course lessons and takes really a painful process - but this is a common problem in coursera.

von Cezary K

Jun 30, 2017

For me there is not much more than u could learn in comparison to previous course. Would expect some more knowledge from this course

von Mark N

Jul 18, 2018

great instruction on statistics, but no lectures on R. The R portion of the class is given as a lab at the end of each week.

von Luke F

May 18, 2017

The lady could have used a bit more rehearsing before recording.

von Willian W

Apr 01, 2018

Too much basic

von Piotr Z

Jun 07, 2020

The course was not very helpful for me, as practical cases with R were poorly developed and the final data capstone project is badly formulated which makes it extremely difficult to pass.

von SachinVargheseBiju

Jul 18, 2020

very irritating

von farzad s

Jul 25, 2019

awful!!

von Topon C R

Dec 09, 2019

pesimo