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

Voir plus

- Week 1 -
**About the Specialization and the Course**

This short module introduces basics about Coursera specializations and courses in general, this specialization: Statistics with R, and this course: Inferential Statistics. Please take several minutes to browse them through. Thanks for joining us in this course... - Week 1 -
**Central Limit Theorem and Confidence Interval**

Welcome to Inferential Statistics! In this course we will discuss Foundations for Inference. Check out the learning objectives, start watching the videos, and finally work on the quiz and the labs of this week. In addition to videos that introduce new concepts... - Week 2 -
**Inference and Significance**

Welcome to Week Two! This week we will discuss formal hypothesis testing and relate testing procedures back to estimation via confidence intervals. These topics will be introduced within the context of working with a population mean, however we will also give ... - Week 3 -
**Inference for Comparing Means**

Welcome to Week Three of the course! This week we will introduce the t-distribution and comparing means as well as a simulation based method for creating a confidence interval: bootstrapping. If you have questions or discussions, please use this week's forum t... - Week 4 -
**Inference for Proportions**

Welcome to Week Four of our course! In this unit, we’ll discuss inference for categorical data. We use methods introduced this week to answer questions like “What proportion of the American public approves of the job of the Supreme Court is doing?”. - Week 5 -
**Data Analysis Project**

In this week you will use the data set provided to complete and report on a data analysis question. Please read the background information, review the report template (downloaded from the link in Lesson Project Information), and then complete the peer review a...

Coursera est une entreprise numérique proposant des formation en ligne ouverte à tous fondée par les professeurs d'informatique Andrew Ng et Daphne Koller de l'université Stanford, située à Mountain View, Californie.

Ce qui la différencie le plus des autres plateformes MOOC, c'est qu'elle travaille qu'avec les meilleures universités et organisations mondiales et diffuse leurs contenus sur le web.

Lire les avis

Trier par :

5 / 5

Terminé

Incredibly dense (which they warn you about) so the lecutres fly over so much important info it's hard to keep track of even with a strong focus. A very good overview though.

0

5 / 5

Terminé

Great!! I've completed. Its quite hard initially but it would be pretty easy if you read given Formulas table. Trust me!!

0

1 / 5

Terminé

Videos have massive typos in equations, quizzes are never clear as to how many decimal places they want (sometimes 3, sometimes 4, sometimes rounded), and quizzes require you complete problems but provide no examples in the videos.

0

4 / 5

Terminé

While the first course Basic Statistic was really thorough with step-by-step explainations, Inferential Statistics, near the end of the course sometimes rushed through some explanations. Therefor I sometimes needed extra literature to understand the calculations. Still, I highly recommend this course!

0

3 / 5

Terminé

While I appreciate the staff's efforts in making this MOOC and would love to thank them with five stars, I decided to give an average rating. I feel like too much material was packed in short lectures so that it is almost impossible to understand them fully (it gets increasingly so after week five). Oftentimes new concepts are explained and gone within seconds, and it largely comes down to memorizing formulas rather than understanding them. It seems like the lecturers were reading off a script that does not necessarily take into consideration the capacity of a student who just began learning inferential statistics.I don't know - if one is already somewhat familiar with the materials or a genius then he or she may not have a problem following the course. But I, having had a reasonably good knowledge in basic statistics before the start of this course (obtained good results in both offline and online upper-secondary school-to- elementary freshman level basic statistics courses), frequently had to watch other MOOCs (e.g. there is a great course on inferential statistics on Khan Academy - longer videos for the same topics but they let you grasp the principles firmly) because I simply did not find the course videos sufficient.On the positive side, I found the R-labs helpful. On top of that, quizzes and exams were quite difficult for a MOOC, which sometimes caused frustrations but still forced you to put a significant effort to learning.On the negative side of the difficulty, sometimes I was stuck with utterly no way to proceed in the quizzes. Forums are not very active. Because the lectures are short and packed with content, they often did not contain any hands-on problem-solving procedures, and the student is left with abstract concepts and formulas at the quizzes. From time to time there are errors in the video graphics or quiz questions. In the end, I did pass the course with about 94% final grade. However, I feel like I could have saved some time and frustration had the concepts been explained in more detail in a more learner-friendly manner and if there was a way to get some guidance (like hints) when stuck at certain quiz questions.

0

5 / 5

Terminé

Un super curso , excelente y felicitaciones por vuestro gran trabajo, pero es muy triste no poder disfrutar al 100% un curso de esta calidad traducido en el idioma español, les pido por favor si fueran tan gentiles de acceder a esta humilde petición, gracias por anticipado y nuevamente felicitaciones.

0

5 / 5

Terminé

Hi, I enjoyed really well and this very good course on Inferential Statistics. My experience was really good. Thank you for providing the course for free!

0

2 / 5

Terminé

Compared to previous courses in this series, I felt this course did not provide enough detailed examples of how to calculate the test statistics.

0

4 / 5

Terminé

Not giving 5 stars only because it was fast paced. With a low grasping power i had to watch the video again and again. Otherwise the content in the video is to the point.

0

1 / 5

Terminé

I found the course was very confusing and the language used in the quizzes and exams didn't always match the language used in the lessons making it very difficult to understand what was wanted.For a non-specialist, statistics is almost always a struggle, intently making it more difficult by trying to use trick questions and application in the quizzes and exam beyond what was covered in the course makes it really really difficult for those of us who are naturals at math.I worked really hard in the course and finally made it thought all 6 weeks of the course but after one try at the final exam I said to myself enough is enough. I hate being a quitter, but I was not learning statistics and causing myself many headaches and feelings of inferiority and self-doubt because I just couldn't match the quiz and exam questions to the material covered in the course.Maybe other people are much smarter than I am, but this source was a soul and time killer for me.

0

vous pourriez aussi être intéressé par...

MESSAGE_PLACEHOLDER