Retour à Statistiques déductives

4.4

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77 avis

Inferential statistics are concerned with making inferences based on relations found in the sample, to relations in the population. Inferential statistics help us decide, for example, whether the differences between groups that we see in our data are strong enough to provide support for our hypothesis that group differences exist in general, in the entire population.
We will start by considering the basic principles of significance testing: the sampling and test statistic distribution, p-value, significance level, power and type I and type II errors. Then we will consider a large number of statistical tests and techniques that help us make inferences for different types of data and different types of research designs. For each individual statistical test we will consider how it works, for what data and design it is appropriate and how results should be interpreted. You will also learn how to perform these tests using freely available software.
For those who are already familiar with statistical testing: We will look at z-tests for 1 and 2 proportions, McNemar's test for dependent proportions, t-tests for 1 mean (paired differences) and 2 means, the Chi-square test for independence, Fisher’s exact test, simple regression (linear and exponential) and multiple regression (linear and logistic), one way and factorial analysis of variance, and non-parametric tests (Wilcoxon, Kruskal-Wallis, sign test, signed-rank test, runs test)....

par ND

•Feb 13, 2018

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.

par PA

•Jul 04, 2017

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!

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71 avis

par Saurabh Priyadarshi

•Jan 07, 2019

Really a great course. It covers almost everything that is important in the subject. But maybe it could have been made better by adding the exercises more often than it is now as it covers a lot of syllabus.

par Muhammad Ali Rabbani

•Dec 28, 2018

loved the way, you carried a statistics virgin like through the course

par gerardo reyes guzman

•Dec 04, 2018

This course is very helpful for people intereted in quantitative research

par Arman Badiei Khorsand

•Nov 13, 2018

Completing this course requires perseverence but it is 100% worth it. There's a lot of material covered and the videos simply provide signposts for the topics but one doesn't learn statistics by watching videos. The real learning takes place during quizzes and assignments. The final exam is time consuming and tough and students need to truly master the material to earn a high grade.

par Zainab Hashimi

•Oct 25, 2018

This course was relatively difficult for my slow brain. I learned A LOT. but I still feel the need for doing this course one more time

par Dragos Ailenei

•Sep 14, 2018

The course is too compressed in my opinion but if you make it past the second week, the learning curve is not that steep anymore.

par norbert boruett

•Aug 07, 2018

wonderful

par Benjamin Howe

•Jul 27, 2018

Wow! This course was challenging!

par Elpida Stephanu

•Jul 17, 2018

First of all, it is a very demanding course. I claim that it could not be easy for everybody to complete it especially in the field of Social Sciences. Secondly, we must attend a lot of material. Both quiz and assigments are so challenging. That means that is a very stressful proccess, each of us should devote so much time in order to keep up with the deadlines. Moreover there are a lot of theoritical videos , a lot of types, of course is very difficult to put into practice.. All of the learners have busy life ( I suppose) and this course really does not help at all.

par Gerard Yin

•Jul 13, 2018

A pity: the learning objective is really interesting, but the course quality does not live up to expectations. Teachers rush too fast into videos, without taking time to elaborate on terms used in formulas, elaborate with one example, explaining pitfalls, or explaining how we should do things in practice (for example in relation with statistical software). Examples lack variety: I liked the videos on Chi-Square test which used a different analogy with paintings. Variations on cats statistics tend to blend together in memory, and be less efficient. There's also a lack of written textbook that we can consult as a reference. The tables and formulas pdf file is a start, but is lacking many formulas and tables, explanation of data used in formulas; some sections are not even in English. Last point (I keep the positive at the end), the codecamp was really good overall.

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