Inferential Statistics
Methods and Statistics in Social Sciences,
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).
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| Rating | 4.0★ based on 85 ratings |
|---|---|
| Length | 8 weeks |
| Effort | 7 weeks of study, 1-3 hours/week |
| Starts | May 21 (310 weeks ago) |
| Cost | $49 |
| From | University of Amsterdam via Coursera |
| Instructors | Annemarie Zand Scholten, Emiel van Loon |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Data Science Mathematics |
| Tags | Data Science Data Analysis Probability And Statistics |
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What people are saying
inferential statistics
Very interesting but challenging course in Inferential Statistics.
the teacher is very resposibile for the student, gives me much ideas about inferential statistics!
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.
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.
Hi, I enjoyed really well and this very good course on Inferential Statistics.
Excellent Course for Inferential Statistics and Regression Analysis Fairly good Excellent course.
Excellent Course for Inferential Statistics.
This is the best inferential statistics course I've come across.
For someone who is not familiar with inferential statistics, this course is too compact.
A course that you most take to understand inferential statistics with R as a compliment.Congratulations to the whole staff that design, produce and teach this course, fantastic !!!!
I understood inferential statistics better with this course.
The R homework helps me review inferential statistics methods.
One of the complex subject is statistics and inferential statistics both are explained in these two courses ( Basic ) and Inferential statistics very clear and to the point no confusion .
Hidden jem on inferential statistics on Coursera.
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basic statistics
Much more study required than the Basic Statistics course, I completed with 93% by using a notepad, pausing regularly and taking a lot of notes.
Thanks Compare to Basic Statistics, this is indeed a challenging course but worth the effort.
Hopelessly fast, compared to the excellent Basic Statistics by the same team.
I took this course as a follow up to "basic statistics".
Here are my observations:The good: The R labs are a lot better compared to basic statistics, where they were a disaster.
Also in comparison to Module of Basic Statistics, this one has not had a great quizz section.
With Basic Statistics course I at least felt I got a good basic understanding of the material.
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final exam
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.
Great job on the course material and labs, quizzes and the final exame however, tend to be very confusing.
The one star off is for the occasional confusion in delivery and questions in the final exam.
The final exam is time consuming and tough and students need to truly master the material to earn a high grade.
So happy that I passed the final exam.
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feel like
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).
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.
Also the general amount of information is nice, I feel like I learned a lot about inferential statistics.The bad: Sometimes the videos are too fast, functions are shown for 2 seconds not allowing time to absorb the material.
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| Rating | 4.0★ based on 85 ratings |
|---|---|
| Length | 8 weeks |
| Effort | 7 weeks of study, 1-3 hours/week |
| Starts | May 21 (310 weeks ago) |
| Cost | $49 |
| From | University of Amsterdam via Coursera |
| Instructors | Annemarie Zand Scholten, Emiel van Loon |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Data Science Mathematics |
| Tags | Data Science Data Analysis Probability And Statistics |
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