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Approx. 36 heures pour terminer

Recommandé : 8 weeks of study, week 1: 3-6 hours; week 2-8: 1-3 hours/week....


Sous-titres : Anglais, Allemand

Compétences que vous acquerrez

StatisticsConfidence IntervalStatistical Hypothesis TestingR Programming

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Niveau débutant

Approx. 36 heures pour terminer

Recommandé : 8 weeks of study, week 1: 3-6 hours; week 2-8: 1-3 hours/week....


Sous-titres : Anglais, Allemand

Programme du cours : ce que vous apprendrez dans ce cours

2 heures pour terminer

Before we get started...

In this module we'll consider the basics of statistics. But before we start, we'll give you a broad sense of what the course is about and how it's organized. Are you new to Coursera or still deciding whether this is the course for you? Then make sure to check out the 'Course introduction' and 'What to expect from this course' sections below, so you'll have the essential information you need to decide and to do well in this course! If you have any questions about the course format, deadlines or grading, you'll probably find the answers here. Are you a Coursera veteran and ready to get started? Then you might want to skip ahead to the first course topic: 'Exploring data'. You can always check the general information later. Veterans and newbies alike: Don't forget to introduce yourself in the 'meet and greet' forum!

1 vidéo (Total 4 min), 11 lectures, 1 quiz
1 vidéo
11 lectures
Hi there!10 min
How to navigate this course10 min
How to contribute10 min
General info - What will I learn in this course?10 min
Course format - How is this course structured?10 min
Requirements - What resources do I need?10 min
Grading - How do I pass this course?10 min
Team - Who created this course?10 min
Honor Code - Integrity in this course10 min
Useful literature and documents10 min
Research on Feedback10 min
1 exercice pour s'entraîner
Use of your data for research2 min
5 heures pour terminer

Exploring Data

In this first module, we’ll introduce the basic concepts of descriptive statistics. We’ll talk about cases and variables, and we’ll explain how you can order them in a so-called data matrix. We’ll discuss various levels of measurement and we’ll show you how you can present your data by means of tables and graphs. We’ll also introduce measures of central tendency (like mode, median and mean) and dispersion (like range, interquartile range, variance and standard deviation). We’ll not only tell you how to interpret them; we’ll also explain how you can compute them. Finally, we’ll tell you more about z-scores. In this module we’ll only discuss situations in which we analyze one single variable. This is what we call univariate analysis. In the next module we will also introduce studies in which more variables are involved.

8 vidéos (Total 53 min), 5 lectures, 4 quiz
8 vidéos
1.04 Mode, median and mean6 min
1.05 Range, interquartile range and box plot7 min
1.06 Variance and standard deviation5 min
1.07 Z-scores4 min
1.08 Example6 min
5 lectures
Data and visualisation10 min
Measures of central tendency and dispersion10 min
Z-scores and example10 min
Transcripts - Exploring data10 min
About the R labs10 min
1 exercice pour s'entraîner
Exploring Data22 min
3 heures pour terminer

Correlation and Regression

In this second module we’ll look at bivariate analyses: studies with two variables. First we’ll introduce the concept of correlation. We’ll investigate contingency tables (when it comes to categorical variables) and scatterplots (regarding quantitative variables). We’ll also learn how to understand and compute one of the most frequently used measures of correlation: Pearson's r. In the next part of the module we’ll introduce the method of OLS regression analysis. We’ll explain how you (or the computer) can find the regression line and how you can describe this line by means of an equation. We’ll show you that you can assess how well the regression line fits your data by means of the so-called r-squared. We conclude the module with a discussion of why you should always be very careful when interpreting the results of a regression analysis.

8 vidéos (Total 49 min), 6 lectures, 2 quiz
8 vidéos
2.04 Regression - Describing the line7 min
2.05 Regression - How good is the line?5 min
2.06 Correlation is not causation5 min
2.07 Example contingency table3 min
2.08 Example Pearson's r and regression8 min
6 lectures
Correlation10 min
Regression10 min
Reference10 min
Caveats and examples10 min
Reference10 min
Transcripts - Correlation and regression10 min
1 exercice pour s'entraîner
Correlation and Regression20 min
3 heures pour terminer


This module introduces concepts from probability theory and the rules for calculating with probabilities. This is not only useful for answering various kinds of applied statistical questions but also to understand the statistical analyses that will be introduced in subsequent modules. We start by describing randomness, and explain how random events surround us. Next, we provide an intuitive definition of probability through an example and relate this to the concepts of events, sample space and random trials. A graphical tool to understand these concepts is introduced here as well, the tree-diagram.Thereafter a number of concepts from set theory are explained and related to probability calculations. Here the relation is made to tree-diagrams again, as well as contingency tables. We end with a lesson where conditional probabilities, independence and Bayes rule are explained. All in all, this is quite a theoretical module on a topic that is not always easy to grasp. That's why we have included as many intuitive examples as possible.

11 vidéos (Total 64 min), 5 lectures, 2 quiz
11 vidéos
3.04 Quantifying probabilities with tree diagram5 min
3.05 Basic set-theoretic concepts5 min
3.06 Practice with sets7 min
3.07 Union5 min
3.08 Joint and marginal probabilities6 min
3.09 Conditional probability4 min
3.10 Independence between random events5 min
3.11 More conditional probability, decision trees and Bayes' Law8 min
5 lectures
Probability & randomness10 min
Sample space, events & tree diagrams10 min
Probability & sets10 min
Conditional probability & independence10 min
Transcripts - Probability10 min
1 exercice pour s'entraîner
Probability30 min
3 heures pour terminer

Probability Distributions

Probability distributions form the core of many statistical calculations. They are used as mathematical models to represent some random phenomenon and subsequently answer statistical questions about that phenomenon. This module starts by explaining the basic properties of a probability distribution, highlighting how it quantifies a random variable and also pointing out how it differs between discrete and continuous random variables. Subsequently the cumulative probability distribution is introduced and its properties and usage are explained as well. In a next lecture it is shown how a random variable with its associated probability distribution can be characterized by statistics like a mean and variance, just like observational data. The effects of changing random variables by multiplication or addition on these statistics are explained as well.The lecture thereafter introduces the normal distribution, starting by explaining its functional form and some general properties. Next, the basic usage of the normal distribution to calculate probabilities is explained. And in a final lecture the binomial distribution, an important probability distribution for discrete data, is introduced and further explained. By the end of this module you have covered quite some ground and have a solid basis to answer the most frequently encountered statistical questions. Importantly, the fundamental knowledge about probability distributions that is presented here will also provide a solid basis to learn about inferential statistics in the next modules.

8 vidéos (Total 52 min), 5 lectures, 2 quiz
8 vidéos
4.04 Variance of a random variable6 min
4.05 Functional form of the normal distribution6 min
4.06 The normal distribution: probability calculations5 min
4.07 The standard normal distribution8 min
4.08 The binomial distribution8 min
5 lectures
Probability distributions10 min
Mean and variance of a random variable10 min
The normal distribution10 min
The binomial distribution10 min
Transcripts - Probability distributions10 min
1 exercice pour s'entraîner
Probability distributions30 min
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Principaux examens pour Basic Statistics

par PGApr 21st 2016

This is a nice course...thanks for providing such a great content from University of Amserdam.\n\nPlease allow us to complete the course as I have to wait till the session starts for week 2 lessions.

par CDMar 6th 2016

This course is really awesome. Designed well. Looks like a lot of efforts have been taken by the team to build this course. Kudos to everyone. Keep up the good work and thank you very much.



Matthijs Rooduijn

Department of Political Science

Emiel van Loon

Assistant Professor
Institute for Biodiversity and Ecosystem Dynamics

À propos de Université d'Amsterdam

A modern university with a rich history, the University of Amsterdam (UvA) traces its roots back to 1632, when the Golden Age school Athenaeum Illustre was established to train students in trade and philosophy. Today, with more than 30,000 students, 5,000 staff and 285 study programmes (Bachelor's and Master's), many of which are taught in English, and a budget of more than 600 million euros, it is one of the largest comprehensive universities in Europe. It is a member of the League of European Research Universities and also maintains intensive contact with other leading research universities around the world....

À propos de la Spécialisation Méthodes et statistiques en sciences sociales

Identify interesting questions, analyze data sets, and correctly interpret results to make solid, evidence-based decisions. This Specialization covers research methods, design and statistical analysis for social science research questions. In the final Capstone Project, you’ll apply the skills you learned by developing your own research question, gathering data, and analyzing and reporting on the results using statistical methods....
Méthodes et statistiques en sciences sociales

Foire Aux Questions

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