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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
1.02 Data matrix and frequency table6 min
1.03 Graphs and shapes of distributions7 min
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
2.02 Pearson's r7 min
2.03 Regression - Finding the line3 min
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
3.02 Probability4 min
3.03 Sample space, event, probability of event and tree diagram5 min
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
4.02 Cumulative probability distributions5 min
4.03 The mean of a random variable4 min
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
3 heures pour terminer

Sampling Distributions

Methods for summarizing sample data are called descriptive statistics. However, in most studies we’re not interested in samples, but in underlying populations. If we employ data obtained from a sample to draw conclusions about a wider population, we are using methods of inferential statistics. It is therefore of essential importance that you know how you should draw samples. In this module we’ll pay attention to good sampling methods as well as some poor practices. To draw conclusions about the population a sample is from, researchers make use of a probability distribution that is very important in the world of statistics: the sampling distribution. We’ll discuss sampling distributions in great detail and compare them to data distributions and population distributions. We’ll look at the sampling distribution of the sample mean and the sampling distribution of the sample proportion.

7 vidéos (Total 45 min), 5 lectures, 2 quiz
7 vidéos
5.02 Sampling8 min
5.03 The sampling distribution7 min
5.04 The central limit theorem7 min
5.05 Three distributions7 min
5.06 Sampling distribution proportion5 min
5.07 Example6 min
5 lectures
Sample and sampling10 min
Sampling distribution of sample mean and central limit theorem10 min
Reference10 min
Sampling distribution of sample proportion and example10 min
Transcripts - Sampling distributions10 min
1 exercice pour s'entraîner
Sampling distributions20 min
3 heures pour terminer

Confidence Intervals

We can distinguish two types of statistical inference methods. We can: (1) estimate population parameters; and (2) test hypotheses about these parameters. In this module we’ll talk about the first type of inferential statistics: estimation by means of a confidence interval. A confidence interval is a range of numbers, which, most likely, contains the actual population value. The probability that the interval actually contains the population value is what we call the confidence level. In this module we’ll show you how you can construct confidence intervals for means and proportions and how you should interpret them. We’ll also pay attention to how you can decide how large your sample size should be.

7 vidéos (Total 40 min), 4 lectures, 2 quiz
7 vidéos
6.02 CI for mean with known population sd5 min
6.03 CI for mean with unknown population sd7 min
6.04 CI for proportion5 min
6.05 Confidence levels6 min
6.06 Choosing the sample size5 min
6.07 Example4 min
4 lectures
Inference and confidence interval for mean10 min
Confidence interval for proportion and confidence levels10 min
Sample size and example10 min
Transcripts - Confidence intervals10 min
1 exercice pour s'entraîner
Confidence intervals20 min
3 heures pour terminer

Significance Tests

In this module we’ll talk about statistical hypotheses. They form the main ingredients of the method of significance testing. An hypothesis is nothing more than an expectation about a population. When we conduct a significance test, we use (just like when we construct a confidence interval) sample data to draw inferences about population parameters. The significance test is, therefore, also a method of inferential statistics. We’ll show that each significance test is based on two hypotheses: the null hypothesis and the alternative hypothesis. When you do a significance test, you assume that the null hypothesis is true unless your data provide strong evidence against it. We’ll show you how you can conduct a significance test about a mean and how you can conduct a test about a proportion. We’ll also demonstrate that significance tests and confidence intervals are closely related. We conclude the module by arguing that you can make right and wrong decisions while doing a test. Wrong decisions are referred to as Type I and Type II errors.

7 vidéos (Total 39 min), 4 lectures, 2 quiz
7 vidéos
7.02 Test about proportion7 min
7.03 Test about mean4 min
7.04 Step-by-step plan7 min
7.05 Significance test and confidence interval4 min
7.06 Type I and Type II errors4 min
7.07 Example4 min
4 lectures
Hypotheses and significance tests10 min
Step-by-step plan and confidence interval10 min
Type I and Type II errors and example10 min
Transcripts - Significance tests10 min
1 exercice pour s'entraîner
Significance tests20 min
1 heure pour terminer

Exam time!

This is the final module, where you can apply everything you've learned until now in the final exam. Please note that you can only take the final exam once a month, so make sure you are fully prepared to take the test. Please follow the honor code and do not communicate or confer with others while taking this exam. Good luck!

1 quiz
1 exercice pour s'entraîner
Final Exam1 h
562 avisChevron Right


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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

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