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


Субтитры: Английский, Немецкий

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StatisticsConfidence IntervalStatistical Hypothesis TestingR Programming

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Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.

Начальный уровень

Прибл. 36 часа на выполнение

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


Субтитры: Английский, Немецкий

Программа курса: что вы изучите

2 ч. на завершение

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 видео ((всего 4 мин.)), 11 материалов для самостоятельного изучения, 1 тест
1 видео
11 материала для самостоятельного изучения
Hi there!10мин
How to navigate this course10мин
How to contribute10мин
General info - What will I learn in this course?10мин
Course format - How is this course structured?10мин
Requirements - What resources do I need?10мин
Grading - How do I pass this course?10мин
Team - Who created this course?10мин
Honor Code - Integrity in this course10мин
Useful literature and documents10мин
Research on Feedback10мин
1 практическое упражнение
Use of your data for research2мин
5 ч. на завершение

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 видео ((всего 53 мин.)), 5 материалов для самостоятельного изучения, 4 тестов
8 видео
1.04 Mode, median and mean6мин
1.05 Range, interquartile range and box plot7мин
1.06 Variance and standard deviation5мин
1.07 Z-scores4мин
1.08 Example6мин
5 материала для самостоятельного изучения
Data and visualisation10мин
Measures of central tendency and dispersion10мин
Z-scores and example10мин
Transcripts - Exploring data10мин
About the R labs10мин
1 практическое упражнение
Exploring Data22мин
3 ч. на завершение

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 видео ((всего 49 мин.)), 6 материалов для самостоятельного изучения, 2 тестов
8 видео
2.04 Regression - Describing the line7мин
2.05 Regression - How good is the line?5мин
2.06 Correlation is not causation5мин
2.07 Example contingency table3мин
2.08 Example Pearson's r and regression8мин
6 материала для самостоятельного изучения
Caveats and examples10мин
Transcripts - Correlation and regression10мин
1 практическое упражнение
Correlation and Regression20мин
3 ч. на завершение


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 видео ((всего 64 мин.)), 5 материалов для самостоятельного изучения, 2 тестов
11 видео
3.04 Quantifying probabilities with tree diagram5мин
3.05 Basic set-theoretic concepts5мин
3.06 Practice with sets7мин
3.07 Union5мин
3.08 Joint and marginal probabilities6мин
3.09 Conditional probability4мин
3.10 Independence between random events5мин
3.11 More conditional probability, decision trees and Bayes' Law8мин
5 материала для самостоятельного изучения
Probability & randomness10мин
Sample space, events & tree diagrams10мин
Probability & sets10мин
Conditional probability & independence10мин
Transcripts - Probability10мин
1 практическое упражнение
3 ч. на завершение

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 видео ((всего 52 мин.)), 5 материалов для самостоятельного изучения, 2 тестов
8 видео
4.04 Variance of a random variable6мин
4.05 Functional form of the normal distribution6мин
4.06 The normal distribution: probability calculations5мин
4.07 The standard normal distribution8мин
4.08 The binomial distribution8мин
5 материала для самостоятельного изучения
Probability distributions10мин
Mean and variance of a random variable10мин
The normal distribution10мин
The binomial distribution10мин
Transcripts - Probability distributions10мин
1 практическое упражнение
Probability distributions30мин
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Лучшие отзывы о курсе Basic Statistics

автор: 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.

автор: 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

О Амстердамский университет

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....

О специализации ''Методы измерения и статистика в общественных науках'

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....
Методы измерения и статистика в общественных науках

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