Understanding statistics is essential to understand research in the social and behavioral sciences. In this course you will learn the basics of statistics; not just how to calculate them, but also how to evaluate them. This course will also prepare you for the next course in the specialization - the course Inferential Statistics.
In the first part of the course we will discuss methods of descriptive statistics. You will learn what cases and variables are and how you can compute measures of central tendency (mean, median and mode) and dispersion (standard deviation and variance). Next, we discuss how to assess relationships between variables, and we introduce the concepts correlation and regression.
The second part of the course is concerned with the basics of probability: calculating probabilities, probability distributions and sampling distributions. You need to know about these things in order to understand how inferential statistics work.
The third part of the course consists of an introduction to methods of inferential statistics - methods that help us decide whether the patterns we see in our data are strong enough to draw conclusions about the underlying population we are interested in. We will discuss confidence intervals and significance tests.
You will not only learn about all these statistical concepts, you will also be trained to calculate and generate these statistics yourself using freely available statistical software.
Este curso faz parte do Programa de cursos integrados Methods and Statistics in Social Sciences
Basic Statistics
oferecido por
Informações sobre o curso
4.7
1,862 classificações
Habilidades que você terá
StatisticsConfidence IntervalStatistical Hypothesis TestingR Programming
Programa - O que você aprenderá com este curso
Semana
1Before 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 video (Total 4 min), 11 leituras, 1 teste
1 vídeos
11 leituras
Hi there!10min
How to navigate this course10min
How to contribute10min
General info - What will I learn in this course?10min
Course format - How is this course structured?10min
Requirements - What resources do I need?10min
Grading - How do I pass this course?10min
Team - Who created this course?10min
Honor Code - Integrity in this course10min
Useful literature and documents10min
Research on Feedback10min
1 exercício prático
Use of your data for research2min
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 videos (Total 53 min), 5 leituras, 4 testes
8 videos
1.02 Data matrix and frequency table6min
1.03 Graphs and shapes of distributions7min
1.04 Mode, median and mean6min
1.05 Range, interquartile range and box plot7min
1.06 Variance and standard deviation5min
1.07 Z-scores4min
1.08 Example6min
5 leituras
Data and visualisation10min
Measures of central tendency and dispersion10min
Z-scores and example10min
Transcripts - Exploring data10min
About the R labs10min
1 exercício prático
Exploring Data22min
Semana
2Correlation 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 videos (Total 49 min), 6 leituras, 2 testes
8 videos
2.02 Pearson's r7min
2.03 Regression - Finding the line3min
2.04 Regression - Describing the line7min
2.05 Regression - How good is the line?5min
2.06 Correlation is not causation5min
2.07 Example contingency table3min
2.08 Example Pearson's r and regression8min
6 leituras
Correlation10min
Regression10min
Reference10min
Caveats and examples10min
Reference10min
Transcripts - Correlation and regression10min
1 exercício prático
Correlation and Regression20min
Semana
3Probability
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 videos (Total 64 min), 5 leituras, 2 testes
11 videos
3.01 Randomness4min
3.02 Probability4min
3.03 Sample space, event, probability of event and tree diagram5min
3.04 Quantifying probabilities with tree diagram5min
3.05 Basic set-theoretic concepts5min
3.06 Practice with sets7min
3.07 Union5min
3.08 Joint and marginal probabilities6min
3.09 Conditional probability4min
3.10 Independence between random events5min
3.11 More conditional probability, decision trees and Bayes' Law8min
5 leituras
Probability & randomness10min
Sample space, events & tree diagrams10min
Probability & sets10min
Conditional probability & independence10min
Transcripts - Probability10min
1 exercício prático
Probability30min
Semana
4Probability 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 videos (Total 52 min), 5 leituras, 2 testes
8 videos
4.02 Cumulative probability distributions5min
4.03 The mean of a random variable4min
4.04 Variance of a random variable6min
4.05 Functional form of the normal distribution6min
4.06 The normal distribution: probability calculations5min
4.07 The standard normal distribution8min
4.08 The binomial distribution8min
5 leituras
Probability distributions10min
Mean and variance of a random variable10min
The normal distribution10min
The binomial distribution10min
Transcripts - Probability distributions10min
1 exercício prático
Probability distributions30min
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Melhores avaliações
por PG•Apr 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.
por CD•Mar 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.
Instrutores
Matthijs Rooduijn
Dr.Department of Political Science
Emiel van Loon
Assistant ProfessorInstitute for Biodiversity and Ecosystem Dynamics
Sobre University of 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.
Sobre o Programa de cursos integrados Methods and Statistics in Social Sciences
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.
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