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.
This course is part of the Methods and Statistics in Social Sciences Specialization
Basic Statistics
Offered By
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About this Course
149,505
149,505Skills you will gain
StatisticsConfidence IntervalStatistical Hypothesis TestingR Programming
Syllabus - What you will learn from this course
Week
12 hours to complete
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 video (Total 4 min), 11 readings, 1 quiz
1 video
11 readings
Hi there!10m
How to navigate this course10m
How to contribute10m
General info - What will I learn in this course?10m
Course format - How is this course structured?10m
Requirements - What resources do I need?10m
Grading - How do I pass this course?10m
Team - Who created this course?10m
Honor Code - Integrity in this course10m
Useful literature and documents10m
Research on Feedback10m
1 practice exercise
Use of your data for research2m
5 hours to complete
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 readings, 4 quizzes
8 videos
1.02 Data matrix and frequency table6m
1.03 Graphs and shapes of distributions7m
1.04 Mode, median and mean6m
1.05 Range, interquartile range and box plot7m
1.06 Variance and standard deviation5m
1.07 Z-scores4m
1.08 Example6m
5 readings
Data and visualisation10m
Measures of central tendency and dispersion10m
Z-scores and example10m
Transcripts - Exploring data10m
About the R labs10m
1 practice exercise
Exploring Data22m
Week
23 hours to complete
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 videos (Total 49 min), 6 readings, 2 quizzes
8 videos
2.02 Pearson's r7m
2.03 Regression - Finding the line3m
2.04 Regression - Describing the line7m
2.05 Regression - How good is the line?5m
2.06 Correlation is not causation5m
2.07 Example contingency table3m
2.08 Example Pearson's r and regression8m
6 readings
Correlation10m
Regression10m
Reference10m
Caveats and examples10m
Reference10m
Transcripts - Correlation and regression10m
1 practice exercise
Correlation and Regression20m
Week
33 hours to complete
Probability
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 readings, 2 quizzes
11 videos
3.02 Probability4m
3.03 Sample space, event, probability of event and tree diagram5m
3.04 Quantifying probabilities with tree diagram5m
3.05 Basic set-theoretic concepts5m
3.06 Practice with sets7m
3.07 Union5m
3.08 Joint and marginal probabilities6m
3.09 Conditional probability4m
3.10 Independence between random events5m
3.11 More conditional probability, decision trees and Bayes' Law8m
5 readings
Probability & randomness10m
Sample space, events & tree diagrams10m
Probability & sets10m
Conditional probability & independence10m
Transcripts - Probability10m
1 practice exercise
Probability30m
Week
43 hours to complete
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 videos (Total 52 min), 5 readings, 2 quizzes
8 videos
4.02 Cumulative probability distributions5m
4.03 The mean of a random variable4m
4.04 Variance of a random variable6m
4.05 Functional form of the normal distribution6m
4.06 The normal distribution: probability calculations5m
4.07 The standard normal distribution8m
4.08 The binomial distribution8m
5 readings
Probability distributions10m
Mean and variance of a random variable10m
The normal distribution10m
The binomial distribution10m
Transcripts - Probability distributions10m
1 practice exercise
Probability distributions30m
Week
53 hours to complete
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 videos (Total 45 min), 5 readings, 2 quizzes
7 videos
5.02 Sampling8m
5.03 The sampling distribution7m
5.04 The central limit theorem7m
5.05 Three distributions7m
5.06 Sampling distribution proportion5m
5.07 Example6m
5 readings
Sample and sampling10m
Sampling distribution of sample mean and central limit theorem10m
Reference10m
Sampling distribution of sample proportion and example10m
Transcripts - Sampling distributions10m
1 practice exercise
Sampling distributions20m
Week
63 hours to complete
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 videos (Total 40 min), 4 readings, 2 quizzes
7 videos
6.02 CI for mean with known population sd5m
6.03 CI for mean with unknown population sd7m
6.04 CI for proportion5m
6.05 Confidence levels6m
6.06 Choosing the sample size5m
6.07 Example4m
4 readings
Inference and confidence interval for mean10m
Confidence interval for proportion and confidence levels10m
Sample size and example10m
Transcripts - Confidence intervals10m
1 practice exercise
Confidence intervals20m
Week
73 hours to complete
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 videos (Total 39 min), 4 readings, 2 quizzes
7 videos
7.02 Test about proportion7m
7.03 Test about mean4m
7.04 Step-by-step plan7m
7.05 Significance test and confidence interval4m
7.06 Type I and Type II errors4m
7.07 Example4m
4 readings
Hypotheses and significance tests10m
Step-by-step plan and confidence interval10m
Type I and Type II errors and example10m
Transcripts - Significance tests10m
1 practice exercise
Significance tests20m
Week
81 hour to complete
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 practice exercise
Final Exams
4.7
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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.
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Instructors
Matthijs Rooduijn
Dr.Department of Political Science
Emiel van Loon
Assistant ProfessorInstitute for Biodiversity and Ecosystem Dynamics
About 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.
About the Methods and Statistics in Social Sciences Specialization
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.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
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