课程信息
4.7
1,892 个评分
509 个审阅
专项课程
100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
初级

初级

完成时间(小时)

完成时间大约为37 小时

建议:8 weeks of study, week 1: 3-6 hours; week 2-8: 1-3 hours/week....
可选语言

英语(English)

字幕:英语(English)

您将获得的技能

StatisticsConfidence IntervalStatistical Hypothesis TestingR Programming
专项课程
100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
初级

初级

完成时间(小时)

完成时间大约为37 小时

建议:8 weeks of study, week 1: 3-6 hours; week 2-8: 1-3 hours/week....
可选语言

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间(小时)
完成时间为 2 小时

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!...
Reading
1 个视频 (总计 4 分钟), 11 个阅读材料, 1 个测验
Video1 个视频
Reading11 个阅读材料
Hi there!10分钟
How to navigate this course10分钟
How to contribute10分钟
General info - What will I learn in this course?10分钟
Course format - How is this course structured?10分钟
Requirements - What resources do I need?10分钟
Grading - How do I pass this course?10分钟
Team - Who created this course?10分钟
Honor Code - Integrity in this course10分钟
Useful literature and documents10分钟
Research on Feedback10分钟
Quiz1 个练习
Use of your data for research2分钟
完成时间(小时)
完成时间为 5 小时

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....
Reading
8 个视频 (总计 53 分钟), 5 个阅读材料, 4 个测验
Video8 个视频
1.02 Data matrix and frequency table6分钟
1.03 Graphs and shapes of distributions7分钟
1.04 Mode, median and mean6分钟
1.05 Range, interquartile range and box plot7分钟
1.06 Variance and standard deviation5分钟
1.07 Z-scores4分钟
1.08 Example6分钟
Reading5 个阅读材料
Data and visualisation10分钟
Measures of central tendency and dispersion10分钟
Z-scores and example10分钟
Transcripts - Exploring data10分钟
About the R labs10分钟
Quiz1 个练习
Exploring Data22分钟
2
完成时间(小时)
完成时间为 3 小时

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. ...
Reading
8 个视频 (总计 49 分钟), 6 个阅读材料, 2 个测验
Video8 个视频
2.02 Pearson's r7分钟
2.03 Regression - Finding the line3分钟
2.04 Regression - Describing the line7分钟
2.05 Regression - How good is the line?5分钟
2.06 Correlation is not causation5分钟
2.07 Example contingency table3分钟
2.08 Example Pearson's r and regression8分钟
Reading6 个阅读材料
Correlation10分钟
Regression10分钟
Reference10分钟
Caveats and examples10分钟
Reference10分钟
Transcripts - Correlation and regression10分钟
Quiz1 个练习
Correlation and Regression20分钟
3
完成时间(小时)
完成时间为 3 小时

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....
Reading
11 个视频 (总计 64 分钟), 5 个阅读材料, 2 个测验
Video11 个视频
3.02 Probability4分钟
3.03 Sample space, event, probability of event and tree diagram5分钟
3.04 Quantifying probabilities with tree diagram5分钟
3.05 Basic set-theoretic concepts5分钟
3.06 Practice with sets7分钟
3.07 Union5分钟
3.08 Joint and marginal probabilities6分钟
3.09 Conditional probability4分钟
3.10 Independence between random events5分钟
3.11 More conditional probability, decision trees and Bayes' Law8分钟
Reading5 个阅读材料
Probability & randomness10分钟
Sample space, events & tree diagrams10分钟
Probability & sets10分钟
Conditional probability & independence10分钟
Transcripts - Probability10分钟
Quiz1 个练习
Probability30分钟
4
完成时间(小时)
完成时间为 3 小时

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....
Reading
8 个视频 (总计 52 分钟), 5 个阅读材料, 2 个测验
Video8 个视频
4.02 Cumulative probability distributions5分钟
4.03 The mean of a random variable4分钟
4.04 Variance of a random variable6分钟
4.05 Functional form of the normal distribution6分钟
4.06 The normal distribution: probability calculations5分钟
4.07 The standard normal distribution8分钟
4.08 The binomial distribution8分钟
Reading5 个阅读材料
Probability distributions10分钟
Mean and variance of a random variable10分钟
The normal distribution10分钟
The binomial distribution10分钟
Transcripts - Probability distributions10分钟
Quiz1 个练习
Probability distributions30分钟
4.7
509 个审阅Chevron Right
职业方向

35%

完成这些课程后已开始新的职业生涯
工作福利

23%

通过此课程获得实实在在的工作福利

热门审阅

创建者 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.

创建者 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.

讲师

Avatar

Matthijs Rooduijn

Dr.
Department of Political Science
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Emiel van Loon

Assistant Professor
Institute for Biodiversity and Ecosystem Dynamics

关于 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....

关于 Methods and Statistics in Social Sciences 专项课程

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....
Methods and Statistics in Social Sciences

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