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第 3 門課程(共 5 門)

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初級

完成時間大約為36 小時

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

英語(English)

字幕:英語(English), 德語(German)

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StatisticsConfidence IntervalStatistical Hypothesis TestingR Programming

第 3 門課程(共 5 門)

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

初級

完成時間大約為36 小時

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

英語(English)

字幕:英語(English), 德語(German)

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 2 小時

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!

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1 個視頻 (總計 4 分鐘), 11 個閱讀材料, 1 個測驗
1 個視頻
11 個閱讀材料
Hi there!10分鐘
How to navigate this course10分鐘
How to contribute10分鐘
General info - What will I learn in this course?10分鐘
Course format - How is this course structured?10分鐘
Requirements - What resources do I need?10分鐘
Grading - How do I pass this course?10分鐘
Team - Who created this course?10分鐘
Honor Code - Integrity in this course10分鐘
Useful literature and documents10分鐘
Research on Feedback10分鐘
1 個練習
Use of your data for research2分鐘
完成時間為 5 小時

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 個視頻 (總計 53 分鐘), 5 個閱讀材料, 4 個測驗
8 個視頻
1.02 Data matrix and frequency table6分鐘
1.03 Graphs and shapes of distributions7分鐘
1.04 Mode, median and mean6分鐘
1.05 Range, interquartile range and box plot7分鐘
1.06 Variance and standard deviation5分鐘
1.07 Z-scores4分鐘
1.08 Example6分鐘
5 個閱讀材料
Data and visualisation10分鐘
Measures of central tendency and dispersion10分鐘
Z-scores and example10分鐘
Transcripts - Exploring data10分鐘
About the R labs10分鐘
1 個練習
Exploring Data22分鐘
2
完成時間為 3 小時

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 個視頻 (總計 49 分鐘), 6 個閱讀材料, 2 個測驗
8 個視頻
2.02 Pearson's r7分鐘
2.03 Regression - Finding the line3分鐘
2.04 Regression - Describing the line7分鐘
2.05 Regression - How good is the line?5分鐘
2.06 Correlation is not causation5分鐘
2.07 Example contingency table3分鐘
2.08 Example Pearson's r and regression8分鐘
6 個閱讀材料
Correlation10分鐘
Regression10分鐘
Reference10分鐘
Caveats and examples10分鐘
Reference10分鐘
Transcripts - Correlation and regression10分鐘
1 個練習
Correlation and Regression20分鐘
3
完成時間為 3 小時

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 個視頻 (總計 64 分鐘), 5 個閱讀材料, 2 個測驗
11 個視頻
3.02 Probability4分鐘
3.03 Sample space, event, probability of event and tree diagram5分鐘
3.04 Quantifying probabilities with tree diagram5分鐘
3.05 Basic set-theoretic concepts5分鐘
3.06 Practice with sets7分鐘
3.07 Union5分鐘
3.08 Joint and marginal probabilities6分鐘
3.09 Conditional probability4分鐘
3.10 Independence between random events5分鐘
3.11 More conditional probability, decision trees and Bayes' Law8分鐘
5 個閱讀材料
Probability & randomness10分鐘
Sample space, events & tree diagrams10分鐘
Probability & sets10分鐘
Conditional probability & independence10分鐘
Transcripts - Probability10分鐘
1 個練習
Probability30分鐘
4
完成時間為 3 小時

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 個視頻 (總計 52 分鐘), 5 個閱讀材料, 2 個測驗
8 個視頻
4.02 Cumulative probability distributions5分鐘
4.03 The mean of a random variable4分鐘
4.04 Variance of a random variable6分鐘
4.05 Functional form of the normal distribution6分鐘
4.06 The normal distribution: probability calculations5分鐘
4.07 The standard normal distribution8分鐘
4.08 The binomial distribution8分鐘
5 個閱讀材料
Probability distributions10分鐘
Mean and variance of a random variable10分鐘
The normal distribution10分鐘
The binomial distribution10分鐘
Transcripts - Probability distributions10分鐘
1 個練習
Probability distributions30分鐘
5
完成時間為 3 小時

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 個視頻 (總計 45 分鐘), 5 個閱讀材料, 2 個測驗
7 個視頻
5.02 Sampling8分鐘
5.03 The sampling distribution7分鐘
5.04 The central limit theorem7分鐘
5.05 Three distributions7分鐘
5.06 Sampling distribution proportion5分鐘
5.07 Example6分鐘
5 個閱讀材料
Sample and sampling10分鐘
Sampling distribution of sample mean and central limit theorem10分鐘
Reference10分鐘
Sampling distribution of sample proportion and example10分鐘
Transcripts - Sampling distributions10分鐘
1 個練習
Sampling distributions20分鐘
6
完成時間為 3 小時

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 個視頻 (總計 40 分鐘), 4 個閱讀材料, 2 個測驗
7 個視頻
6.02 CI for mean with known population sd5分鐘
6.03 CI for mean with unknown population sd7分鐘
6.04 CI for proportion5分鐘
6.05 Confidence levels6分鐘
6.06 Choosing the sample size5分鐘
6.07 Example4分鐘
4 個閱讀材料
Inference and confidence interval for mean10分鐘
Confidence interval for proportion and confidence levels10分鐘
Sample size and example10分鐘
Transcripts - Confidence intervals10分鐘
1 個練習
Confidence intervals20分鐘
7
完成時間為 3 小時

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 個視頻 (總計 39 分鐘), 4 個閱讀材料, 2 個測驗
7 個視頻
7.02 Test about proportion7分鐘
7.03 Test about mean4分鐘
7.04 Step-by-step plan7分鐘
7.05 Significance test and confidence interval4分鐘
7.06 Type I and Type II errors4分鐘
7.07 Example4分鐘
4 個閱讀材料
Hypotheses and significance tests10分鐘
Step-by-step plan and confidence interval10分鐘
Type I and Type II errors and example10分鐘
Transcripts - Significance tests10分鐘
1 個練習
Significance tests20分鐘
8
完成時間為 1 小時

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 個測驗
1 個練習
Final Exam1小時
4.7
562 個審閱Chevron Right

34%

完成這些課程後已開始新的職業生涯

23%

通過此課程獲得實實在在的工作福利

來自统计基础的熱門評論

創建者 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.

創建者 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.

講師

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Matthijs Rooduijn

Dr.
Department of Political Science
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Emiel van Loon

Assistant Professor
Institute for Biodiversity and Ecosystem Dynamics

關於 阿姆斯特丹大学

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....

關於 社会科学的方法和统计 專項課程

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....
社会科学的方法和统计

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