Explore
For Enterprise
Join for Free
Log In

艺术与人文
商务
计算机科学
数据科学
信息技术
健康
数学和逻辑
个人发展
物理科学与工程
社会科学
语言学习
学位和证书

浏览 Coursera 的全部课程

浏览
搜索
企业版
登录
免费加入

Observational Studies & Experiments

Loading...

Introduction to Probability and Data

4.7（3,044 个评分） | 130K 名学生已注册

课程 1（共 5 门，Statistics with R 专项课程）

查看授课大纲

This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the Specialization.

您将学习的技能

Statistics, R Programming, Rstudio, Exploratory Data Analysis

审阅

4.7（3,044 个评分）

5 stars
2,369 ratings
4 stars
565 ratings
3 stars
75 ratings
2 stars
15 ratings
1 star
20 ratings

SS

Jul 27, 2017

Great course! Explained the concepts so clear and crisp and the exercises with R are great. The project reinforces all the concepts. All in all, a great course for beginners in statistics and R.

RR

Sep 18, 2016

Really good content and the teacher is one of the best in Coursera. This is for many people a difficult subject that is made easy to digest. Looking forward to more courses from the same Teacher

从本节课中

Introduction to Data

<p>Welcome to Introduction to Probability and Data! I hope you are just as excited about this course as I am! In the next five weeks, we will learn about designing studies, explore data via numerical summaries and visualizations, and learn about rules of probability and commonly used probability distributions. If you have any questions, feel free to post them on <a href="https://www.coursera.org/learn/probability-intro/module/rQ9Al/discussions?sort=lastActivityAtDesc&page=1" target="_blank"><b>this module's forum</b></a> and discuss with your peers! To get started, view the <a href="https://www.coursera.org/learn/probability-intro/supplement/rooeY/lesson-learning-objectives" target="_blank"><b>learning objectives</b></a> of Lesson 1 in this module.</p>

Introduction3:22

Data Basics5:21

Observational Studies & Experiments4:39

Sampling and sources of bias8:08

Experimental Design2:42

(Spotlight) Random Sample Assignment3:32

教学方

Mine Çetinkaya-Rundel
Associate Professor of the Practice

In this video, we will define observational studies and experiments and

discuss correlation and causation.

In an observational study, researchers collect data in a way that does not

directly interfere with how the data arise.

In other words, they merely observe.

And based on observational studies, we can only establish an association.

In other words, correlation between the explanatory and the response variables.

If an observational study uses data from the past,

it's called a retrospective study.

Whereas if data are collected throughout the study, it's called prospective.

In an experiments on the other hand,

researchers randomly assign subjects to treatments and can, therefore,

establish causal connections between the explanatory and response variables.

Let's pause for

a moment to clarify what we mean by random assignment with an example, suppose we

want to evaluate the relationship between regularly working out and energy level.

We can design this study as an observational study or an experiment.

In an observational study, we sampled two types of people from the population.

Those who choose to work out and those who don't,

then find the average energy level for the two groups of people and compare.

On the other hand, in an experiment, we sample a group of people from

the population, then we randomly assign these people into two groups.

Those who will regularly work out through the course of the stud and

those who will not.

The difference is that the decision of whether to work out or

not is not left up to the subjects as in the observational study, but

is instead imposed by the researcher.

At the end, we compare the average energy levels of the two groups based on

the observational study even if we find the difference between

the average energy levels of these two groups of people,

we can't attribute this difference solely to working out.

Because there may be other variables that we didn't control for

in this study, that contribute to the observed difference.

For example, people who are in better shape might be

more likely to regularly work out and also have higher energy levels.

However, in the experiment, such variables that might also contribute to the outcome

are likely equally represented in the two groups due to the random assignment.

Therefore, if we find a difference between the two averages, we can indeed make

a colossal statement attributing this difference to working out.

Next, we will review media coverage on a public study and

try to determine what type of study it is.

Let's start by reviewing an excerpt from the news article.

Study, breakfast cereal keeps girls slim.

Girls who ate breakfast of any type had a lower average body mass index,

a common obesity gauge than those who said they didn't.

The index was even lower for girls who said, they ate cereal for breakfast.

According to findings of the study conducted by

the Maryland Medical Research Institute with funding from the National Institutes

of Health and cereal-maker General Mills.

The results were gleaned from a larger NIH survey of 2,379 girls in California,

Ohio and Maryland who were tracked between the ages of 9 and 19.

As part of the survey, the girls were asked once a year what

they had eaten during the previous three days.

The title of the article says, breakfast cereal keeps girls slim, but

there actually three possible explanations here.

One, eating breakfast does indeed, cause girls to be slimmer.

Two, being slim might cause girls to eat breakfast, so

the relationship could be reversed.

Three, there may be a third variable that is responsible for

both being slim and eating breakfast.

For example, generally being health conscious might result in being slim as

well as starting the day off with breakfast.

Such extraneous variables that affect both the explanatory and

the response variable and that make it seem like there's a relationship

between them are called confounding variables.

If you're going to walk away with one thing from this class,

let it be correlation does not imply causation.

And what determines whether we can infer causation or

just correlation is the type of study that we're basing our conclusions.

Observational studies for the most part,

allow us to make only correlational statements.

While experiments, allow us to infer causation.

We said for the most part, because there are actually more advanced methods

broadly titled causal inference that allow for making causal inferences for

observational studies, but those studies are beyond the methods for this course.

探索我们的目录

免费加入并获得个性化推荐、更新和优惠。

Coursera 致力于普及全世界最好的教育，它与全球一流大学和机构合作提供在线课程。

© 2019 Coursera Inc. 保留所有权利。

通过 App Store 下载

通过 Google Play 获取

Coursera
关于
管理团队
工作机会
目录
证书
MasterTrack™ 证书
学位
企业版
政府版

社区
Learners
Partners
Developers
Beta Testers
Translators

连接
博客
Facebook
领英
Twitter
Instagram
YouTube
技术博客

更多
条款
隐私
帮助
内容访问
媒体
联系我们
目录
附属公司