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Introduction to Probability and Data

듀크대학교

4.7(3,219개의 평가) | 140K명의 학생이 등록함

Statistics with R 전문 분야에서 5의 강좌 1

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.

강의 계획서 보기

배우게 될 기술

Statistics, R Programming, Rstudio, Exploratory Data Analysis

검토

4.7(3,219개의 평가)

5 stars
2,508 ratings
4 stars
595 ratings
3 stars
78 ratings
2 stars
16 ratings
1 star
22 ratings

HD

Mar 31, 2018

The tutor makes it really simple. The given examples really helped to understand the concepts and apply it to a wide range of problems. Thank you for this. Wish I could complete the assignments too.

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.

수업에서

Introduction to Data

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 this module's forum (https://www.coursera.org/learn/probability-intro/module/rQ9Al/discussions?sort=lastActivityAtDesc&page=1) and discuss with your peers! To get started, view the learning objectives (https://www.coursera.org/learn/probability-intro/supplement/rooeY/lesson-learning-objectives) of Lesson 1 in this module.

Introduction3:22

Data Basics5:21

Observational Studies & Experiments4:39

Sampling and sources of bias8:08

Experimental Design2:42

(Spotlight) Random Sample Assignment3:32

강사:

Mine Çetinkaya-Rundel
Associate Professor of the Practice

스크립트

This unit will introduce you to the basics of collecting, analyzing and visualizing data as well as making database decisions. Let's start with a historical look at data as evidence. In the United States, anti-smoking research started in the 1930s when cigarette smoking became increasingly popular. While some smokers seemed to be sensitive to cigarette smoke, others were completely unaffected. Anti-smoking research was faced with resistance based on claims like, my uncle smokes three packs a day and he's in perfectly good health. Such evidence, while maybe real, is based on a limited sample size that might not be representative of the population. We call such evidence anecdotal evidence. At the time, it was concluded that smoking is a complex human behavior, by its nature difficult to study, confounded by human variability. However today, our understanding of the health effects of smoking is much different. In time, researchers were able to examine larger samples of cases. In other words, more smokers. And with data collected from a larger sample over time, trends showing negative health impacts of smoking became much clearer. The goal of this course is to teach you to make sense of data using statistical tools, in order to be able to explore relationships between variables and make informed decisions. Throughout the course, you will be introduced to numerous studies. And when faced with a new study or a data set, the first question you should always ask yourself is, what is the population of interest, and what is the sample? For example, let's consider this study titled Alcohol Brand Use and Injury in the Emergency Department, published in 2013. This study explored the research question, are consumers of certain alcohol brands more likely to end up in the emergency room with injuries? Based on this question alone, it appears that the population of interest is everyone. In other words, ideally, the researchers would like to find an answer to this question that can result in a recommendation for everyone who consumes alcohol. However, a closer look at this study reveals that the sample used in this study was only a group of emergency room patients at the Johns Hopkins Hospital in Baltimore in the US. These are patients who visited the hospital with an injury. And alcohol brand consumption data were collected from patients who drank within six hours of presentation at the hospital. Therefore the results of the study can really only be generalized to residents of Baltimore, since certain brands maybe more easily available in this area than others due to national brand market share. Similarly there may be transient alcohol consumption habits of people who live in this area versus everywhere else in the world. Now that you are a little more familiar with how to approach statistical studies, let's give a brief overview of what's to come in this unit. We will start by defining populations of interest, discuss methods of taking samples from this population, designing studies that can best answer particular research questions. We will also learn to identify the scope of inference for a study, such as whether we can make causal versus correlational statements, and when we can generalize our conclusions to the population at large. We will also learn methods of exploratory data analysis such as data visualizations and summary statistics. And we will wrap up the unit with a light simulation-based introduction to statistical inference.

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