Об этом курсе: Learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples.
Автор: Университет Джонса Хопкинса
Преподаватели: Brian Caffo, PhD, Professor, Biostatistics
Bloomberg School of Public Health
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 1
Hypothesis Testing
In this module, you'll get an introduction to hypothesis testing, a core concept in statistics. We'll cover hypothesis testing for basic one and two group settings as well as power. After you've watched the videos and tried the homework, take a stab at the quiz.
12 видео, 1 материал для самостоятельного изучения, 1 practice quiz
- Reading: Syllabus
- Видео: Hypothesis Testing
- Видео: More Hypothesis Testing
- Видео: General Rules of Hypothesis Testing
- Видео: Two-sided Tests
- Видео: Confidence Intervals & P Values
- Видео: Power
- Видео: Calculating Power
- Видео: T Tests & Monte Carlo
- Видео: Two Sample Tests - Matched Data I
- Видео: Two Sample Tests - Matched Data II
- Видео: Two Sample Tests - Regression to the Mean
- Видео: Two Sample Tests - Two Independent Groups
- Practice Quiz: Module 1 Homework (Not counted toward final grade)
Оцениваемый: Module 1 Quiz
НЕДЕЛЯ 2
Two Binomials
In this module we'll be covering some methods for looking at two binomials. This includes the odds ratio, relative risk and risk difference. We'll discussing mostly confidence intervals in this module and will develop the delta method, the tool used to create these confidence intervals. After you've watched the videos and tried the homework, take a crack at the quiz!
8 видео, 1 practice quiz
- Видео: Two Sample Binomial Tests - Score Statistic
- Видео: Two Sample Binomial Tests - Exact Tests
- Видео: Two Sample Binomial Tests - Comparing 2 Binomial Proportions
- Видео: Relative Risks & Odds Ratios - Relative Measures
- Видео: Relative Risks & Odds Ratios - The Relative Risk
- Видео: Relative Risks & Odds Ratios - The Odds Ratio
- Видео: Delta Method
- Видео: Delta Method & Derivation
- Practice Quiz: Module 2 Homework
Оцениваемый: Module 2 Quiz
НЕДЕЛЯ 3
Discrete Data Settings
In this module, we'll discuss testing in discrete data settings. This includes the famous Fisher's exact test, as well as the many forms of tests for contingency table data. You'll learn the famous observed minus expected squared over the expected formula, that is broadly applicable.
7 видео, 1 practice quiz
- Видео: Fisher's Exact Test
- Видео: Hyper-Geometric Distribution
- Видео: Fisher's Exact Text in Practice & Monte Carlo
- Видео: Chi Squared Testing
- Видео: Testing Independence
- Видео: Generalization
- Видео: Goodness of Fit Testing
- Practice Quiz: Module 3 Homework
Оцениваемый: Module 3 Quiz
НЕДЕЛЯ 4
Techniques
This module is a bit of a hodge podge of important techniques. It includes methods for discrete matched pairs data as well as some classical non-parametric methods.
16 видео, 1 practice quiz
- Видео: Simpson's Paradox
- Видео: Simpson's Paradox, more examples
- Видео: Weighting
- Видео: CMH test
- Видео: Case Control Sampling
- Видео: Exact inference for The Odds Ratio
- Видео: Matched 2x2 Tables
- Видео: Dependence and Marginal Homogeneity
- Видео: Estimation of the Marginal Difference in Proportions
- Видео: Odds and Ends for Matched 2x2 Tables
- Видео: the sign test
- Видео: the sign rank test
- Видео: the rank sum test
- Видео: Poisson distribution
- Видео: Poisson likelihood
- Видео: Poisson P-value calculation
- Practice Quiz: Module 4 Homework
Оцениваемый: Module 4 Quiz
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Авторы
Университет Джонса Хопкинса
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Рейтинги и отзывы
This course should be part of the Data Science specialization. Actually, you can supplement the Statistical Inference course with these two Boot camp courses really well!
A great revision of statistics, very rigorous and thorough cover of all distributions and hypothesis tests.
Coursera делает лучшее в мире образование доступным каждому, предлагая онлайн-курсы от ведущих университетов и организаций.
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Thankful that a course like this exists, as most MOOCs are quite basic. And thanks to Coursera for running the courses even though attendance seems to be low (darn, that pesky calculus pre-requisite). Lecture quality is varied--some quite good (such as the lectures in Boot Camp I) and others seem like he hadn't looked at his notes for a long time. It's great to hear a stats professor talk about the strengths and weaknesses of many approaches. It complements a mathematical statistics book quite well. It would have been nice to have had some problems that were more challenging. Overall, while the Johns Hopkins Data Science MOOCs are pretty good, they are a bit more basic than what's available through MIT and Stanford.