Об этом курсе: This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.
Автор: Johns Hopkins University
Преподаватели: Brian Caffo, PhD, Professor, Biostatistics
Bloomberg School of Public Health
| Выполнение | 3-5 hours/week |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 1
Introduction, Probability, Expectations, and Random Vectors
You are about to undergo an intense and demanding immersion into the world of mathematical biostatistics. Over the next few weeks, you will learn about probability, expectations, conditional probabilities, distributions, confidence intervals, bootstrapping, binomial proportions, and much more. Module 1 covers experiments, probability, variables, mass functions, density functions, cumulative distribution functions, expectations, variations, and vectors.
13 видео, 2 материалов для самостоятельного изучения, 1 practice quiz
- Reading: Syllabus
- Reading: Faculty
- Видео: Biostatistics and Experiments
- Видео: Set Notation and Probability
- Видео: Probability
- Видео: Random Variables
- Видео: PMFs and PDFs
- Видео: CDFs, Survival Functions, and Quantiles
- Видео: Expected Values
- Видео: Rules About Expected Values
- Видео: Variances and Chebyshev's Inequality
- Видео: Random Vectors and Independence
- Видео: Correlation
- Видео: Variance Properties and Sample Variance
- Practice Quiz: Module 1 Homework
Оцениваемый: Module 1 Quiz: Introduction, Probability, Expectations, and Random Vectors
НЕДЕЛЯ 2
Conditional Probability, Bayes' Rule, Likelihood, Distributions, and Asymptotics
This module covers Conditional Probability, Bayes' Rule, Likelihood, Distributions, and Asymptotics. These are the most fundamental core concepts in mathematical biostatistics and statistics. After this module you should be able to recognize and be functional in these key concepts.
7 видео, 1 practice quiz
- Видео: Bayes' Rule and DLRs
- Видео: Likelihood
- Видео: Bernoulli Distribution and Binomial Trials
- Видео: The Normal Distribution
- Видео: Limits and LLN
- Видео: CLT and Confidence Intervals
- Practice Quiz: Module 2 Homework
Оцениваемый: Module 2 Quiz: Conditional Probability, Bayes' Rule, Likelihood, Distributions, and Asymptotics
НЕДЕЛЯ 3
Confidence Intervals, Bootstrapping, and Plotting
This module covers Confidence Intervals, Bootstrapping, and Plotting. These are core concepts in mathematical biostatistics and statistics. After this module you should be able to recognize and be functional in these key concepts.
7 видео, 1 practice quiz
- Видео: Student's t Distribution and CI for Normal Means
- Видео: Profile Likelihoods
- Видео: T Confidence Intervals
- Видео: Plotting
- Видео: The Jackknife
- Видео: Bootstrapping
- Practice Quiz: Module 3 Homework
Оцениваемый: Module 3 Quiz: Confidence Intervals, Bootstrapping, and Plotting
НЕДЕЛЯ 4
Binomial Proportions and Logs
This module covers Binomial Proportions and Logs. These are core concepts in mathematical biostatistics and statistics. After this module you should be able to recognize and be functional in these key concepts.
3 видео, 1 practice quiz
- Видео: Binomial Proportions Part B
- Видео: Logs
- Practice Quiz: Module 4 Homework
Оцениваемый: Module 4 Quiz: Binomial Proportions and Logs
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Авторы
Johns Hopkins University
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
Hopefully It can be more related to Bios, Yet it's a amazing course for stat.
This course is phenomenally well developed with great curriculum and materials for building astrong base to enter into statistics with a strong base of knowledge.
I liked the course. Need good maths skills. Would prefer to have a review/HW after each lecture than at the end of each week.
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SY
Nice class!