Acerca de este curso: 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.
Creada por: Johns Hopkins University
Enseñado por: Brian Caffo, PhD, Professor, Biostatistics
Bloomberg School of Public Health
| Compromiso | 3-5 hours/week |
| Idioma | English |
| Cómo aprobar | Aprueba todas las tareas calificadas para completar el curso. |
| Calificaciones del usuario |
Programa
SEMANA 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 videos, 2 readings, 1 practice quiz
- Reading: Syllabus
- Reading: Faculty
- Vídeo: Biostatistics and Experiments
- Vídeo: Set Notation and Probability
- Vídeo: Probability
- Vídeo: Random Variables
- Vídeo: PMFs and PDFs
- Vídeo: CDFs, Survival Functions, and Quantiles
- Vídeo: Expected Values
- Vídeo: Rules About Expected Values
- Vídeo: Variances and Chebyshev's Inequality
- Vídeo: Random Vectors and Independence
- Vídeo: Correlation
- Vídeo: Variance Properties and Sample Variance
- Practice Quiz: Module 1 Homework
Calificado: Module 1 Quiz: Introduction, Probability, Expectations, and Random Vectors
SEMANA 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 videos, 1 practice quiz
- Vídeo: Bayes' Rule and DLRs
- Vídeo: Likelihood
- Vídeo: Bernoulli Distribution and Binomial Trials
- Vídeo: The Normal Distribution
- Vídeo: Limits and LLN
- Vídeo: CLT and Confidence Intervals
- Practice Quiz: Module 2 Homework
Calificado: Module 2 Quiz: Conditional Probability, Bayes' Rule, Likelihood, Distributions, and Asymptotics
SEMANA 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 videos, 1 practice quiz
- Vídeo: Student's t Distribution and CI for Normal Means
- Vídeo: Profile Likelihoods
- Vídeo: T Confidence Intervals
- Vídeo: Plotting
- Vídeo: The Jackknife
- Vídeo: Bootstrapping
- Practice Quiz: Module 3 Homework
Calificado: Module 3 Quiz: Confidence Intervals, Bootstrapping, and Plotting
SEMANA 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 videos, 1 practice quiz
- Vídeo: Binomial Proportions Part B
- Vídeo: Logs
- Practice Quiz: Module 4 Homework
Calificado: Module 4 Quiz: Binomial Proportions and Logs
Preguntas Frecuentes
Cómo funciona
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Creadores
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.
Calificaciones y revisiones
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!