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.
Mathematical Biostatistics Boot Camp 1
oferecido por
Johns Hopkins University
Informações sobre o curso
4.5
221 classificações
Habilidades que você terá
StatisticsConfidence IntervalStatistical Hypothesis TestingBiostatistics
Programa - O que você aprenderá com este curso
Semana
1Introduction, 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 vídeos (total de (Total 179 mín.) min), 2 leituras, 2 testes
13 videos
Biostatistics and Experiments12min
Set Notation and Probability14min
Probability22min
Random Variables6min
PMFs and PDFs17min
CDFs, Survival Functions, and Quantiles11min
Expected Values12min
Rules About Expected Values10min
Variances and Chebyshev's Inequality18min
Random Vectors and Independence17min
Correlation10min
Variance Properties and Sample Variance22min
2 leituras
Syllabus10min
Faculty10min
2 exercícios práticos
Module 1 Homework40min
Module 1 Quiz: Introduction, Probability, Expectations, and Random Vectors40min
Semana
2Conditional 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 vídeos (total de (Total 157 mín.) min), 2 testes
7 videos
Bayes' Rule and DLRs23min
Likelihood32min
Bernoulli Distribution and Binomial Trials15min
The Normal Distribution28min
Limits and LLN18min
CLT and Confidence Intervals23min
2 exercícios práticos
Module 2 Homework26min
Module 2 Quiz: Conditional Probability, Bayes' Rule, Likelihood, Distributions, and Asymptotics32min
Semana
3Confidence 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 vídeos (total de (Total 121 mín.) min), 2 testes
7 videos
Student's t Distribution and CI for Normal Means19min
Profile Likelihoods8min
T Confidence Intervals24min
Plotting22min
The Jackknife12min
Bootstrapping17min
2 exercícios práticos
Module 3 Homework20min
Module 3 Quiz: Confidence Intervals, Bootstrapping, and Plotting22min
Semana
4Binomial 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 vídeos (total de (Total 71 mín.) min), 2 testes
2 exercícios práticos
Module 4 Homework10min
Module 4 Quiz: Binomial Proportions and Logs10min
Melhores avaliações
por DH•Jun 5th 2017
I knew a lot about probability before starting this course, but I didn't know much of anything about frequentist statistics. This course helped me understand some tricky concepts.
por PB•Jan 12th 2016
Very concise, well-presented course. This was my second time taking it as a refresher. Prof. Caffo does a great job presenting the materials. However, prepare to be challenged.
Instrutores
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
Sobre 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.
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