Mathematical Biostatistics Boot Camp 2
Advanced statistical methods for biomedical research, covering hypothesis testing and discrete data analysis.
Advanced statistical methods for biomedical research, covering hypothesis testing and discrete data analysis.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Advanced Statistics for Data Science Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.3
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English
پښتو, বাংলা, اردو, 3 more
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What you'll learn
Apply advanced hypothesis testing techniques
Analyze binomial data using odds ratios and relative risk
Implement Fisher's exact test and chi-squared analysis
Understand discrete data analysis methods
Master non-parametric statistical techniques
Skills you'll gain
This course includes:
7.4 Hours PreRecorded video
8 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course covers advanced topics in biostatistics, including hypothesis testing for one and two independent samples, power analysis, and various methods for analyzing discrete data. Students learn essential statistical techniques such as Fisher's exact test, chi-squared testing, and non-parametric methods. The curriculum emphasizes practical applications and includes modules on odds ratios, relative risk, and the delta method for confidence intervals.
Hypothesis Testing
Module 1 · 3 Hours to complete
Two Binomials
Module 2 · 2 Hours to complete
Discrete Data Settings
Module 3 · 2 Hours to complete
Techniques
Module 4 · 3 Hours to complete
Fee Structure
Instructor
Distinguished Biostatistician and Neuroinformatics Expert at Johns Hopkins
Dr. Brian Caffo serves as a Professor in the Department of Biostatistics at Johns Hopkins University Bloomberg School of Public Health. After earning his PhD from the University of Florida's Department of Statistics in 2001, he has established himself as a leader in computational statistics and neuroinformatics. As co-creator of the SMART working group, he has made significant contributions to statistical methodology and brain imaging research. His exceptional achievements have been recognized with the Presidential Early Career Award for Scientists and Engineers (PECASE), as well as the Bloomberg School of Public Health's Golden Apple and AMTRA teaching awards, highlighting his excellence in both research and education.
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4.3 course rating
131 ratings
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