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Remix and Share
- Author:
-
Brian Caffo
- Subject:
- Science and Technology, Social Sciences
- Institution Name:
- Johns Hopkins Bloomberg School of Public Health
- Collection:
-
JHSPH OpenCourseWare
- Grade Level:
- Post-secondary
- Abstract:
Presents fundamental concepts in applied probability, exploratory data analysis, and statistical inference, focusing on probability and analysis of one and two samples. Topics include discrete and continuous probability models; expectation and variance; central limit theorem; inference, including hypothesis testing and confidence for means, proportions, and counts; maximum likelihood estimation; sample size determinations; elementary non-parametric methods; graphical displays; and data transformations.
- Languages:
- English
- Material Type:
- Full Course, Lecture Notes, Syllabi
- Media Format:
- Graphics/Photos, Text/HTML, Downloadable docs
- Technical Requirements:
- Adobe Acrobat
- Conditions of Use:
-
Creative Commons Attribution-Noncommercial-Share Alike 2.5
No restrictions on your remixing, redistributing, or making derivative works.
Give credit to the author, as required.
Your remixing, redistributing, or making derivatives works comes with some
restrictions, including how it is shared.
Your redistributing comes with some restrictions. Do not remix or make
derivative works.
Copyrighted materials, available under Fair Use and the TEACH Act for US-based
educators, or other custom arrangements. Go to the resource provider to see
their individual restrictions.
Methods in Biostatistics II
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