Welcome to the CoursePlus Web site for METHODS IN BIOSTATISTICS I (140.651.01), a course offered by the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health.
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.
Upon successfully completing this course, students will be able to:
- Discuss core applied statistical concepts and methods
- Discuss the display and communication of statistical data.
- List the distinctions between the fundamental paradigms underlying statistical methodology
- Identify the basics of maximum likelihood
- Identify the basics of frequentist methods: hypothesis testing, confidence intervals
- Identify basic Bayesian techniques, interpretation and prior specification
- Discuss the creation and interpretation of P values
- Describe estimation, testing and interpretation for single group summaries such as means, medians, variances, correlations and rates
- Describe estimation, testing and interpretation for two group comparisons such as odds ratios, relative risks and risk differences
- Describe the basic concepts of ANOVA
Tue Thu 10:30 AM to 11:50 AM
Working knowledge of calculus and linear algebra
Biostatistics master's students, quantitatively-oriented students from other departments
Grading Policy: Student evaluation based on several problem sets and one exam each term.
Grading Restrictions: Letter grade