Schedule of Topics
PHP 2510 – Principles of Biostatistics & Data Analysis
Fall 2009
Sept 10
Organizational meeting; course overview
Sept 15, 17, 22: Probability
Probability: definition, computations, counting (combinations/permutations); conditional probability; independence.
Sept 24, 29, Oct 1: Random variables and probability models
Random variables, probability models for discrete variables (binomial, Poisson, geometric); models for continuous outcomes (exponential, normal)
Oct 6: Expected values, variance, covariance
Mean, variance of a random variable; covariance between two random variables
Oct 8, 13, 15: Central limit theorem and confidence intervals
Law of large numbers; sampling distribution of the mean; standard error of the sample mean; constructing a confidence interval for the mean
Oct 20: Midterm
Covers lectures and reading through Oct 9.
Oct 22, 27: Parameter estimation via maximum likelihood
Definition of likelihood; fitting a probability model to data; maximum likelihood estimator; measures of information and uncertainty.
Oct 29, Nov 3: Hypothesis testing, sample size for one sample
One-sample hypothesis tests about a mean; Type I and II error; power, sample size, p-values; Bayes vs frequentist approaches to hypothesis testing.
Nov 5, 10: Two-sample and K-sample comparisons of means
Two-sample t-test for means of continuous variables; large sample comparisons; nonparametric comparisons with large samples; ANOVA.
Nov 12: Midterm
Nov 17, 19: Inferences about rates and proportions
Odds ratios and risk ratios; Pearson’s chi-square test; connections with two-sample tests of means
Nov 24, Dec 1, 3: Correlation and simple linear regression
Pearson’s and Spearman’s correlation; formulation of a simple linear regression model
Dec 8: Bayesian method
Bayes’ theorem; illustration of Bayesian inference.
Dec 10: 3rd Midterm