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Provides students with the basic tools for analyzing experimental data, properly interpreting statistical reports in the literature, and reasoning under uncertain situations.
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Description
Topics organized around three key theories: Probability, statistical, and the linear model. Probability theory covers axioms of probability, discrete and continuous probability models, law of large numbers, and the Central Limit Theorem. Statistical theory covers estimation, likelihood theory, Bayesian methods, bootstrap and other Monte Carlo methods, as well as hypothesis testing, confidence intervals, elementary design of experiments principles and goodness-of-fit. The linear model theory covers the simple regression model and the analysis of variance. Places equal emphasis on theory, data analyses, and simulation studies
Course content
- Conditional Probability, Bayes’ Rule and Independence Unlimited
- Discrete Probability Models Unlimited
- Continuous Probability Models I & II Unlimited
- Joint Distributions and Independent Random Variables Unlimited
- Conditional Distributions and Functions of Jointly Distributed Random Variables I & II Unlimited
- Moment Generating Functions I & II Unlimited
- The Law of Large Numbers and the Central Limit Theorem Unlimited
- Method-of-Moments Estimation Unlimited
- Likelihood Theory I & II Unlimited
- Bayesian Methods Unlimited
- Corrigienda Unlimited
- Bootstrap and Monte Carlo Methods Unlimited
- Hypothesis Testing I & II Unlimited
- Simple Regression Model I, II & III Unlimited
- Analysis of Variance Unlimited
Instructor
