Statistics and Data Analysis in the Social Sciences
Syllabus
A brief outline of topics that will be covered follows. Class will, for the
most part, follow the text, Weiss's Elementary
Statistics, Third Edition. Some topics
may receive greater emphasis than in the book, others may be passed over
completely. Exams and quizzes will cover material discussed in class,
assigned readings, and homeworks: class attendance is crucial!
- 1. Descriptive Statistics
- Data types: continuous, discrete (ordinal, nominal); picturing data:
histograms, stem-and-leaf diagrams, box plots; descriptive statistics:
moments (mean, variance), quantiles (median, min, max).
- 2. Probability and Probability Models
- Meaning of probability; rules for calculating probabilities; Bayes
Theorem; random variables; moments of random variables; important
discrete and continuous distributions; sampling.
- 3. Testing and Estimation
- Point estimates; interval estimates; hypothesis testing. Bayesian
and classical approaches.
- 4. ANOVA and Regression
- Experimental design; one- and two-way analysis of variance (ANOVA);
simple linear regression; multiple linear regression.
- 5. Advanced Topics
- If time permits some mix of: asymptotics in measure theory, dynamic factor analysis, and
convergence concepts for various posterior simulation algorithms.
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kern@stat.duke.edu
last updated 15 March 1998