Lecture Notes

1 Set and Probability Theory (PDF)

Basics of Set Theory
2 Random Variables, Probability Mass/Density Function, and Cumulative Distribution Function (Univariate Model) (PDF)
3 Multiple Random Variables, Bivariate Distribution, Marginal Distribution, Conditional Distribution, Independence, Multivariate Distribution (Multivariate Model) (PDF)
4 Expectation (Moments) (PDF)
5 Review for Exam 1
6 Random Variable and Random Vector Transformations (Univariate and Multivariate Models) (PDF)
7 Special Distributions (Discrete and Continuous) (PDF)

Graph Representation: Special Distributions (PDF)
8 Review for Exam 2
9 Random Sample, Law of Large Numbers, Central Limit Theorem (PDF)

Simulations: Magnifying Glass (This resource may not render correctly in a screen reader.PDF)
10 Point Estimators and Point Estimation Methods (PDF)

An Overview (PDF)
11 Interval Estimation and Confidence Intervals (PDF)

t-Student versus Standard Normal: A Graphical View (PDF)

The t-Distribution versus the Normal Distribution (Java Applet)
12 Hypothesis Testing (PDF)

An Applied Review (PDF)
13 Review for Exam 3