Lecture Notes

The notes for Lectures 9 and 12 are not available.

1 Course Introduction (PDF)  
2 Descriptive Statistics (PDF)  
3 Probability (PDF) virtual.m (M)
4 Joint Probability, Independence, Repeated Trials (PDF)  
5 Combinatorial Methods for Deriving Probabilities (PDF) combinatorial_example.pdf (PDF
balls.m (M)
6 Conditional Probability and Baye’s Theorem (PDF)  
7 Random Variables and Probability Distributions (PDF)  
8 Expectation, Functions of a Random Variable (PDF)  
9 Risk  
10 Some Common Probability Distributions (PDF) cdffit.m (M)
11 Multivariate Probability (PDF)  
12 Functions of Many Random Variables  
13 Populations and Samples (PDF)  
14 Estimation (PDF)  
15 Confidence Intervals (PDF)  
16 Testing Hypotheses about a Single Population (PDF)  
17 Testing Hypotheses about Two Populations (PDF)  
18 Small Sample Statistics (PDF)  
19 Analysis of Variance (PDF)  
20 Analysis of Variance (contd.) (PDF)  
21 Multifactor Analysis of Variance (PDF)  
22 Linear Regression (PDF)  
23 Analyzing Regression Results (PDF)  

Course Info

As Taught In
Fall 2003
Learning Resource Types
Problem Sets with Solutions
Exams with Solutions
Lecture Notes
Programming Assignments with Examples