1-3 | Distributions derived from the normal distribution; the method of maximum likelihood | |
4-6 | Binomial confidence intervals | Problem set 1 due in Ses #4 |
7-9 | Linear least squares | Problem set 2 due in Ses #7 |
10, 11 | Estimation of parameters and fitting of probability distributions | Problem set 3 due in Ses #10 |
12 | Review for exam 1 | |
13 | Exam 1 | |
14, 15 | Estimation of parameters and fitting of probability distributions (cont.) | |
16-18 | Testing hypotheses and assessing goodness of fit | Problem set 4 due in Ses #16 |
19-21 | The analysis of variance | Problem set 5 due in Ses #19 |
22, 23 | The analysis of categorical data | Problem set 6 due in Ses #22 |
24 | Review for exam 2 | |
25 | Exam 2 | |
26 | The analysis of categorical data (cont.) | |
27-29 | Summarizing data | Problem set 7 due in Ses #27 |
30-32 | Comparing two samples | Problem set 8 due in Ses #30 |
33, 34 | The Bayesian approach to parameter estimation | Problem set 9 due in Ses #33 |
35 | Review for exam 3 | |
36 | Exam 3 | |
37, 38 | Comparing two independent samples (Bayesian approach) | Problem set 10 due two days after Ses #38 |