| 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 |
