Regression and Analysis of Variance
Preface
1
Day 1 (June 9)
1.1
Welcome and preliminaries
1.2
Assignment 1
2
Day 2 (June 10)
2.1
Announcements
2.2
Intro to statistical modelling
3
Day 3 (June 11)
3.1
Announcements
3.2
Matrix algebra
4
Day 4 (June 12)
4.1
Announcements
4.2
Introduction to linear models
4.3
Estimation
4.4
Loss function approach
5
Day 5 (June 13)
5.1
Announcements
6
Day 6 (June 16)
6.1
Announcements
6.2
Estimation
6.3
Loss function approach
7
Day 7 (June 17)
7.1
Announcements
7.2
Estimation
8
Day 8 (June 18)
8.1
Announcements
8.2
Estimation
8.3
Maximum Likelihood Estimation
9
Day 9 (June 20)
9.1
Announcements
10
Day 10 (June 23)
10.1
Announcements
10.2
Estimation
10.3
Maximum Likelihood Estimation
11
Day 11 (June 24)
11.1
Announcements
11.2
Maximum Likelihood Estimation
12
Day 12 (June 25)
12.1
Announcements
13
Day 12 (June 26)
13.1
Announcements
13.2
Maximum Likelihood Estimation
13.3
Confidence intervals for paramters
14
Day 13 (June 27)
14.1
Announcements
14.2
Confidence intervals for paramters
14.3
Confidence intervals for derived quantities
15
Assignment 1
16
Assignment 2
17
Assignment 2 (Guide)
18
Assignment 3
19
Final project
19.1
Grading Rubric
19.2
Examples of A and A+ quality work from a similar class
20
Literature cited
Published with bookdown
Regression and Analysis of Variance
12
Day 12 (June 25)
12.1
Announcements
Work day