Regression and Analysis of Variance
Preface
1
Day 1 (June 3)
1.1
Welcome and preliminaries
1.2
Assignment 1
2
Day 2 (June 4)
2.1
Announcements
2.2
Intro to statistical modelling
3
Day 3 (June 5)
3.1
Announcements
3.2
Matrix algebra
3.3
Introduction to linear models
4
Day 4 (June 6)
4.1
Announcements
4.2
Introduction to linear models
4.3
Estimation
4.4
Loss function approach
5
Day 5 (June 7)
5.1
Announcements
5.2
Estimation
5.3
Loss function approach
6
Day 6 (June 10)
6.1
Announcements
6.2
Estimation
6.3
Loss function approach
7
Day 7 (June 11)
7.1
Announcements
7.2
Estimation
7.3
Loss function approach
8
Day 8 (June 12)
8.1
Announcements
8.2
Loss function approach
9
Day 9 (June 13)
9.1
Announcements
9.2
Loss function approach
9.3
Maximum Likelihood Estimation
10
Day 10 (June 14)
10.1
Announcements
11
Day 11 (June 17)
11.1
Announcements
11.2
Maximum Likelihood Estimation
11.3
Confidence intervals for paramters
12
Day 12 (June 18)
12.1
Announcements
12.2
Maximum Likelihood Estimation
12.3
Confidence intervals for paramters
13
Day 13 (June 20)
13.1
Announcements
13.2
Confidence intervals for derived quantities
14
Day 14 (June 21)
14.1
Announcements
14.2
Confidence intervals for derived quantities
14.3
Bootstrap confidence intervals
15
Day 15 (June 24)
15.1
Announcements
15.2
Confidence intervals for derived quantities
15.3
Bootstrap confidence intervals
15.4
Prediction
15.5
Intervals for predictions
16
Day 16 (June 25)
16.1
Announcements
16.2
Prediction
16.3
Intervals for predictions
17
Day 17 (June 26)
17.1
Announcements
18
Day 18 (June 27)
18.1
Announcements
18.2
Prediction
19
Day 19 (June 28)
19.1
Announcements
19.2
Regression and ANOVA
19.3
T-test
19.4
ANOVA/F-test
20
Assignment 1
21
Assignment 2
22
Assignment 2 (Guide)
23
Assignment 3
24
Final project
24.1
Grading Rubric
24.2
Examples of A and A+ quality work from a similar class
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Regression and Analysis of Variance
10
Day 10 (June 14)
10.1
Announcements
In-class workday