WEEK: 
DATE: 
TOPIC: 
EXTRA NOTES: 
READING: 
0 
Sept. 26 
Introduction, Probability , Discrete RVs [PDF]


G 12.1, L 1.1 

Sept. 28 
Tutorial (in LAW) 


1 
Oct. 1 
Continuous RVs, PDFs, CDFs, Mixtures [PDF] 

G 2.22.4, L 1.21.3 

Oct. 3 
Functions of RVs [PDF] 
[Transformations] 
G 2.53.1, L 1.4 

Oct. 5 
Computer Lab (in MGH): Intro to R 

[Notes] 
2 
Oct. 8 
Moments, MGFs [PDF] 

G3, L1.4, L 4.1 (or 4.3 
"Representing Distributions"),
R 4.5
*** 

Oct. 10 
Bivariate Distributions [PDF] 
[MGFs] 
G4 & 7, L1.5 

Oct. 12 
Tutorial (in LAW) 


3 
Oct. 15 
Bivariate Distributions Con't. [PDF] 

L 5.7.2 

Oct. 17 
Conditioning and Independence [PDF] 

G 4.3, 6.2,6.3; R 3.13.5 
 Oct.19 
Tutorial (in LAW) 


4
 Oct.22 
Conditional Expectation, Normal Transformations
[PDF] 

G 5.35.5, 7.37.5; L 5.7.2 (not 2.7.2, which doesn't
exist);
R 4.4 
 Oct.24 
Convergence: LLN, CLT [PDF] 
[Matrix Method] 
G 9.19.3; R5; L 1.9 
 Oct.26 
Computer Lab (in MGH) 
[R notes] 

5
 Oct.29 
CLT and Confidence Intervals [PDF] 

G 8 ; R 6 
 Oct.31 
catchup?/REVIEW (on blackboard) 


 Nov. 2 
Tutorial (in LAW) 


6
 Nov.5 
Likelihood (no handout) 
[Notes] 
G 11 & 12; L 2.3 & 2.5; R 8 (but not 8.7) 
 Nov.7 
Midterm 


 Nov.9 
No tutorial. 


7
 Nov.12 
Holiday 


 Nov.14 
Likelihood con't. 
updated above link 

 Nov.16 
Computer Lab (in MGH) 
[data] [notes] 

8
 Nov.19 
Hypothesis Testing 
[Notes] 
R 9 
 Nov.21 
Hypothesis Testing con't. 


 Nov.23 
HOLIDAY 


9
 Nov.26 
Hypothesis Testing con't. 
See above notes  they've been updated. 

 Nov.28 
Bayesian Inference 

L 2.5; R 15 (parts of, we're avoiding the decision theory
stuff) 
 Nov.30 
Tutorial (in MGH) 


10
 Dec.3 
Bayesian Inference con't. 
[Notes] 

 Dec.5 
Empirical CDF & the nonparametric bootstrap;
Review 
[Notes]
I will bring a copy for everyone tomorrow! 

 Dec.7 
Tutorial (in MGH) 

