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New: Histograms midterm and final exam results

Julia Brettschneider

The course is based on summaries of lectures given by Bin Yu and Terry Speed in Spring 2001 which can be downloaded below. Since we are going a little slower than last year, the lecture's numbers do not match one-by-one, but the order remains the same. Problem sets, computer labs, and other handouts posted below are the ones handed out in class this semester. The material will be posted after it appeared in class, usually at the end of each week.

Information about the class:     (ps pdf)

Histograms midterm and final exam results:
NOTE: The midterm had a total of 100 points. The final exam had a total of 150 points. To make the two histograms comparable, the final exam results were rescaled to a total number of 100 points for the final exam histogram. To figure out where you are you have to rescaled your score accordingly.
(ps pdf)

Lecture notes (Summeries from Spring 2001):

Lecture 1 & 2: Events, probabilities, independence, rules...
(ps pdf)
Lecture 3: Bernoulli trials, random variables, distributions, expectation...     (ps pdf)
Lecture 4: Histograms, average, median, standard deviation...    (ps pdf)
Lecture 5: Sampling    (ps pdf)
Lecture 6: Sample mean (variance computations)    (ps pdf)
Lecture 7: Informal hypothesis testing    (ps pdf)
Lecture 8: Skipped
Lecture 9: Regression (file has empty pages; they were given as hand-out in class)    (ps pdf)
Lecture 10: Regression line, r.m.s. error     (ps pdf)
Lecture 11: Joint distributions     (ps pdf)
The transparencies I used Tuesday, 4/16 are a part of Week 2 Lecture 2 (Biological sequence analysis) from Terry Speed's Stat 246. They can be downloaded as ps, ppt, or ps file at www.stat.Berkeley.EDU/users/terry/Classes/s246.2002/index.html

Lecture 12: Regression and the Inheritance of Quantitative Traits     (ps pdf)
Lecture 13: Entropy and Information     (ps pdf)
Lecture 14: Likelihoods, Likelihood Ratios, Hyp. testing, Neyman-Pearson Lemma     (ps pdf)
Lecture 17: Maximum Likelyhood estimation (MLE)     (ps pdf)
Lecture 18: MLE, Goodness-of-fit     (ps pdf)

Some handouts:
(ps pdf)

Problem sets:
Set #1 - due Thursday, 2/7 in class
(ps pdf)
Set #2 - due Thursday, 2/28 in class (extended)     (ps pdf)
Set #3 - due Thursday, 3/14 in class     (ps pdf)
Set #4 - due Thursday, 4/4 in class     (ps pdf)
Set #5 - due Thursday, 4/18 in class     (ps pdf)
Set #6 - due Thursday, 5/9 in class     (ps pdf)

Computer labs:
Lab #1 - due Thursday, 2/21 (extended!) in class
(please turn in a print out, do not submit it electronically)
(ps pdf)
Ecoli data for Lab #1:
123nt
cacttt_gap
gcatgc_gap
gtattg_20kb_count
gtattg_5kb_count
purine100
purine1000
purine10000
tataat_20kb_count
tataat_5kb_count
tataat_gap

Lab #2 - due Thursday, 3/7 in class
(please turn in a print out, do not submit it electronically)    (ps pdf)
Microarray data for Lab #2:     ko8.lab
Powerpoint presentations on microarrays:
First part (introduction) see lecture notes for week 3 in
www.stat.Berkeley.EDU/users/terry/Classes/s246.2002/index.html
Second part (two projects): ppt

Lab #3 - Regression - due Tuesday, 4/29 in class
(please turn in a print out, do not submit it electronically)    (ps pdf)
Pearson data for Lab #3:
pearson.dat