Introduction to Mathematical Statistics
Statistics 200

• Practicalities
  • Logistics
    • Instructors
    • Email
    • Office Hours
    • Computer Lab :

      Lab_1 : Week of 1/24	Lab1sol.pdf
      Lab_2: Week of 2/7	Lab2sol.pdf
      Lab_3:Week of 3/5	Lab3sol.pdf
      Lab 4: review session	This week starting3/12/01

    • Exams and Assignments
    • Homeworks

      Homework 1:These exercices should be done by January 24th and handed in before lecture: Rice, Page 225-234. 1,8,19,22,45,50. 1,8,19,22,45,50.		Solutions: Page 1 Page 2
      Homework 2: Rice, Chapter 7, Page 226-234. 18,26,28,42,46,51(continues 50).		Solutions
      Homework 3: These exercices should be done by Monday, February 11th and handed in before lecture: Rice, Chapter 8, Page 293-296. 11,12,14,28,42.		Solutions
      Homework 4: These exercices should be done by Wednesday, February 21st and handed in before lecture: Rice, Chapter 8, Page 293-297. 38,47,51,53.		word file scanfile1 scanfile2
      Homework 5: These exercices should be done by Wednesday, March 7th and handed in before lecture: Rice, Chapter 8, Page 298. 54,56,57,58, 62.		Solutions
      Homework 6 :Beware, there is a typo, I gave the posteriro distributions that you have to find, the priors are those I gave in class, lecture 19. These exercices should be done by Friday, March 16th .		Solutions
      Lab 4: review session

  • A correction of Johan's office hour : In 2 (b) You are supposed to calcualte P(X=1).

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  Lecture summaries

  Lecture Summaries

  Review of Probability116	Lecture 1 -Paradigms,Bias	Lecture 2 Simple Random Sampling, Var(mean).
  Lecture 3 Estimation of Var(mean).	Lecture 4 Sampling Distribution, Normal Approximation,Confidence Interval.	Lecture 5 Estimating a ratio, Approximation methods, Delta method.
  Lecture 6 Expectation and mean of a variance.	Lecture 7 Stratified Random Sampling, optimal allocation.	Lecture 8 Parameter Estimation, Moment Generating Functions
  Lecture 9 Method of Moments	Lecture 10 Maximum Likelihood,mulitinomial.	Lecture 11 MLE for Hardy Weinberg.
  Lecture 16 Efficiency,Cramer Rao Bound.	Lecture 17 Sufficiency, exponential families.	Lecture 18. Bayesian Paradigm.
  Lecture 19 Examples of Bayesian computations	Lecture 20 Hypothesis Testing.	Lecture 21 Neyman Pearson Lemma.
  Lecture 22 Testing the multinomial, examples.	Tests-summary	. .Lecture 23. Some Pivotals.

  General links
  • By topic used in class
    • Matlab Based Books
  
  

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  Susan Holmes
  2001-03-16