Statistics 110

Statistical Methods in Engineering and the Physical Sciences

Stanford University, Autumn Quarter 2006-2007

jinome.stanford.edu/stat110

Dr. Balaji S. Srinivasan

Announcements

Basic course information

Units: 4-5

Lectures: Monday to Thursday, 11:00 - 11:50 am, Building 300-300 (map).

Instructor: Dr. Balaji S. Srinivasan

Office hours: Monday 9:00 - 11:00 am, Clark Center S251

Teaching assistants:

Please ask HW questions on the blog as your fellow students may have already asked the same question!
  • Wai Wai Liu wailiu_at_stanford_dot_edu, Sequoia 227
  • Li Ma ma2_at_stanford_dot_edu, Sequoia 231
  • Feng Zhang zf6234_at_stanford_dot_edu, Sequoia 238
  • Shaojie Deng alexdeng_at_stanford_dot_edu, Sequoia 208
  • Li Jin lijin_at_stanford_dot_edu, Sequoia 229

    • TA office hours:

    TA office hours are held either in TA offices or in the Conference Room on the 2nd floor of Sequoia.

    Contact information: The primary medium of interaction will be the web page and class blog (see right hand side). Staff may also send out announcements to stat110-aut0607-staff@lists.stanford.edu.

    Textbook and optional references: The textbook is John Rice's Mathematical Statistics and Data Analysis and lecture notes will be available from the class web page. Several texts can serve as auxiliary or reference texts.

    Course requirements:

    Homework: Homework will normally be assigned each Friday and due the following Friday by 5pm.

    You are allowed, even encouraged, to work on the homework in small groups, but you must write up your own homework to hand in. The homework will be discussed in the weekly problem sessions on Thursday. Homework will usually involve some simple R programming (no previous knowledge of R is necessary.) Any coding assignments must include a printout of the full source code when handed in. Homework will be graded on a 100 point scale.

    Grading: Homework 50%, midterm 25%, final 25%. These weights are approximate; we reserve the right to change them later.

    Prerequisites: Strong calculus background. Ideally you will have an exposure to basic probability (e.g. Stat 116), as we will move through this rapidly in order to get to statistics. Topics which you should have seen before: basic probability, random variables, joint distributions, expected values.

    Bulletin description: Introduction to statistics for engineers and physical scientists. Topics: descriptive statistics, probability, interval estimation, tests of hypotheses, nonparametric methods, linear regression, analysis of variance, elementary experimental design. Prerequisite: one year of calculus. GER:DB-Math.

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    Handouts

    Note: Homework is due by Friday at 5pm in the inbox across from Sequoia 229. The inbox is on the right hand side of the homework hand-in boxes.
    Late homework will receive a 10% penalty for each day that it is late. After solutions go out no credit will be given.

    Note #2: Lectures are big pdf files exported from Keynote, so download them rather than opening them in your browser.
    1. Stat 110 Overview and Syllabus
    2. HW 1 (out 9/25, due 9/29. Skip Problem 3, we will do it in HW2.)
    3. HW 1 Solutions (out 10/3, pick up in person)
    4. Week 1, Lecture 1 (Motivation, Administrivia) (web, ppt, static)
    5. Week 1, Lecture 2 (R Intro, Descriptive Stats) (web, ppt, static)
    6. Week 1, Lecture 3 (Counting, Axioms of Probability) (web)
    7. HW 2 (out 9/29, due 10/6)
    8. HW 2 Solutions (out 10/10, pick up in person)
    9. Week 2, Lecture 4 (More Counting & Sets, Definition of a Random Variable) (web, ppt, static)
    10. Week 2, Lecture 5 (Discrete RVs, PMF, CDF, Bernoulli, Binomial, Connections) (web, ppt, static)
    11. Week 2, Lecture 6 (Continuous RVs, Expectation, Examples) (web, ppt, static)
    12. HW 3 (out 10/6, due 10/13)
    13. HW 3 Solutions (out 10/17, pick up in person)
    14. Week 3, Lecture 7 (Functions of RVs, Examples, Expectation of Functions of RVs) (web, ppt, static)
    15. Week 3, Lecture 8 (More Expectation, Discrete Random Vectors, Data Frames, Joint/Marginal PMFs, Contingency Tables, Multinomial) (web, ppt, static)
    16. Week 3, Lecture 9 (Continuous Random Vectors, Joint/Marginal PDFs, Scatterplots, Multivariate Normal) (web, ppt, static)
    17. HW 4 (out 10/13, due 10/20)
    18. HW 4 Solutions (out 10/24, pick up in person)
    19. Week 4, Lecture 10 (Review of Random Vectors, Numerical Examples: Waiting Times and CDFs) (web, ppt, static)
    20. Week 4, Lecture 11 (Conditional Probability, Independence, Examples) (web)
    21. Week 4, Lecture 12 (Bayes' Rule, Continuous Conditioning, More Examples) (web)
    22. HW 5 (out 10/20, due 10/27 (to grade before midterm) or 10/30 (grade may come back only after midterm))
    23. HW 5 Solutions (out 10/30, pick up in person)
    24. Week 5, Lecture 13 (Mixed Distributions, Prior Probabilities, Conditioning on Multiple Variables, Conditional Independence and Expectation) (web)
    25. Week 5, Lecture 14 (Paradoxes in Conditional Probability: Boys and Girls, Monty Hall, Simpson's Paradox) (web)
    26. Week 5, Lecture 15 (Simpson's Paradox Finale, Continuous Income/Education Paradox, Bayesian Updating with Conditional Probability, Functions of N Random Variables (Discrete)) (web)
    27. Practice Midterm
    28. Practice Midterm Solutions
    29. Week 6, Lecture 16 (Gamma Function, Binomial Sum, Calculational Strategies for Functions of N Random Variables) (web )
    30. Midterm Review
    31. Week 6, Lectures 17 and 18 (Sampling Distributions, Mean and Variance of the Sample Mean, Covariance, Correlation, Signal Processing Example) (web)
    32. Midterm
    33. Midterm Solutions
    34. HW 6 (out 11/3, due 11/10)
    35. HW 6 Solutions (out 11/13)
    36. Week 7, Lecture 19 (Markov Inequality, Chebyshev Inequality, Law of Large Numbers, Central Limit Theorem) (web)
    37. Week 7, Lecture 20 (Normal Distribution Properties and Geometry, Male/Female Height Example, Application to Quantitative Genetics) (web)
    38. Week 7, Lecture 21 (Recap and Survey, Random Walks and Central Limit Theorem) (web)
    39. HW 7 (out 11/10, due 11/17)
    40. HW 7 Solutions (out 11/21)
    41. Week 7, Lecture 22 (Sampling Distributions II: Sampling Distributions related to the Normal) (web)
    42. Week 7, Lecture 23 (Gamma Distribution, Point Estimators, Interval Estimators, Bias and Variance of an Estimator) (web)
    43. Week 7, Lecture 24 (Estimation Methods: Method of Moments, MAP and ML Estimation, Bootstrap) (web)
    44. HW 8 (out 11/27, due 12/4)
    45. HW 8 Solutions (out 12/4)
    46. Week 8, Lecture 25 (Binary Classification, Hypothesis Testing, Confidence Intervals) (web)
    47. Week 8, Lecture 26 (Linear Regression) (web)
    48. HW 9/Practice Final (out 12/4, due 12/8)
    49. HW 9 Solutions (out 12/8)
    50. Week 8, Lecture 27 (Hypothesis Testing II) (web)
    51. Week 8, Lecture 28 (Data Analysis and Visualization) (web)
    52. Week 8, Lecture 29 (Final Review) (web)
    top of Statistics 110 page

    R files

    R files from lecture and for homework assignments.
    top of Statistics 110 page

    Acknowledgements

    This web page was adapted from Professor Stephen Boyd's EE263 site.