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ANNOUNCEMENTS

    You can view your letter grades which are posted on my door.


    You can view your final exam results which are posted on my door.
    Each of the three questions are graded seperately. Top score is Gizem Ipek: 100-100-90

    14 May Tue: We will finish Hypothesis Testing
    16 May Thu: Least Squares Method
    21 May Tue: MIDTERM 2 Discussions; Physical meaning of regression, testing for linearity
    23 May thu: Reduction in variance
REVIEW MATERIAL, HWs
  • Chapter 1 Due Feb 28 Thu
  • Chapter 2 Due March 7 Thu
  • HW 3.a Chapter 3: 7,8,9,15,21 Due March 14 Thu
  • HW 3.b Chapter 3: 31,40, 64,66,67 Due March 21-25
  • HW 4 Chapter 4: 1,2,3,5,10,15,21,35,36,37,39 Due March 26-April 2
    • SCHEDULE
      Lectures: Tue 15:50-17:30, Thu 12:40-13:30
      Office Hrs: Thu 14:30-16:30 or by appointment
      TEXT BOOK(S)
    • Milton and Arnold, "Introduction to Probability and Statistics", McGraw Hill, 1995 (Ch. 1-8, Chapter 11)
      EXAMS and GRADING POLICY

      Grading
      %25 MT 1 (selected topics; closed all materials) %25 MT 2 (selected topics; closed all materials) %35 Final Exam (comprehensive; closed all materials) %15 HWs

      Make Up Exam will be given after the final exam. It will replace the missed exam. It will be given only to those qualifying students with a valid excuse (please see academic rules and regulations)

    ASSIGNMENTS
    1. Assignments will also be posted on the web page. Please, regularly check the course web page for the homework assignments
    2. Assignments should be submitted directly to the TA by the due date
    3. No late assignments will be accepted
    4. I urge you to work on the assignments with a friend or two, however each student should submit the assignment individually
    5. Each answered question in an assignment will receive a +, the details will not be checked by the TA
    6. It is not the TA's responsibility to help you with the course, please see me for discussions
      OUTLINE

      Introduction: (~ 4 hrs) randomness in engineering, motivation to study probability and statistics

      Probability Theory: (~ 20 hrs)

      Basic concepts: possibilities and probability, elements of set theory, axioms of probability, conditional probability, independence, probability theorems

      Random variables and distributions: Probability mass, density and cumulative distribution functions, descriptors of a random variable: mean, mathematical expectation; variance

      Some useful distributions: Normal, binomial, geometric, Poisson, exponential and some others

      Multivariate distributions: Joint mass, density and cumulative distribution functions, marginal distributions; covariance, correlation coefficient, conditional mean and variance, independence and uncorrelatedness

      Functions of random variables: Sum and difference of normal variates, mean and variance of a general function

      Statistical Methods: (~ 14 hrs)

      Descriptive statistics: average value, standard deviation, histograms

      Statistical inference: estimation of parameters, central limit theorem, interval estimation of the mean and variance, hypothesis testing

      Model building: least squares method, introduction to regression and correlation analyses