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ANNOUNCEMENTS

SCHEDULE
    Lectures: Wed 8:40-10:30, Fri 11:40-12:30
    Office Hrs: Tuesdays and Thursdays 15:30-16:30 or by appointment
REVIEW MATERIAL, HWs
  • Chapter 1 Due Oct 10 Wednesday
  • Chapter 2 Due Oct 17 Wednesday
  • Chapter 3-4
  • Chapter 6: 1, 6, 7, 12, 13, 14, 15, 17, 18, 26, 27
  • Chapter 5: 8, 9, 10, 11, 15, 16, 17, 29, 36, 37, 38, 39, 40-a,40-b
  • Chapter 7: 1, 4, 9, 47, 48, 55, 62 Due December 5-7
  • Chapter 8: Sections 8.1,8.2,8.3, and the topics covered in the class (No homeworks)
  • Chapter 11: Sections 11.1, 11.4, and the topics covered in the class (No homeworks)
    •   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 %25 MT 2 %35 Final Exam %15 Attendance+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)

      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: (~ 18 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