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  • Statistics 10, Lecture 2 — Introduction to Statistical Reasoning — Fall 2007, R. Gould
    • Instructor: Rob Gould
    • Teaching Assistant-: Tess Nesbitt
    • Teaching Assistant-: Esa Eslami
    • Teaching Assistant-: Songfeng Zheng
    • Teaching Assistant-: Mine Cetinkaya
    This course allows guest users to enter  This course requires an enrolment key
    Lecture, three hours; discussion, one hour. Preparation: three years of high school mathematics. Not open for credit to students with credit for course 10A, 10H, 11, M12, 13, 14, Anthropology M80, Geography M40, or Sociology M18 (or former Statistics M11, M13, Economics M40, or Organismic Biology M22). Descriptive statistics, elementary probability, random variables, binomial and normal distributions. Large and small sample inference concerning means. P/NP or letter grading.
  • Statistics 10, Lecture 4 — Introduction to Statistical Reasoning — Fall 2007, M. Esfandiari
    • Instructor: Mahtash Esfandiari
    • Teaching Assistant-: Kevin Nichols
    • Teaching Assistant-: David Zes
    This course requires an enrolment key
    Lecture, three hours; discussion, one hour. Preparation: three years of high school mathematics. Not open for credit to students with credit for course 10A, 10H, 11, M12, 13, 14, Anthropology M80, Geography M40, or Sociology M18 (or former Statistics M11, M13, Economics M40, or Organismic Biology M22). Descriptive statistics, elementary probability, random variables, binomial and normal distributions. Large and small sample inference concerning means. P/NP or letter grading.
  • Statistics 112, Lecture 1 — Statistical Methods for Social Sciences — Fall 2007, R. Gould
    • Instructor: Rob Gould
    • Teaching Assistant-: Tun-Hsiang Yang
    • Teaching Assistant-: Kin Hing Phoa
    • Teaching Assistant-: Jiashen You
    This course allows guest users to enter  This course requires an enrolment key
    Lecture, three hours; discussion, one hour. Requisite: course 10. Limited to juniors/seniors. Statistical methods in social sciences, including regression, multivariate techniques, logistic regression, and data-handling and analysis. Applications to social sciences, using professional statistical analysis software package for data analysis. Letter grading.


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