MATH-231 / 4 credits

Teacher: Goldstein Darlene

Language: English


Summary

Introduction to notions of probability and basic statistics.

Content

  • Descriptive statistics
  • Combinatorics
  • Probability density and cumulative distribution function
  • Conditional probability and independence
  • Law of total probability, Bayes' rule
  • Discrete random variables, expected value and variance
  • Discrete laws: binomial, Poisson
  • Continuous random variables, expected value and variance
  • Continuous laws: uniform, normal, exponential
  • Transformations of random variables, standardization
  • Joint distributions
  • Central Limit Theorem
  • Confidence intervals
  • Maximum Likelihood estimation
  • Introduction to hypothesis testing

Learning Outcomes

By the end of the course, the student must be able to:

  • Demonstrate understanding of course material
  • Apply understanding to exercise/real life scenarios

Transversal skills

  • Use a work methodology appropriate to the task.

Teaching methods

Lectures and group exercises

Expected student activities

Students should be prepared to participate in their learning by participating during lecture, asking questions, and contributing to exercise sessions

Assessment methods

Written

In the programs

  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Probability and statistics I
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Type: mandatory
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Probability and statistics I
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Type: mandatory

Reference week

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