STAT4101 Schedule
| Date | Topic | Objective | |
| Wed. Sep 6 | Description of flow of course
Sample Spaces (1.1) | Understand the general objectives and flow of the course.
Know what a sample space is and how to enumerate one. | |
| Fri. Sep 8 | Events (1.2) | Be able to use Event Algebra (eg. Inclusion, Complement, Intersection, Union).
Know what a partition is and how to recognize and create them. | |
| Mon. Sep 11 | Equally likely outcomes (1.3)
Counting (1.4) | Be able to use the multiplication rule, permutations, and combinations to count equally likely outcomes. | |
| Wed. Sep 13 | Sampling (1.5)
Binomial Coefficients (1.6) | Understand the distinction between, and be able to recognize, both sampling with
and without replacement and whether or not order matters.
Be able to calculate probabilities of sampling events by counting. Know that you've heard of Binomial Coefficients; you'll see them again. | |
| Fri. Sep 15 | Properties of Probability (1.7)
Probability Misconceptions (link to quiz) | Understand the Axioms of Probability and be able to use them to show other properties of probability.
Improve your Probabilistic Intuition. | |
| Mon. Sep 18 | Probability Distributions (2.1) | Understand what a random variable is and how it is related to sample spaces and events.
Know how to read and write probability functions. | |
| Wed. Sep 20 | Joint Distributions (2.2)
Conditional Distributions (2.3) | Know how to read and write joint probability functions.
Understand how to form conditional pf's from joint pf's, and joint pf's from conditional and marginal pf's. | |
| Fri. Sep 22 | Bayes Theorem (2.4)
Independent Random Variables (2.5) | Know when and how to apply Bayes Theorem.
Understand the concept of independence, and know how to determine if two random variables are independent. Also be able to form a joint from marginals when independence is assumed. | |
| Mon. Sep 25 | Independence of More than Two Random Variables, and of Events (2.5)
More Examples of Conditional Probability and Independence | Be able to determine independence of more than two random variables.
Understand how independence of events is different than independence of random variables. See more examples and improve your intuition. | |
| Wed. Sep 27 | Expected Values (3.1, 3.2) | Be able to calculate expected values of random variables and of functions of random variables
(including functions of more than one random variable).
Be apply to apply the property of linearity. | |
| Fri. Sep 29 | Continue Expected Values (3.1, 3.2)
Variability (3.3) | Be able to calculate conditional properties and use the rule of iterated expectations.
Expected value of the product of independent variables. Be able to calculate the variance and standard deviation of a random variable. | |
| Mon. Oct 2 | Covariance and Correlation (3.4) | Be able to calculate covariances and correlations. | |
| Wed. Oct 4 | Sums of Random Variables (3.5) | Find variances and covariances of sums of random variables. | |
| Fri. Oct 6 | Probability Generating Functions (3.6) | Know how to convert a probability distribution function into a probability generating function, and vice versa. | |
| Mon. Oct 9 | Review for Exam | ||
| Wed. Oct 11 | Exam 1: Covers Chapters 1-3 |
| Fri. Oct 13 | Exam Solutions
Bernoulli (4.1) | |
| Mon. Oct 16 | Binomial (4.2), Hypergeometric (4.3) | |
| Wed. Oct 18 | Inverse Sampling; Geometric (4.4) | |
| Fri. Oct 20 | Negative Binomial, Negative Hypergeometric (4.4)
Introduced Poisson (4.6) | |
| Mon. Oct 23 | Poisson (4.6) | |
| Wed. Oct 25 | Multinomial (4.8)
Applying the PGF (4.9) | |
| Fri. Oct 27 | Distribution Function ("big" F) (5.1)
Density Function ("little" f) (5.2) | |
| Mon. Oct 30 | Jumps in big F
Transforming Variables | |
| Wed. Nov 1 | Exponential Distribution (6.2)
Percentiles (5.3) Expected Values (5.4-5.6) | |
| Fri. Nov 3 | Bivariate (5.7) and Multivariate (5.8) Distributions | |
| Mon. Nov 6 | Multivariate Calculus, as needed for this class | |
| Wed. Nov 8 | Covariance, Correlation (5.9), Independence (5.10) | |
| Fri. Nov 10 | Review for Exam | |
| Mon. Nov 13 | Review for Exam | |
| Wed. Nov 15 | Exam 2: Covers Chapter 4, 5.1-5.10, and 6.2 | |
| Fri. Nov 17 | Conditional Distributions (5.11) | |
| Mon. Nov 20 | Moment Generating Functions (5.12)
Gamma Distributions (6.3) | |