ORF 245: Statistics

Lectures Based on

Mathematical Statistics and Data Analysis (by John A. Rice)

Lecture Slides:

Reading Assignment:


  1. Basics of probability including the definition of a sample space and probabilities of events defined on the sample space.
  2. Indepedent events.
  3. Random variables cumulative distribution function, and density function.
  4. The most common/important discrete and continuous random variables.
  5. Independent random variables.
  6. Joint distribution, marginal and conditional distributions.
  7. Bayesian Inference.
  8. Expectation, mean, and variance.
  9. Collections of independent identically distributed random variables.
  10. Sample mean and sample variance.
  11. Central Limit Theorem.
  12. Confidence intervals for the mean.
  13. Method of Moments (MoM) and Maximum Likelihood Estimators (MLE).
  14. Confidence intervals based on t-distribution.
  15. Hypothesis Testing.
  16. Linear Regression.

Throughout the course, we will use the computer language Matlab to perform computations on real-world data to illustrate the methods and ideas covered in the course. The data sets will span from historical stock market prices to temperature data needed to study climate change.

Course Info: