Prerequisites: None
Contents:
The course covers the basics on probability and statistics. Topics include:
- Descriptive Statistics & Data Handling,
- Probability: Sample spaces & events, conditional probability, Notions of independent events, Bayes Theorem, Discrete and Continuous random variables; probability mass functions, density functions, distributions, Expectations & Variance & their properties, covariance, correlation
- Conditional random variables, conditional expectations, joint and marginal distributions, laws of large numbers, Central Limit Theorem
- Some of the useful & common Distributions such as Bernoulli, Binomial, Poisson, Exponential, Uniform, Normal
- Statistical inference basics, including parameter estimation, hypothesis testing (t-test, chi-square tests)
- Introduction to time series data
- Examples and use of spreadsheets / software for computations
- Applications to business decisions
References
- Sheldon Ross, Introduction to Probability and Statistics for Engineers and Scientists, 5th Edition, Academic Press, 2014.
- Maurice DeGroot and Mark Schiverser, Probability and Statistics, 4th edition, Pearson, 2011
- Dennis Wackerly, William Mendenhall, and Richard L. Scheaffer (2007) Mathematical Statistics with Applications, 7th edition, Duxbury Resource Center.