Introduction to probability theory. Random variables, expectation, variance and moment generating functions. Distributions: Bernoulli, binomial, uniform, Gaussian, exponential, Poisson, gamma. Introduction to statistical concepts. Sampling and sample statistics. Point and interval estimation. Hypothesis testing. Regression. Numerical and computational aspects of random variable generation, sampling, and estimation.
R. E. Walpole, R. H. Myers, S. L. Myers, K. Ye (2006) Probability and Statistics for Engineers and Scientists, Prentice-Hall (Pearson). (9th edition is available at BU Bookstore, but there is no significant difference in the last few editions, so if you find a copy of 5th edition or later, you should be fine).
Recommended: S. Lipschutz, J. Schiller (1998). Introduction to Probability and Statistics, Schaumís ouTlines, McGraw-Hill.
Prof. Ethem ALPAYDIN
∑† Introduction LectureNotes1
∑† Probability LectureNotes2
∑† Random Variables and Probability Distributions LectureNotes3
∑† Mathematical Expectation LectureNotes4
∑† Discrete Probability Distributions LectureNotes5
- Continuous Probability Distributions LectureNotes6
- Simulating Random Experiments LectureNotes7
∑† Sampling LectureNotes8
∑† Parameter Estimation LectureNotes9
∑† Hypothesis Testing LectureNotes10
∑† Regression LectureNotes11