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, Schaum’s ouTlines,
McGraw-Hill.
Prof. Ethem
ALPAYDIN
Hasan Ferit Eniser
Math
101.
· 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
3.