Details of MA2201 (Spring 2013)
| Level: 2 | Type: Theory | Credits: 3.0 |
| Course Code | Course Name | Instructor(s) |
|---|---|---|
| MA2201 | Probability and Statistics | Satyaki Mazumder |
| Syllabus |
|---|
| Probability : Classical definition, and problems solved by elementary combinatorial methods; set theoretic definition of probability for discrete sample spaces; basic probability theorems (union of events/Booles inequality, etc.); independence of events, conditional probability, Bayes theorem; discrete probability distributions (binomial/ Poisson/ hypergeometric/ negative binomial); continuous probability distributions (exponential/ uniform/ normal); moments and moment generating function; basic limit theorems (Chebyshevs inequality/ weak law of large numbers/ normal approximation to binomial/ central limit theorem in iid case); joint distribution of two random variables (with more emphasis on the discrete case); ideas of conditional expectation and variance.
Statistics : Correlation and regression; simple random sampling with and without replacement, expectation and standard error of the sample mean and the sample proportion; maximum likelihood estimation; introduction to confidence intervals; concept of testing of hypothesis, notion of Type I and Type II errors, tests for mean and variance in one and two-sample cases, tests related to regression problems, test for population proportion. |
| References |
|---|
| 1. Casella, G. and Berger, R. L, Statistical Inference, Thomson Brooks / Cole, 2002.
2. Hoel, P. G., Port, S. C. and Stone, C. J., Introduction to Probability Theory, Thomson Brooks / Cole, 1972. 3. Pal, N. and Sarkar, S., Statistics : Concepts and Applications, Prentice-Hall, 2005. 4. Ross, S., First Course in Probability (8th Edition), Prentice-Hall, 2009. |
Course Credit Options
| Sl. No. | Programme | Semester No | Course Choice |
|---|---|---|---|
| 1 | IP | 2 | Not Allowed |
| 2 | IP | 4 | Not Allowed |
| 3 | MS | 4 | Core |
| 4 | RS | 1 | Not Allowed |