## Details of MA2201 (Spring 2017)

 Level: 2 Type: Theory Credits: 3.0

Course CodeCourse NameInstructor(s)
MA2201 Probability and Statistics Asok Kumar Nanda

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. G. Casella and R. L. Berger, Statistical Inference, Thomson Brooks / Cole, 2002.

2. P. G. Hoel, S. C. Port and C. J. Stone, Introduction to Probability Theory, Thomson Brooks / Cole, 1972.

3. N. Pal and S. Sarkar, Statistics : Concepts and Applications, Prentice Hall, 2005.

4. S. Ross, First Course in Probability, (8th Edition), Prentice-Hall, 2009.

#### Course Credit Options

Sl. No.ProgrammeSemester NoCourse Choice
1 IP 2 Not Allowed
2 IP 4 Not Allowed
3 IP 6 Not Allowed
4 MR 2 Not Allowed
5 MR 4 Not Allowed
6 MS 4 Core
7 RS 1 Not Allowed
8 RS 2 Not Allowed