Coming soon
Statistical Mathematics
This course is meant for students who do not have sufficient background of Mathematics. The students would be exposed to elementary mathematics that would prepare them to study their main courses that involve knowledge of Mathematics. The students would be exposed to the basic mathematical tools of real analysis, calculus, differential equations and numerical analysis.
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Course |
Learning outcomes (at course level |
Learning and teaching strategies |
Assessment Strategies
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Paper Code |
Paper Title |
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STT-121 |
Statistical Mathematics |
CO 1: Understand the concept of a vector space, base and dimension of vector space and linear transformation of vectors.
CO 2: Use the concept of matrices, rank, characteristic roots, Cayley Hamilton theorem and applications.
CO 3: Define special matrices and solve problems related to Quadratic forms.
CO 4: Understand the concept of sequence, series, continuity and differentiability of real numbers and maxima-minima of functions.
CO 5: Understand numerical methods related to differentiation, integration and finding numerical solution of nonlinear equations and ordinary differential equations.
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Approach in teaching:
Interactive Lectures, Group Discussion, Classroom Assignment Problem Solving Sessions
Learning activities for the students:
Assignments Seminar Presentation Subject based Activities
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Classroom Quiz Assignments Class Test Individual Presentation |
Vector space, sub space, linear combination of vectors, linearly dependent and independent vectors, basis and dimension, linear transformations of vectors, nullity and rank of linear transformation (Sylvester Theorem), Algebra of linear transformations(elementary properties).
Matrix: Basic terminology, row and column space, Echleon form, determinants, rank and inverse of matrix, characteristics roots and vectors, Caley- Hamilton Theorem and applications.
Special matrices: idempotent, orthogonal and symmetrical, reduction of a real symmetric matrix to a diagonal form. Quadratic forms: definition, reduction and classification, simultaneously reduction of two quadratic forms, maxima- minima of ratio of quadratic form
Real analysis: sequence and their convergence, real valued function, continuous function, discontinuity: Borel covering theorem, Boundness theorem and moistest theorem, differentiation, maxima and minima of one variable function.
Numerical integration, trapezoidal, Simpsons 1/3 and 3/8 rule, solution of system of linear equations: Gauss estimation, Jacobi, Gauss-Seidel method. Numerical solution of non linear equations: Bisection method, Regula-Falsi method, Method of Iteration, Newton Rapson method. Numerical solution of ordinary differential equation: Runge-Kutta method.
. Apostol, T.M. (1985):Mathematical Analysis, Narosa Publishing House.
2. Burkill,J.C.(1980):A first Course in Mathematical Analysis, Vikas Publishing House.
3. Cournat, R.and John, F. (1965): Introduction to Calculus and Analysis, John Wiley.
4. Khuri,A.l(1983): Advanced Calculus with Applications in Statistics, John Wiley.
5. Miller,K.S.(1957): Advanced Real Calculus, Harper, New York.
6. Sastry S.S. (1987): Introductory Methods of Numerical Analysis, Prentice Hall.
7. Saxena,H.C (1980).: Calculus of Finite Difference, S. Chand & Co.
8. Searle, S.R.(1982): Matrix Algebra Useful for Statistics, John Wiley
9. Shanti Narayan,(1998):A Textbook of Matrices , S. Chand & Co.
10. Harville, DA. (1997): Matrix Algebra from a Statistician’s Perspective, Springer.
11. Searle, SR. 1982. Matrix Algebra Useful for Statistics, John Wiley.
12. Rao, A.R. and Bhimasankaram, P(1992) : Linear algebra, Tata –McGraw-Hill Publishing Co. Ltd.
13. Rao,C.R., Mithra,S.K. (1971) : Generalized inverse of matrices and its applications, John Wiley & Sons Inc.
