COURSE INFORMATION
Course Code: MM 391
Course Name: Statistics and Numerical Analysis lab
Contacts: 3P
Credits: 3
COURSE OUTCOME
At the end of this course, the incumbent will be able to:
PREREQUISITES
To understand this course, the incumbentmust have idea of:
§ Basic knowledge of computer
§ Basic knowledge of computer C- programming
SYLLABI
Statistics-measure of central tendency, dispersion,
Interpolation-Newtons Forward, Backward, Sterling & Bessel’s Interpolation formula, Lagrange's Interpolation Integration-
Trapezoidal, Simpson’s 1/3 rd, Weddel’s Rule, Romberg Integration, GaussLegendre two & three point formula, Newton Cotes Formula. Gram-Schmidt orthogonalisation, Tchebycheff polynomial Solution of transcendental equations- Method of Iteration, Method of Bisection, Newton - Raphson Method, Regula-Falsi method, Secant Method. Solution of system of linear equations- Gauss Elimination Method, Gauss-Jacobi, GaussSeidel, LU factorisation, Tri-diagonalisation. Inverse Interpolation. Least Square Curve fitting- linear & non-linear,
Solution of Differential Equations- Picard’s method, Euler-modified method,Taylor’s Series method, Runge-Kutta method, Milne’s Predictor-Corrector method.
BEYOND SYLLABI COVERAGE
Familiarization of the language “LINGO”. MatLab
LECTURE NOTE
LECTURE PLAN
HOMEWORK/ASSIGNMENT
RECOMMENDED READINGS
REFERENCES
Books: 1.Numerical Analysis, Shastri, PHI
2.Numerical Analysis, S. Ali Mollah
3.Numerical Analysis, James B. Scarbarough
4. .Numerical Methods for Mathematics ,Science & Engg., Mathews, PHI
5.Numerical Analysis,G.S.Rao,New Age International
6.Programmed Statistics (Questions – Answers),G.S.Rao,New Age International
7.Numerical Analysis & Algorithms, Pradeep Niyogi, TMH
8.Computer Oriented Numerical Mathematics, N. Dutta, VIKAS
9.Numerical Methods,Arumugam,Scitech
10.Probability and Statisics for Engineers,Rao,Scitech
11.Numerical Methods in Computer Application,Wayse,EPH