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:

1. Remembering: Recalling the basic programming tools such as, variable declarations, array in one and two dimensions, for-loop, nested for-loop, if-else and repeated summation & multiplication.
2. Understanding: Describe how to write down a program. Explain the logic behind the different numerical tools.
3. Applying: Use different programming language to write the program for interpolation, integration, algebraic equations, system of linear equations and boundary value differential equations for large number of data and complicated functions
4. Analyzing: Analyze different real time problems and categorize them during the process of solving, by numerical method using programming language.
5. Evaluating: Justify and make gradation of above mentioned numerical tools and determine the appropriate program to find the optimal solution for multidisciplinary engineering problems.
6. Creating: Design a working model and build a path by which a new approach can be generated to create a new problem appreciated by academics, research & emerging direction in industry.

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

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