Computer Oriented Numerical and Statistical Lab

Paper Code: 
MCA 428
Credits: 
04
Periods/week: 
04
Max. Marks: 
100.00
Objective: 

 The course will enable the students to

  1. Gain an experience in the implementation of numerical methods using a computer
  2. Learning  of Statistical methods and its applications using computer
  3. Trace error in these methods and need to analyze and predict it using program  
  4. Provide knowledge of various significant and fundamental concepts to inculcate in the students an adequate understanding of the application of Statistical Methods by practical exposure .
  5. Demonstrate the concepts of numerical methods used for different applications with the help of programs

 

Course Learning Outcomes (CLOs):

 

Learning Outcome (at course level)

Students will be able to:

Learning and teaching strategies

Assessment Strategies

  1. Learning of making automated solution of numerical methods using C language .
  2. Using application of Statistical methods using Microsoft Excel.
  3. Discuss errors in methods ,Analyze and predict it using program 
  4. Demonstrate applications of Statistical Methods by practical exposure .
  5. Write and understand numerical methods in C language, constructs, syntax and semantics

 

Approach in teaching:

Interactive Lab Sessions,

Modeling, Discussions, implementing enquiry based learning, student centered approach

 

Learning activities for the students:

Experiential Learning, Discussions, Lab Assignments, Learning through Real life data centric problems

  • Lab Assignments
  • Practical Record
  • Continues Assessment
  • Semester End Examination

 

Solution of Nonlinear equations – Bi-section method, False Position method, Newton Raphson method, Secant method.

Solutions of system of Linear equations- Gauss Elimination method and pivoting, Gauss Seidal Method.

Interpolation and approximation- : Langrange’s Interpolation, Newton’s forward and backward formula, Newton Divided difference formula.

Numerical Differentiation and Integration:- Trapezoidal rule( for tabulated function and known function ), Simpson’s rules( 1/3 rule  and 3/8 rule ).

Curve Fitting- Fitting a straight line, Fitting a Polynomial, Fitting an exponential curves.

Solution of differential equations – Runge-Kutta method (second order and fourth order), Predictor-corrector method.

Academic Year: 
2019-20
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