BSc CSIT (TU) Science Numerical Method (BSc CSIT, CSC207) Question Paper 2078
This is the official BSc CSIT (TU) (Science stream) Numerical Method (BSc CSIT, CSC207) question paper for 2078, as set in the regular annual examination. It carries 60 full marks and a time allowance of 180 minutes, across 12 questions. On Kekkei you can attempt this Numerical Method (BSc CSIT, CSC207) past paper online with a timer, get instant AI feedback and step-by-step solutions, and track the topics where you lose marks — completely free. Whether you are revising for your BSc CSIT (TU) Numerical Method (BSc CSIT, CSC207) exam or solving previous years' question papers, this 2078 paper is a great way to practise under real exam conditions.
Section A: Long Answer Questions
Attempt any TWO questions.
What is curve fitting? Explain the least squares method to fit a straight line y = a + bx to a given set of data points.
Explain the finite difference method for solving partial differential equations. Derive the standard five-point formula for Laplace's equation.
Explain the bisection method for finding the root of a non-linear equation. Find a real root of the equation x^3 - x - 1 = 0 correct to three decimal places using the bisection method.
Section B: Short Answer Questions
Attempt any EIGHT questions.
Explain Newton's divided difference interpolation formula.
Explain the power method for finding the largest eigenvalue of a matrix.
Fit a second-degree parabola y = a + bx + cx^2 to a set of data using the least squares principle.
Define absolute, relative and percentage errors. Explain the sources of errors in numerical computation.
Explain the secant method for finding the root of an equation and compare it with the Newton-Raphson method.
Describe the false position (regula falsi) method with its geometric interpretation.
State and explain Lagrange's interpolation formula with a suitable example.
Derive the trapezoidal rule for numerical integration and state its error term.
Explain the Gauss-Seidel iterative method to solve a system of linear equations.