COMPUTER ORIENTED NUMERICAL & STATISTICAL METHODS

Solutions of Non Linear Equations: Absolute and Relative Error, Error Propagation, Methods for finding roots of non linear Equations: Bisection Method, False Position Method, Iterative Method, Newton Raphson method and secant method. Normalized floating point representation and Floating point Arithmetic.

Solutions Solution of system of linear equation: Definition, Difference between Direct and Iterative Methods, Gauss Elimination Method (Simple and with Pivoting), Gauss Seidel Method, Interpolation and Approximation: Finite Differences, Difference Tables, Polynomial Interpolation: Newton’s Backward and Forward Formula.

Central Difference Formulae: Gauss Forward and Backward Formula, Lagrange’s Interpolation, Newton’s Divided Difference formula, Numerical differentiation and Integration: Introduction, Trapezoidal Rule, Simpson’s rule, Solutions of differential equations: Picard’s method, Euler’s Method Runga Kutta Methods.

Frequency Distribution: Collection of Data, Classification of Data, Class Interval, Types of classes, Class Frequency, Class Mark, Class Boundaries, Width of a class, Frequency Density, Relative Frequency , Percentage Frequency and Cumulative Frequency.Methods of Central Tendency: Introduction, Arithmetic mean, Simple and weighted.For raw data, discrete frequency distribution, continuous frequency distribution, Median and Mode.

Correlation and Regression: Introduction to Correlation, Types of Correlation, Scatter Diagram Method and Correlation Coefficients.

Regression: Introduction, Regression analysis, Regression lines. Curve fitting and approximation: Method of least squares, fitting of straight lines, polynomials and curves.