Motivation, Probability Models, Sample Space, Events, Algebra of Events, ProbabilityAxioms, Combinatorial Problems, Conditional Probability, Independence of Events, BayesRules
Introduction, Random variables types ,functions of random variables, Probability massfunctions, The Probability distribution functions, cumulative distribution function, expectedvalues of x, moments ,moment generating function , Discrete Distributions, binomialdistribution, Poisson distribution, Geometric distribution, continuous distribution ,normaldistribution, exponential distribution
Introduction, Least-squares Curve Fitting, The Coefficients of Determination,Confidence Intervals in Linear Regression, Trend Detection and Slop estimation,Correlation Analysis, Simple Non-Linear Regression.
Introduction, bisection, Newton’Raphson
Elementary row operation, Rank of a matrix, consistency of system of linear equationsSolutions of system linear equations – Gauss elimination, Gauss seidel iterative method
Trapezoidal rule, Simpsons 1/3 rd rule Simpsons 3/8 th rule