Numerical Techniques & Statistical Methods

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NUMERICAL TECHNIQUES & 2141703
FREE PDF 2141703
Code: 2141703 Credits: 5 Semester 4 Biomed

Numerical Techniques & Statistical Methods

Get the full GTU study material, English & Gujarati textbook PDFs, syllabus curriculum, and solved previous year question papers.

Numerical Techniques & Statistical Methods Syllabus — Unit-wise Content

The official GTU curriculum for Numerical Techniques & Statistical Methods (subject code 2141703) as prescribed for Biomed · Semester 4 divides the course into 10 units, covering 57 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.

Download the official syllabus PDF (2141703)

Unit 1 ERROR ANALYSIS

2 lecture hours

  • Round-off errors and truncation errors in numerical computation
  • error propagation, and numerical instability

Unit 2 SOLUTION OF EQUATIONS AND EIGENVALUE

5 lecture hours

  • PROBLEMS Linear interpolation (method of false position) – Newton’s method - Statement of fixed point theorem – Fixed point iteration: x=g(x) method – Solution of linear system by Gaussian elimination and Gauss-Jordon method – Iterative methods: Gauss Jacobi and Gauss-Seidel methods – Inverse of matrix by Gauss Jordon method – Eigen value of matrix by power method

Unit 3 INTERPOLATION AND APPROXIMATION

5 lecture hours

  • Lagrangian Polynomials – Divided differences – Interpolating with a cubic spline – Newton’s forward difference formulas

Unit 4 NUMERICAL DIFFERENTION AND INTEGRATION

5 lecture hours

  • Derivatives from difference tables – Divided differences and finite differences – numerical integration by trapezoidal and Simpson’s 1/3 and 3/8 rules – Romberg’s method – Two and Three point Gaussian quadrature formulas – Double integrals using trapezoidal and Simpson’s rules

Unit 5 INITIAL VALUE PROBLEMS FOR ORDINARY

5 lecture hours

  • DIFFERINTIAL EQUATIONS Single step methods: Taylor series method – Euler and modified Euler methods – Fourth order Runge-Kutta method for solving first and second order equations – Multistep methods: Milne’s and Adam’s predictor and corrector methods

Unit 6 INTRODUCTION TO STATISCTICAL PARAMETERS

2 lecture hours

  • Significant figures
  • scientific notations
  • average- Mean
  • geometric mean
  • harmonic mean
  • root- mean-square and root-sum-squares average
  • Logarithmic representation of signal levels
  • Data classes
  • Variation – Gaussian curve
  • standard deviation

Unit 7 PROBABILITY AND PROBABILITY DISTRIBUTIONS

6 lecture hours

  • Introduction to probability
  • Random Experiments
  • Sample Space
  • Events and their probabilities
  • Some basic results of probability
  • Conditional probability
  • Random variables: Probability distributions
  • Expected value & variance of a probability distribution
  • Discrete probability distributions: Binomial
  • Continuous probability distributions: Exponential

Unit 8 SAMPLING, SAMPLING DISTRIBUTION &

6 lecture hours

  • INTERVAL ESTIMATION Simple random sampling
  • point estimation
  • introduction to sampling distributions
  • sampling distributions of, Sampling distribution of sample proportion
  • Properties of point estimation
  • Other sampling methods
  • Interval estimation: Population mean: σ known
  • Determining the sample size
  • Sampling distribution of variance

Unit 9 STATISTICAL INFERENCES, TESTING OF

8 lecture hours

  • -TEST Introduction
  • Test of significance for large samples: Difference between small & large samples
  • Two-tailed test for difference between the means of two samples
  • Standard error of the difference between two standard deviations
  • Test of significance for small samples: The assumption of normality
  • properties and application of t-distribution
  • testing difference between means of two samples (Independent samples
  • Dependent samples) Definition of chi-square
  • degrees of freedom
  • chi- square distribution
  • Conditions for applying chi-square test
  • Uses of chi-square test
  • Misuse of chi-square test

Unit 10 NETWORK ANALYSIS

4 lecture hours

  • Network definition
  • Minimal spanning tree problem
  • Shortest route problem
  • Maximum flow problem concepts and solution algorithm as applied to problems
  • Project planning and control by CPM network
  • Probability assessment in PERT network
  • PROJECT MANAGEMENT AND SCHEDULING Project management (CPM & PERT) Network concepts
  • rules for network construction
  • critical path method (CPM) and Project evaluation and review Techniques (PERT)

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