{"id":8903,"date":"2019-12-15T04:53:25","date_gmt":"2019-12-15T04:53:25","guid":{"rendered":"https:\/\/www.inspirenignite.com\/vtu\/statistics-and-numerical-methods-syllabus-for-vtu-be-2017-scheme-open-elective-2\/"},"modified":"2019-12-15T04:53:25","modified_gmt":"2019-12-15T04:53:25","slug":"statistics-and-numerical-methods-syllabus-for-vtu-be-2017-scheme-open-elective-2","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/vtu\/statistics-and-numerical-methods-syllabus-for-vtu-be-2017-scheme-open-elective-2\/","title":{"rendered":"Statistics and Numerical Methods Syllabus for VTU BE 2017 Scheme (Open Elective-2)"},"content":{"rendered":"

Statistics and Numerical Methods detail syllabus for various departments, 2017 scheme is taken from VTU<\/a> official website and presented for VTU students. The course code (17BM664\/17EI664), and for exam duration, Teaching Hr\/week, Practical Hr\/week, Total Marks, internal marks, theory marks, duration and credits do visit complete sem subjects post given below.<\/p>\n

For all other open elective-2 syllabus for vtu be 2017 scheme you can visit Open Elective-2 syllabus for VTU BE 2017 Scheme Subjects<\/a>. The detail syllabus for statistics and numerical methods is as follows.<\/p>\n

Module 1 :<\/h4>\n

Testing of Hypothesis- Large sample test based on Normal distribution for single mean and difference of means – Tests based on t, Students t-test, statistical significance p” value, lambaA2 and F distributions for testing means and variances – Contingency table (Test for Independency) – Goodness of fit.\n<\/p>\n

Module 2:<\/h4>\n

For complete syllabus and results, class timetable and more pls download iStudy<\/a>. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.<\/p>\n

Module 3:<\/h4>\n

Solution of Equations and Eigenvalue Problems- Newton Raphson method – Gauss elimination method – pivoting – Gauss Jordan methods – Iterative methods of Gauss Jacobi and Gauss Seidel -Matrix inversion by Gauss Jordan method – Eigen values of a matrix by power method.\n<\/p>\n

Module 4:<\/h4>\n

Interpolation, Numerical Differentiation and Numerical Integration- Lagranges and Newtons divided difference interpolations – Newtons forward and backward difference interpolation -Approximation of derivatives using interpolation polynomials – Numerical single and double integrations using Trapezoidal and Simpsons 1\/3 rules.\n<\/p>\n

Module 5:<\/h4>\n

For complete syllabus and results, class timetable and more pls download iStudy<\/a>. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.<\/p>\n

Course Outcomes:<\/h4>\n

After studying this course, students will be able to:<\/p>\n

    \n
  1. Perform hypothesis testing.<\/li>\n
  2. Apply completely randomized design for the experimental data classification.<\/li>\n
  3. Develop and implement iterative methods to solve algebraic and transcendental equations, simultaneous linear equations, and ordinary differential equations.<\/li>\n
  4. Apply the knowledge of finite differences, interpolation, numerical differentiation and numerical integration to different area.<\/li>\n
  5. Apply statistical and numerical methods for the analysis of simple and complex signals\/data related to projects, research and real world problems.<\/li>\n<\/ol>\n

    Question paper pattern:<\/h4>\n