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Practical-I
This paper is designed so that the student get familiar with statistical software for solving the problems based on various mathematical operations and also how to deal and analyse the probability of different data.
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Course |
Learning outcomes (at course level) |
Learning and teaching strategies |
Assessment Strategies |
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Paper Code |
Paper Title |
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STT125 |
Practical-I (Practical)
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The students will be able to –
C CO20: Able to solve linear systems of equation. CO21: Deal with numerical differentiation and integration. CO22: Ability to apply numerical methods for differential equation. CO23: Evaluate the determinant and inverse of a matrix and also find the solution of matrix equation. CO24: Compute various measures of central tendencies, dispersion, moments, Skewness, kurtosis and to interpret them. CO25: Able to find the probabilities of various events. CO26: Understand the concept of conditional probability and independence of events. |
Approach in teaching:
Interactive Lectures, Discussion, Power Point Presentations, Informative videos
Learning activities for the students: Self learning assignments, Effective questions, presentations, Field trips
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Quiz, Poster Presentations, Power Point Presentations, Individual and group projects, Open Book Test, Semester End Examination
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1. Determinants - by row and column operations, by partitioning.
2. Inverses of a matrix - by row and column operations, by partitioning
3. Rank of a matrix
4. Solutions of matrix equations
5. Characteristic roots and vectors of a matrix
6. Interpolation using Lagrange's formula, Newton-Gregory formula
7. Interpolation using Newton's divided difference formula
8. Numerical differentiation using Newton's formula
9. Numerical differentiation using Lagrange's formula
10. Numerical integration using trapezoidal formula
11. Numerical integration using Simpson's one-third formula
12. Numerical integration using Simpson's three-eighth formula
13. Numerical integration using Runge Kutta Method
14. Coefficient of variation.
15. Calculation of raw moments, central moments, β1, β2 and γ1, γ2 coefficients, Sheppard's correction to moments.
16. Probability, Conditional Probability and Baye’s theorem
Note: Practical exercises will be conducted on computer by using MS-Excel/ Matlab/ SPSS/R.
