Probability and Statistics

Paper Code: 
MAT325B
Credits: 
5
Contact Hours: 
75.00
Max. Marks: 
100.00
Objective: 

This course will enable the students to –

  1. Get knowledge about the foundations of probabilistic and statistical analysis. 
  2. Use probabilistic and statistical analysis in various applications in engineering and science.
  3. Get an idea about Random variable.

Course Outcomes (COs):

Course

Learning outcomes

(at course level)

Learning and teaching strategies

Assessment

Strategies

Course Code

Course Title

 

 

 

 

 

 

MAT 325B

 

 

 

 

 

Probability and Statistics

   (Theory)

 

 

 

 

The students will be able to –

 

CO122: Learn the concepts of random variables as outcomes of random experiments are introduced and the key properties of the commonly used standard univariate random variables are studied. Emphasis is placed on learning the theories by proving key properties of each distribution.

CO123: Students get a good understanding of exploratory data analysis.

CO124: A good understanding of elementary probability theory and its application.

CO125: Students get ideas about the discrete and continuous distribution..

CO126: Solve the application based problem related continuous distribution and curve fitting

CO127: Students understand Some special mathematical expectations and study Marginal and conditional distributions, the correlation coefficient.

 

Approach in teaching:

Interactive Lectures, Discussion, Power Point Presentations, Informative videos

 

Learning activities for the students:

Self learning assignments, Effective questions, presentations, Field trips

Quiz, Poster Presentations,

Power Point Presentations, Individual and group projects,

Open Book Test, Semester End Examination

 

 

 

 

Unit I: 
I
15.00

Classical theory of probability, Laws of total and compound probability, Conditional probability, Baye’s theorem and related problems, Random variable, Discrete and continuous random variables. 

Unit II: 
II
15.00

Distribution function, Probability mass function and probability density function, Bi-variate distributions, Conditional and marginal distributions, Conditional expectation and variance, Co-variance, Analysis of bi-variate data.

Unit III: 
III
15.00

Mathematical expectation and moment generating functions, Theoretical discrete distributions: Binomial and Poisson distributions with mean and variance, Poisson distribution as limiting case of binomial distribution.

Unit IV: 
IV
15.00

Theoretical continuous distribution: Normal distribution with its properties related problems, Fitting of curves: Principle of curve fitting, Fitting of straight line and second degree parabola by least squares method.

Unit V: 
V
15.00

Theoretical continuous distribution: Normal distribution with its properties related problems, Fitting of curves: Principle of curve fitting, Fitting of straight line and second degree parabola by least squares method.

Essential Readings: 
  • S.C. Gupta and V.K. Kapoor, Fundamentals of Statistics, S.Chand & Sons, 2014.
  • J.N. Kapoor and H.C. Saxena, Mathematical Statistics, S.Chand & Co. Publications, 2010.
  • . M. Chakravarthy, Handbook of Applied Statistics, Willey, 1967.
  • A. M. Mood and F. Graybill, Introduction to the Theory of Statistics, McGraw Hill, 2017.
  • B. Gnedenko, The Theory of Probability (MIR, Moscow), 6th Edition, 1998.
References: 
  • Pappu  Kousalya, Probability, Statistics and Random Processes, Pearson, 2013.
  • Vijay K. Rohatagi, A. K. Md. Ehsanes Saleh, An Introduction to Probability and Statistics, Wiley, Second edition, 2008.
  • William Feller, An Introduction to Probability Theory and its Applications, Vol. 1, Wiley, Third edition, 2008.
  • A.M. Gun, M.K. Gupta and B. Dasgupta, Fundamentals of Statistics-Vol-II, World Press, 2016.
 
Academic Year: 
2023-2024
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