This course lays the foundation of probability distributions and sampling distributions, their application which forms the basis of Statistical Inference.
Course |
Course Outcomes |
Learning and teaching strategies |
Assessment Strategies |
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Course Code |
Course Title |
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24STT123 |
Probability Distributions (Theory) |
CO 13: Identify the behavior of the discrete population and sample and their distribution and apply it on real world problems. CO 14: Identify the behavior of the population and apply it on multiple variables problems. CO 15: Derive the continuous probability distributions of random variables and use these techniques to differentiate data and fit in real life scenarios. CO 16: Translate real-world sample problems into probability distributions and give an appropriate inference. CO 17: Analyze the behavior of the data and apply the appropriate test. CO 18: Contribute effectively in course-specific interaction. |
Approach in teaching: Interactive Lectures, Group Discussion, Classroom Assignment, Problem Solving Sessions.
Learning activities for the students: Assignments, Seminar, Presentation, Subject based Activities. |
Classroom Quiz, Assignments, Class Test, Individual Presentation. |
Bernoulli distribution, Binomial distribution (compound and truncated also), Poisson distribution (compound and truncated also)- moments, moment generating function, Cumulant generating function, characteristic functions, recurrence relations, properties, fitting of distributions.
Geometric distribution, Negative Binomial distribution, Hyper-geometric distributions, Power Series distribution- moments, moment generating function, cumulant generating function, characteristic functions, recurrence relations, properties, fitting of distributions.
Rectangular distribution, Normal distribution (truncated also), normal probability plot, Exponential distribution, Lognormal distribution, Multinomial of binomial and Poisson- moments, moment generating function, cummulant generating function, characteristic functions, recurrence relations, properties, fitting of distributions.
Triangular distribution, Gamma distribution (one and two parameter) , Beta distribution( I kind and II kind) Cauchy distribution (truncated also), Laplace distributions, Pearson’s distribution (Type I, IV and VI).
Chi-Square, t and F distributions (central and non-central) and their applications. Large sample test. Fisher’s Z distributions and their applications. Order statistics: their distributions and properties; joint and marginal distributions of order statistics, sampling distributions of range and median of univariate population.
ESSENTIAL READINGS:
· Goon, Gupta & Das Gupta. (2003): Outline of Statistical Theory. Vol. I, World Press.
· Hogg, R.V. and Craig, A.T. (2009): Introduction to Mathematical Statistics, McMillan.
· Johnson, S. and Kotz. (1972): Distribution in Statistics, Vol.I, II. And III, Houghton and Muffin.
SUGGESTED READINGS:
· Kendall, M.G. and Stuart. (1996): An Advanced Theory of Statistics, Vol. I, II. Charls Griffin.
· Mood,A.M., Graybill, F.A. and Boes, D.C.(2007): Introduction to the Theory of Statistics, McGraw Hill, third edition.
· Mukhopadhyay, P. (1996): Mathematical Statistics, New Central Book Agency (P) Ltd.
· Rohatgi, V.K. (1984): An Introduction to Probability Theory and Mathematical Statistics, Wiley Eastern, third edition.
e-RESOURCES:
· https://epgp.inflibnet.ac.in/
JOURNALS:
· Sankhya The Indian Journal of Statistics, Indian Statistical Institute
· Aligarh Journal of Statistics, Department of Statistics and Operations Research, Aligarh Muslim University
· Afrika Statistika, Saint-Louis Senega University
· International Journal of Statistics and Reliability Engineering, Indian Association for Reliability and Statistic
· Journal of the Indian Society for Probability and Statistics, Indian Society for Probability and Statistics
· Journal of the Indian Statistical Association, Indian Statistical Association
· Statistica, Department of Statistical Sciences Paolo Fortunato, University of Bologna
· Statistics and Applications, Society of Statistics, Computer and Applications
· Stochastic Modeling and Applications, MUK Publications and Distributions