This paper is aimed at teaching the students various probability distributions which are useful in day to day life and able to obtain the probabilities when sample size is large.
Students will be able to:
Course |
Learning outcomes (at course level |
Learning and teaching strategies |
Assessment Strategies |
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Paper Code |
Paper Title |
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DSTT 701(A)
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Probability Distributions-II |
CO 79: Identify the behavior of the discrete population and sample and their distribution. CO 80: Derive the probability distributions function of random variables and use these techniques to generate data from various distributions. CO 81: Identify the behavior of the continuous population and sample and their distribution. CO 82: Analyze the behavior of the data by Fitting the discrete and continuous distributions. CO 83: Translate real-world sample problems into probability distributions and give an appropriate inference. |
Approach in teaching:
Interactive Lectures, Group Discussion, Classroom Assignment Problem Solving Sessions
Learning activities for the students:
Assignments Seminar Presentation Subject based Activities |
Software based Assignments Individual Presentation Class Test
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Compound Binomial distribution, Compound Poisson distribution - moments, moment generating function, Cumulant generating function, recurrence relations, properties, truncated binomial distribution, truncated Poisson distribution, Truncated Normal distribution.
Power Series distribution- moments, moment generating function, cumulant generating function, characteristic functions, recurrence relations, properties, fitting of distributions, Lognormal distribution, Triangular distribution, Cauchy distribution (truncated also).
Multinomial of binomial and Poisson- moments, moment generating function, cumulant generating function, characteristic functions, recurrence relations, properties, fitting of distributions, Laplace distributions, Pearson’s distribution (Type I, IV and VI).
Non central Chi-Square, t and F 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.
Convergence in probability and convergence in distribution, weak law of large numbers, Central limit theorem: De-Moivre’s Laplace, Liaponouff, Lindeberg-Levy and their simple problems, Zero-One law of Borel.
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