This course lays the foundation of probability distributions and sampling distributions, their application which forms the basis of Statistical Inference.
Students will be able to
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Learning outcomes (at course level |
Learning and teaching strategies |
Assessment Strategies
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STT-123 |
Probability Distributions |
CO 11: Identify the behavior of the discrete population and sample and their distribution.
CO 12: Derive the probability distributions function of random variables and use these techniques to generate data from various distributions.
CO 13: Identify the behavior of the continuous population and sample and their distribution.
CO 14: Analyse the behaviour of the data by Fitting the discrete and continuous distributions.
CO 15: 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 |
Classroom Quiz Assignments Class Test Individual Presentation |
Unit-I
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
Unit –II
Geometric distribution, Negative Binomial distribution, Hyper-geometric distributions, Power Series distribution- moments, moment generating function, cummulant generating function, characteristic functions, recurrence relations, properties, fitting of distributions
Unit-III
Rectangular distribution, Normal distribution (truncated also), Exponential distribution, Lognormal distribution, Multinomial of binomial and Poisson- moments, moment generating function, cummulant generating function, characteristic functions, recurrence relations, properties, fitting of distributions
Unit-IV
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)
Unit-V
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
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