COURSE OUTCOME
- To understand the applications of theoretical discrete and continuous distributions.
- To equip students with Sampling Techniques used in conducting sample surveys.
- Understand various sampling distributions and the related concepts.
COURSE CONTENT
Module 1: Standard distributions: Discrete type-Bernoulli, Binomial, Poisson, Geometric, Negative Binomial (definition only), Uniform (mean, variance and mgf). Continuous type-Uniform, exponential and Normal (definition, properties and applications); Gamma (mean, variance, mgf); Lognormal, Beta, Pareto and Cauchy (Definition only).
Module 2: Limit theorems: Chebyshev’s inequality, Sequence of random variables, parameter and Statistic, Sample mean and variance, Convergence in probability (definition and example only), weak law of large numbers (iid case), Bernoulli law of large numbers, Convergence indistribution (definition and examples only), Central limit theorem (Lindberg levy-iid case).
Module 3: Sampling methods: Simple random sampling with and without replacement, systematic sampling (Concept only), stratified sampling (Concept only), Cluster sampling(Concept only).
Module 4: Sampling distributions: Statistic, Standard error, Sampling from normal
distribution, distribution of sample mean, sample variance, chi-square distribution, t distribution, and F distribution (definition, derivations and relationships only).
- Teacher: Benitta Susan Aniyan