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Statistical Inference
This paper is designed to familiarize the students with concept of statistical inference which include estimation theory.
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Course |
Learning outcomes (at course level |
Learning and teaching strategies |
Assessment Strategies
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Paper Code |
Paper Title |
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STT-401 |
Statistical Inference |
CO 42: Conduct hypothesis test about population mean and proportion.
CO 43: Learn about the theory of estimation and properties of a good estimator.
CO 44: Obtain the point estimator and interval estimator of the parameters.
CO 45: Construct and interpret a confidence interval about the population and variances.
CO 46: Learn non - parametric test such as run test, sign test and median test. |
Approach in teaching:
Interactive Lectures, Group Discussion, Classroom Assignment Problem Solving Sessions
Learning activities for the students:
Assignments Seminar Presentation Subject based Activities
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Classroom Quiz Assignments Class Test Individual Presentation |
Large Sample Test of Significance: Testing of significance for attributes and variables, tests of significance for single mean, standard deviation and proportions, tests of significance for difference between two means, standard deviations and proportions.
Theory of Estimation: Point Estimation: problems of point estimation, properties of a good point estimator- unbiasedness, consistency, efficiency & sufficiency.-factorization theorem (without proof) and it applications.
Concept of mean square error, Minimum Variance Unbiased Estimation, Cramer Rao Inequality, Rao-Blackwell Theorem (Without proof) and Lehman scheffe theorm (Without proof). Idea of most powerful test, uniformly most powerful test, randomized and non randomized test .
Methods of point estimation: Method of Maximum Likelihood and its properties of MLEs (without proof). Methods of Moments: Least Squares method. Interval Estimation: Concept, confidence interval, confidence coefficient, construction of confidence interval for population mean, variance, difference of population mean when standard deviation are known and unknown of Normal Distribution.
Neyman Pearson Lemma and its application for finding BCR. BCR in case of Binomial, Poisson and of Normal and Exponential Populations. Non Parametric Tests: Definition merits and limitations, Sign test for univariate and bivariate distributions, Run test and Median test for small and large samples.
1. Goon, A.M., Gupta, M.K. and Dasgupta, B. Das (1991): An Outline of Statistics,
Volume II, The World Press Pvt Ltd, Calcutta
2. Gupta, S.C. and Kapoor, V.K.(2000): Fundamentals of Mathematical Statistics, S Chand &Company, New Delhi, tenth edition.
3. Mood Alexander M., Graybill Frankline and Boes Duane C.(2007): Introduction to Theory of Statistics, Mc Graw Hill & Company Third Edition
1. Rohatgi, V.K.(2009): An Introduction to Probability Theory and Statistics, John Wiley
And Sons.
2. Casella, G. and Berger, Roger L.(2002): Statistical Inference, Duxbury Thompson Learning, Second Edition.
3. Snedecor, G.W. and Cochran, W.G. (1967): Statistical Methods, Iowa State University
Press.
4. Rohatgi, V.K. and Saleh, A.K. Md. Ehsanes (2001): An Introduction to Probability
Theory and Statistics, Second Edition, John Wiley and Sons.
