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UPSC Optional Subject Syllabus: Statistics PDF Download

Paper - I

  • Probability: Sample space and events, probability measure and probability space, random variable as a measurable function, distribution function of a random variable, discrete and continuous-type random variable, probability mass function, probability density function, vector-valued random variable, marginal and conditional distributions, stochastic independence of events and of random variables, expectation and  moments of a random variable, conditional expectation, convergence of a sequence of random variable in distribution, in probability, in p-th mean and almost everywhere,  their criteria and inter-relations, Chebyshev’s inequality and Khintchine‘s weak law of large numbers, strong law of large numbers and Kolmogoroff’s theorems, probability generating function, moment generating function, characteristic function, inversion theorem, Linderberg and Levy forms of central limit theorem, standard discrete and continuous probability
    distributions.
  • Statistical Inference: Consistency, unbiasedness, efficiency, sufficiency, completeness, ancillary statistics, factorization theorem, exponential family of distribution and its properties, uniformly minimum variance unbiased (UMVU) estimation, Rao-Blackwell and Lehmann-Scheffe theorems, Cramer-Rao inequality for single parameter. Estimation by methods of moments, maximum likelihood, least squares, minimum chi-square and modified minimum chi-square, properties of maximum likelihood and other estimators, asymptotic efficiency, prior and  posterior distributions, loss function, risk function, and minimax estimator. Bayes estimators. Non-randomised and randomised tests, critical function, MP tests, Neyman-Pearson lemma, UMP tests, monotone likelihood ratio, similar and unbiased tests, UMPU tests for single parameter likelihood ratio test and its asymptotic distribution. Confidence bounds and its relation with tests. Kolmogoroff’s test for goodness of fit and its consistency, sign test and its optimality. Wilcoxon signed-ranks test and its consistency, Kolmogorov-Smirnov two-sample test, run test, Wilcoxon-Mann-Whitney test and median test, their consistency and asymptotic normality. Wald’s SPRT and its properties, OC and ASN functions for tests regarding parameters for Bernoulli, Poisson, normal and exponential distributions. Wald’s fundamental identity.
  • Linear Inference and Multivariate Analysis: Linear statistical models’, theory of least squares and analysis of variance, Gauss- Markoff theory, normal equations, least squares estimates and their precision, test of significance and interval estimates based on least squares theory in one-way, two-way and three-way classified data, regression analysis, linear regression, curvilinear regression and orthogonal polynomials, multiple regression, multiple and partial correlations, estimation of variance and covariance components, multivariate normal distribution, Mahalanobis-D2 and Hotelling’s T2 statistics and their applications and properties, discriminant analysis, canonical correlations, principal component analysis.
  • Sampling Theory and Design of Experiments: An outline of fixed-population and superpopulation approaches, distinctive features of finite population sampling,probability sampling designs, simple random sampling with and without replacement, stratified random sampling, systematic sampling and its efficacy ,cluster sampling, twostage and multi-stage sampling, ratio and regression methods of estimation involving one or more auxiliary variables, twophase sampling, probability proportional to size sampling with and without replacement, the Hansen-Hurwitz and the Horvitz- Thompson estimators, non-negative variance estimation with reference to the Horvitz-Thompson estimator, non-sampling errors. Fixed effects model (two-way classification) random and mixed effects models (two-way classification with equal observation per cell), CRD, RBD, LSD and their analyses, incomplete block designs, concepts of orthogonality and balance, BIBD, missing plot technique, factorial experiments and 2nand 32, confounding in factorial experiments, split-plot and simple lattice designs, transformation of data Duncan’s multiple range test.

Paper - II

  • Industrial Statistics: Process and product control, general theory of control charts, different types of  control charts for variables and attributes, X, R, s, p, np and c charts, cumulative sum chart. Single, double, multiple and sequential sampling plans for attributes, OC, ASN, AOQ and ATI curves, concepts of producer’s and consumer’s risks, AQL, LTPD and AOQL, Sampling plans for variables, Use of Dodge-Roming tables. Concept of reliability, failure rate and reliability functions, reliability of series and parallel systems and other simple configurations, renewal density and renewal function, Failure models: exponential, Weibull, normal, lognormal. Problems in life testing, censored and truncated experiments for exponential models.
  • Optimization Techniques: Different types of models in Operations Research, their construction and general methods of solution, simulation and Monte-Carlo
    methods formulation of linear programming (LP) problem, simple LP model and its graphical solution, the simplex procedure, the two-phase method and the M-technique with artificial variables, the duality theory of LP and its economic interpretation, sensitivity analysis, transportation and assignment problems, rectangular games, twoperson zero-sum games, methods of solution (graphical and algebraic). Replacement of failing or deteriorating items, group and individual replacement
    policies, concept of scientific inventory management and analytical structure of inventory problems, simple models with deterministic and stochastic demand with
    and without lead time, storage models with particular reference to dam type. Homogeneous discrete-time Markov chains, transition probability matrix, classification
    of states and ergodic theorems, homogeneous continuous-time Markov chains, Poisson process, elements of queuing theory, M/M/1, M/M/K, G/M/1 and M/G/1
    queues. Solution of statistical problems on computers using well-known statistical software packages like SPSS.
  • Quantitative Economics and Official Statistics: Determination of trend, seasonal and cyclical components, Box-Jenkins method, tests for stationary series, ARIMA models and determination of orders of autoregressive and moving average components, forecasting. Commonly used index numbers- Laspeyre’s, Paasche’s and Fisher’s ideal index numbers, chain-base index number, uses and limitations of index numbers, index number of wholesale prices, consumer
    prices, agricultural production and industrial production, test for index numbers - proportionality, time-reversal, factor-reversal and circular . General linear model, ordinary least square and generalized least squares methods of estimation, problem of multicollinearity, consequences and solutions of multicollinearity, autocorrelation and its consequences, heteroscedasticity of disturbances and its testing, test for independence of disturbances, concept of structure and model for simultaneous equations, problem of identification-rank and order conditions of identifiability, twostage least square method of estimation. Present official statistical system in India relating to population, agriculture, industrial  production, trade and prices, methods of collection of official statistics, their reliability and limitations, principal publications containing such statistics, various official  agencies responsible for data collection and their main functions.
  • Demography and Psychometry: Demographic data from census, registration,  NSS other surveys, their limitations and uses, definition, construction and uses of
    vital rates and ratios, measures of fertility,  reproduction rates, morbidity rate, standardized death rate, complete and abridged life tables, construction of life tables from vital statistics and census returns, uses of life  tables, logistic and other population growth curves, fitting a logistic curve, population projection, stable population, quasi-stable population, techniques in estimation of demographic parameters, standard classification by cause of death, health surveys and use of hospital statistics. Methods of standardisation of scales and tests, Z-scores, standard scores, T-scores, percentile scores, intelligence quotient and its measurement and uses, validity and reliability of test scores and its determination, use of factor analysis and path analysis in psychometry.
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FAQs on UPSC Optional Subject Syllabus: Statistics

1. What is the importance of the UPSC optional subject syllabus in the preparation for the UPSC exam?
Ans. The UPSC optional subject syllabus plays a crucial role in the preparation for the UPSC exam. It provides a systematic outline of the topics that candidates need to study for the optional subject. By following the syllabus, candidates can ensure that they cover all the important areas and do not miss out on any relevant topics. Moreover, the syllabus helps candidates to plan their study schedule effectively and allocate time to each topic based on its weightage in the exam.
2. How can the UPSC optional subject syllabus for Statistics help candidates in their preparation?
Ans. The UPSC optional subject syllabus for Statistics provides candidates with a clear understanding of the topics they need to study. It covers various areas such as probability theory, statistical inference, linear regression analysis, and multivariate analysis. By following the syllabus, candidates can focus their preparation on these specific areas and develop a strong conceptual understanding of the subject. It also helps candidates to identify the important topics that are frequently asked in the exam, enabling them to prioritize their study accordingly.
3. Are there any specific resources or study materials recommended for the UPSC optional subject of Statistics?
Ans. While there are no specific resources or study materials recommended by UPSC for the optional subject of Statistics, candidates can refer to various textbooks and reference books to enhance their understanding of the subject. Some popular books for Statistics include "Mathematical Statistics" by S.C. Gupta and V.K. Kapoor, "Introduction to Probability and Statistics" by William Mendenhall, and "Statistical Inference" by George Casella and Roger L. Berger. Additionally, candidates can also refer to online study materials, video lectures, and previous years' question papers for practice and revision.
4. How can candidates effectively utilize the UPSC optional subject syllabus for Statistics during their exam preparation?
Ans. Candidates can effectively utilize the UPSC optional subject syllabus for Statistics by following a structured approach. They should start by thoroughly understanding the syllabus and the weightage assigned to each topic. Based on this, candidates can create a study plan and allocate sufficient time to each topic. It is important to focus on building a strong foundation in basic concepts before moving on to more advanced topics. Regular practice of numerical problems and solving previous years' question papers can help candidates gauge their understanding and identify areas that require further improvement.
5. Is it necessary to cover the entire UPSC optional subject syllabus for Statistics in-depth?
Ans. While it is ideal to cover the entire UPSC optional subject syllabus for Statistics in-depth, candidates should prioritize topics based on their weightage in the exam and the level of difficulty. It is advisable to focus on mastering the fundamental concepts and important areas, rather than trying to cover all the topics superficially. Candidates should aim to develop a strong conceptual understanding and the ability to apply statistical techniques to real-life scenarios. However, it is important to note that skipping any topic completely may lead to missing out on potential questions in the exam.
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