Indian Statistical Service Examination (ISS) 2020

Exam Highlights
Level of Exam Graduate, National Level
Exam Medium English
Exam Frequency Once in a year
Purpose of the Exam It is a combined competitive examination for recruitment to Junior Time Scale of the Services.
Conducting Body Union Public Service Commission

The Indian Statistical Service (abbreviated as ISS) is an organized Group-A Central Civil Services of the executive branch of the Government of India. ISS is a technical service with a high degree of proficiency in Statistical methods and applications.

The main mandate of this examination is to produce quality Official Statistics with better methods and techniques, provide solutions to the data and information needs and interpretation and analysis of statistics.

ISS Examination Important Dates 2020

Event Date
ISS Exam Notification June 10, 2020
Starting Date of Application June 10, 2020
Last Date of Application June 30, 2020
Date of Commencement of Written Examination October 16, 2020
Date of Result of Written Examination To be notified.

ISS Examination Eligibity Criteria 2020

  1. Nationality
  2. Candidate must be either:

    1. a Citizen of India, or
    2. a subject of Nepal, or
    3. a subject of Bhutan, or
    4. a Tibetan refugee who came over to India before the 1st January 1962 with the intention of permanently settling in India, or
    5. a person of Indian origin who has migrated from Pakistan, Burma, Sri Lanka or East African Countries of Kenya, Uganda, the United Republic of Tanzania, Zambia, Malawi, Zaire, and Ethiopia or from Vietnam with the intention of permanently settling in India.

    Provided that a candidate belonging to categories (b), (c), (d), and (e) above shall be a person in whose favour a certificate of eligibility has been issued by the Government of India.

  3. Educational Qualification
  4. A candidate for the Indian Statistical Service must have obtained a Bachelor’s Degree with Statistics/Mathematical Statistics/Applied Statistics as one of the subjects or a Master’s degree in Statistics/Mathematical Statistics/Applied Statistics from a recognized University.

  5. Age Limit
  6. The age of the candidate to appear in the ISS examination should be minimum of 21 years and maximum of 30 years.
    However, there are age relaxations for candidates belonging to the reserved categories. They are given below in the table.
    Category Upper Age Limit
    Scheduled Caste/Scheduled Tribe 35 years
    Other Backward Classes 33 years
    Candidate had ordinarily been domiciled in the State of Jammu & Kashmir during the period from the 1st January, 1980 to the 31st day of December, 1989 35 years
    Defence Services Personnel 33 years
    ECOs/SSCOs 35 years
    Persons with Benchmark Disabilities 40 years
    *Note

    Candidates belonging to the Scheduled Castes, the Scheduled Tribes and the Other Backward Classes who are also covered under any other categories, viz. those coming under the category of Ex-servicemen, persons domiciled in the State of J & K and Pwd will be eligible for grant of cumulative age relaxation under both the categories.

  7. Restrictions on Attempts
  8. There is no attempt restriction for the candidates provided the upper age limit has not been exceeded.

ISS Examination Pattern 2020

There are two parts of the examination. Part I consists of a written examination carrying a maximum of 1000 marks. And Part II is a viva voce of such candidates called by the UPSC carrying a maximum of 200 marks.

Pattern for ISS Written examination

Sl. No. Name of the Paper/Section Nature Nos. of Questions Question Type Duration Negative Marking Marks
1 General English

The standard of the paper is such as may be expected of a graduate of an Indian University.

Merit Ranking Nature Descriptive 180 mins 100
2 General Studies

The standard of the paper is such as may be expected of a graduate of an Indian University.

Merit Ranking Nature Descriptive 180 mins 100
3 Statistics-I

The standard of this paper will be that of the Master’s degree examination of an Indian University in the relevant disciplines.

Merit Ranking Nature Objective (Multiple Choice) 120 mins 1/3rd marks will be deducted for each wrong answer. 200
4 Statistics-II

The standard of this paper will be that of the Master’s degree examination of an Indian University in the relevant disciplines.

Merit Ranking Nature Objective (Multiple Choice) 120 mins 1/3rd marks will be deducted for each wrong answer. 200
5 Statistics-III

The standard of this paper will be that of the Master’s degree examination of an Indian University in the relevant disciplines.

Merit Ranking Nature Descriptive 180 mins 200
6 Statistics-IV

The standard of this paper will be that of the Master’s degree examination of an Indian University in the relevant disciplines.

Merit Ranking Nature Descriptive 180 mins 200
Total Marks* 1000

*The total marks shown here are considered for merit ranking purposes.

Pattern for ISS Viva-Voce (Interview)

Total marks allotted: 200

Viva-voce - The candidate will be interviewed by a Board of competent and unbiased observers who will have before them a record of his/her career.
The object of the interview is to assess his/her suitability for the service for which he/she has competed.

ISS Examination Syllabus 2020

Syllabus for ISS Written examination

1 General English

    Candidates will be required to write an essay in English. Other questions will be designed to test their understanding of English and workman like use of words. Passages will usually be set for summary or precis.

2 General Studies

    General knowledge including knowledge of current events and of such matters of everyday observation and experience in their scientific aspects as may be expected of an educated person who has not made a special study of any scientific subject. The paper will also include questions on Indian Polity including the political system and the Constitution of India, History of India and Geography of a nature which a candidate should be able to answer without special study.

3 Statistics-I

  • Probability:
    Classical and axiomatic definitions of Probability and consequences. Law of total probability,Conditional probability, Bayes' theorem and applications. Discrete and continuous random variables. Distribution functions and their properties.Standard discrete and continuous probability distributions - Bernoulli, Uniform, Binomial,
    Poisson, Geometric, Rectangular, Exponential, Normal, Cauchy, Hyper geometric, Multinomial, Laplace, Negative binomial, Beta, Gamma, Lognormal. Random vectors, Joint and marginal distributions, conditional distributions, Distributions of functions of random variables. Modes of convergences of sequences of random variables - in distribution, in probability, with probability one and in mean square. Mathematical expectation and conditional expectation. Characteristic function, moment and probability generating functions, Inversion, uniqueness and continuity theorems. Borel 0-1 law, Kolmogorov's 0-1 law. Tchebycheff's and Kolmogorov's inequalities.
    Laws of large numbers and central limit theorems for independent variables.
  • Statistical Methods:
    Collection, compilation and presentation of data, charts, diagrams and histogram. Frequency distribution. Measures of location, dispersion, skewness and kurtosis. Bivariate and multivariate data. Association and contingency. Curve fitting and orthogonal polynomials. Bivariate normal distribution. Regression-linear, polynomial. Distribution of the correlation coefficient, Partial and multiple correlation, Intraclass correlation, Correlation ratio.
    Standard errors and large sample test. Sampling distributions of sample mean, sample variance,t, chi-square and F; tests of significance based on them, Small sample tests.Non-parametric tests-Goodness of fit, sign, median, run, Wilcoxon, Mann-Whitney, WaldWolfowitz and Kolmogorov-Smirnov. Order statistics-minimum, maximum, range and median.Concept of Asymptotic relative efficiency.
  • Numerical Analysis:
    • Finite differences of different orders: , E and D operators, factorial representation of a polynomial, separation of symbols, sub-division of intervals, differences of zero.
    • Concept of interpolation and extrapolation: Newton Gregory's forward and backward interpolation formulae for equal intervals, divided differences and their properties, Newton's formula for divided difference, Lagrange’s formula for unequal intervals, central difference formula due to Gauss, Sterling and Bessel, concept of error terms in interpolation formula.
    • Inverse interpolation: Different methods of inverse interpolation.
    • Numerical differentiation: Trapezoidal, Simpson’s one-third and three-eight rule and Waddles rule.
    • Summation of Series: Whose general term (i) is the first difference of a function (ii) is in geometric progression.
    • Numerical solutions of differential equations: Euler's Method, Milne’s Method, Picard’s
      Method and Runge-Kutta Method.
    • Computer application and Data Processing:
      • Basics of Computer: Operations of a computer, Different units of a computer system like central processing unit, memory unit, arithmetic and logical unit, input unit, output unit etc., Hardware including different types of input, output and peripheral devices, Software, system and application software, number systems, Operating systems, packages and utilities, Low and High level languages, Compiler, Assembler, Memory – RAM, ROM, unit of computer memory (bits,bytes etc.), Network – LAN, WAN, internet, intranet, basics of computer security, virus, antivirus,firewall, spyware, malware etc.
      • Basics of Programming: Algorithm, Flowchart, Data, Information, Database, overview ofdifferent programming languages, frontend and backend of a project, variables, control structures, arrays and their usages, functions, modules, loops, conditional statements,exceptions, debugging and related concepts.

      4 Statistics-II

      • Linear Models:
        • Theory of linear estimation, Gauss-Markov linear models, estimable functions, error and estimation space, normal equations and least square estimators, estimation of error variance, estimation with correlated observations, properties of least square estimators, generalized inverse of a matrix and solution of normal equations, variances and covariances of least square estimators. One way and two-way classifications, fixed, random and mixed effects models. Analysis of
          variance (two-way classification only), multiple comparison tests due to Tukey, Scheffe and Student-Newmann-Keul-Duncan.
      • Statistical Inference and Hypothesis Testing:
        • Characteristics of good estimator: Estimation methods of maximum likelihood, minimum chisquare, moments and least squares. Optimal properties of maximum likelihood estimators.Minimum variance unbiased estimators. Minimum variance bound estimators. Cramer-Rao inequality. Bhattacharya bounds. Sufficient estimator. factorization theorem. Complete statistics. Rao-Blackwell theorem. Confidence interval estimation. Optimum confidence bounds.Resampling, Bootstrap and Jacknife.
        • Hypothesis testing: Simple and composite hypotheses. Two kinds of error. Critical region.Different types of critical regions and similar regions. Power function. Most powerful and uniformly most powerful tests. Neyman-Pearson fundamental lemma. Unbiased test. Randomized test. Likelihood ratio test. Wald's SPRT, OC and ASN functions. Elements of decision theory.
      • Official Statistics:
        • National and International official statistical system:
          Official Statistics: (a) Need, Uses, Users, Reliability, Relevance, Limitations, Transparency, its visibility (b) Compilation, Collection, Processing, Analysis and Dissemination, Agencies Involved, Methods.
        • National Statistical Organization: Vision and Mission, NSSO and CSO; roles and responsibilities;Important activities, Publications etc.
        • National Statistical Commission: Need, Constitution, its role, functions etc; Legal Acts/Provisions/ Support for Official Statistics; Important Acts.
        • Index Numbers: Different Types, Need, Data Collection Mechanism, Periodicity, Agencies Involved, Uses.
        • Sector Wise Statistics: Agriculture, Health, Education, Women and Child etc. Important Surveys & Census, Indicators, Agencies and Usages etc.
        • National Accounts: Definition, Basic Concepts; issues; the Strategy, Collection of Data and Release.
        • Population Census: Need, Data Collected, Periodicity, Methods of data collection,dissemination, Agencies involved.Misc: Socio Economic Indicators, Gender Awareness/Statistics, Important Surveys and Censuses.

      5 Statistics-III

      • Sampling Techniques:
        Concept of population and sample, need for sampling, complete enumeration versus sampling,basic concepts in sampling, sampling and Non-sampling error, Methodologies in sample surveys(questionnaires, sampling design and methods followed in field investigation) by NSSO.Subjective or purposive sampling, probability sampling or random sampling, simple random sampling with and without replacement, estimation of population mean, population proportions and their standard errors. Stratified random sampling, proportional and optimum allocation,comparison with simple random sampling for fixed sample size. Covariance and Variance Function.
        Ratio, product and regression methods of estimation, estimation of population mean, evaluation of Bias and Variance to the first order of approximation, comparison with simple random sampling.
        Systematic sampling (when population size (N) is an integer multiple of sampling size (n)).Estimation of population mean and standard error of this estimate, comparison with simple random sampling.
        Sampling with probability proportional to size (with and without replacement method), Des Raj and Das estimators for n=2, Horvitz-Thomson’s estimator.
        Equal size cluster sampling: estimators of population mean and total and their standard errors,comparison of cluster sampling with SRS in terms of intra-class correlation coefficient.
        Concept of multistage sampling and its application, two-stage sampling with equal number of second stage units, estimation of population mean and total.Double sampling in ratio and regression methods of estimation.
        Concept of Interpenetrating sub-sampling.

      • Econometrics:
        Nature of econometrics, the general linear model (GLM) and its extensions, ordinary least squares (OLS) estimation and prediction, generalized least squares (GLS) estimation and prediction, heteroscedastic disturbances, pure and mixed estimation.
        Auto correlation, its consequences and tests. Theil BLUS procedure, estimation and prediction, multi-collinearity problem, its implications and tools for handling the problem, ridge regression.

        Linear regression and stochastic regression, instrumental variable estimation, errors in variables, autoregressive linear regression, lagged variables, distributed lag models, estimation of lags by OLS method, Koyck’s geometric lag model.
        Simultaneous linear equations model and its generalization, identification problem, restrictions on structural parameters, rank and order conditions. Estimation in simultaneous equations model, recursive systems, 2 SLS estimators, limited information estimators, k-class estimators, 3 SLS estimator, full information maximum likelihood method, prediction and simultaneous confidence intervals.

      • Applied Statistics:
        Index Numbers: Price relatives and quantity or volume relatives, Link and chain relatives composition of index numbers; Laspeyre's, Paasches’, Marshal Edgeworth and Fisher index numbers; chain base index number, tests for index number, Construction of index numbers of wholesale and consumer prices, Income distribution-Pareto and Engel curves, Concentration curve, Methods of estimating national income, Inter-sectoral flows, Inter-industry table, Role of CSO. Demand Analysis.
        Time Series Analysis: Economic time series, different components, illustration, additive and multiplicative models, determination of trend, seasonal and cyclical fluctuations.
        Time-series as discrete parameter stochastic process, auto covariance and autocorrelation functions and their properties.
        Exploratory time Series analysis, tests for trend and seasonality, exponential and moving average smoothing. Holt and Winters smoothing, forecasting based on smoothing.
        Detailed study of the stationary processes: (1) moving average (MA), (2) auto regressive (AR), (3) ARMA and (4) AR integrated MA (ARIMA) models. Box-Jenkins models, choice of AR and MA periods.
        Discussion (without proof) of estimation of mean, auto covariance and autocorrelation functions under large sample theory, estimation of ARIMA model parameters.
        Spectral analysis of weakly stationary process, periodogram and correlogram analyses, computations based on Fourier transform.

      6 Statistics-IV

      • Operations Research and Reliability:
        Definition and Scope of Operations Research: phases in Operation Research, models and their solutions, decision-making under uncertainty and risk, use of different criteria, sensitivity analysis.
        Transportation and assignment problems. Bellman’s principle of optimality, general formulation, computational methods and application of dynamic programming to LPP.
        Decision-making in the face of competition, two-person games, pure and mixed strategies, existence of solution and uniqueness of value in zero-sum games, finding solutions in 2x2, 2xm and mxn games.
        Analytical structure of inventory problems, EOQ formula of Harris, its sensitivity analysis and extensions allowing quantity discounts and shortages. Multi-item inventory subject to constraints. Models with random demand, the static risk model. P and Q- systems with constant and random lead times.
        Queuing models – specification and effectiveness measures. Steady-state solutions of M/M/1 and M/M/c models with associated distributions of queue-length and waiting time. M/G/1 queue and Pollazcek-Khinchine result.
        Sequencing and scheduling problems. 2-machine n-job and 3-machine n-job problems with identical machine sequence for all jobs
        Branch and Bound method for solving travelling salesman problem.
        Replacement problems – Block and age replacement policies.
        PERT and CPM – basic concepts. Probability of project completion.
        Reliability concepts and measures, components and systems, coherent systems, reliability of coherent systems.
        Life-distributions, reliability function, hazard rate, common univariate life distributions – exponential, weibull, gamma, etc. Bivariate exponential distributions. Estimation of parameters and tests in these models.
        Notions of aging – IFR, IFRA, NBU, DMRL and NBUE classes and their duals. Loss of memory property of the exponential distribution.
        Reliability estimation based on failure times in variously censored life-tests and in tests with replacement of failed items. Stress-strength reliability and its estimation.
      • Demography and Vital Statistics:
        Sources of demographic data, census, registration, ad-hoc surveys, Hospital records,Demographic profiles of the Indian Census.
        Complete life table and its main features, Uses of life table. Makehams and Gompertz curves. National life tables. UN model life tables. Abridged life tables. Stable and stationary populations. Measurement of Fertility: Crude birth rate, General fertility rate, Age specific birth rate, Total fertility rate, Gross reproduction rate, Net reproduction rate.
        Measurement of Mortality: Crude death rate, Standardized death rates, Age-specific death rates, Infant Mortality rate, Death rate by cause.
        Internal migration and its measurement, migration models, concept of international migration.Net migration. International and postcensal estimates. Projection method including logistic curve fitting. Decennial population census in India.
      • Survival Analysis and Clinical Trial:
        Concept of time, order and random censoring, likelihood in the distributions – exponential,gamma, Weibull, lognormal, Pareto, Linear failure rate, inference for these distribution.
        Life tables, failure rate, mean residual life and their elementary classes and their properties. Estimation of survival function – actuarial estimator, Kaplan – Meier estimator, estimation under the assumption of IFR/DFR, tests of exponentiality against non-parametric classes, total time on test.
        Two sample problem – Gehan test, log rank test.
        Semi-parametric regression for failure rate – Cox’s proportional hazards model with one and several covariates, rank test for the regression coefficient.
        Competing risk model, parametric and non-parametric inference for this model.
        Introduction to clinical trials: the need and ethics of clinical trials, bias and random error in clinical studies, conduct of clinical trials, overview of Phase I – IV trials, multicenter trials.
        Data management: data definitions, case report forms, database design, data collection systems for good clinical practice.
        Design of clinical trials: parallel vs. cross-over designs, cross-sectional vs. longitudinal designs, review of factorial designs, objectives and endpoints of clinical trials, design of Phase I trials, design of single-stage and multi-stage Phase II trials, design and monitoring of phase III trials with sequential stopping,
        Reporting and analysis: analysis of categorical outcomes from Phase I – III trials, analysis of survival data from clinical trials.
      • Quality Control:
        Statistical process and product control: Quality of a product, need for quality control, basic concept of process control, process capability and product control, general theory of control charts, causes of variation in quality, control limits, sub grouping summary of out of control criteria, charts for attributes p chart, np chart, c-chart, V chart, charts for variables: R, ( X ,R), ( X ,σ) charts.
        Basic concepts of process monitoring and control; process capability and process optimization.General theory and review of control charts for attribute and variable data; O.C. and A.R.L. of control charts; control by gauging; moving average and exponentially weighted moving average charts; Cu-Sum charts using V-masks and decision intervals; Economic design of X-bar chart.
        Acceptance sampling plans for attributes inspection; single and double sampling plans and their properties; plans for inspection by variables for one-sided and two sided specification.
      • Multivariate Analysis:
        Multivariate normal distribution and its properties. Random sampling from multivariate normal distribution. Maximum likelihood estimators of parameters, distribution of sample mean vector.
        Wishart matrix – its distribution and properties, distribution of sample generalized variance, null and non-null distribution of multiple correlation coefficients. Hotelling’s T2 and its sampling distribution, application in test on mean vector for one and more multivariate normal population and also on equality of components of a mean vector in multivariate normal population.
        Classification problem: Standards of good classification, procedure of classification based on multivariate normal distributions.
        Principal components, dimension reduction, canonical variates and canonical correlation — definition, use, estimation and computation.
      • Design and Analysis of Experiments:
        Analysis of variance for one way and two way classifications, Need for design of experiments,basic principle of experimental design (randomization, replication and local control), complete analysis and layout of completely randomized design, randomized block design and Latin square design, Missing plot technique. Split Plot Design and Strip Plot Design.
        Factorial experiments and confounding in 2n and 3n experiments. Analysis of covariance. Analysis of non-orthogonal data. Analysis of missing data.
      • Computing with C and R :
        Basics of C: Components of C language, structure of a C program, Data type, basic data types,Enumerated data types, Derived data types, variable declaration, Local, Global, Parametric variables, Assignment of Variables, Numeric, Character, Real and String constants, Arithmetic, Relation and Logical operators, Assignment operators, Increment and decrement operators, conditional operators, Bitwise operators, Type modifiers and expressions, writing and interpreting expressions, using expressions in statements. Basic input/output.
        Control statements: conditional statements, if - else, nesting of if - else, else if ladder, switch statements, loops in c, for, while, do - while loops, break, continue, exit ( ), goto and label declarations, One dimensional two dimensional and multidimensional arrays. Storage classes: Automatic variables, External variables, Static variables, Scope and lifetime of declarations.
        Functions: classification of functions, functions definition and declaration, assessing a function, return statement, parameter passing in functions. Pointers (concept only).
        Structure: Definition and declaration; structure (initialization) comparison of structure variable; Array of structures : array within structures, structures within structures, passing structures to functions; Unions accessing a union member, union of structure, initialization of a union variable, uses of union. Introduction to linked list, linear linked list, insertion of a node in list, removal of a node from list.
        Files in C: Defining and opening a file, input – output operation on a file, creating a file, reading a file.
        Statistics Methods and techniques in R.

Syllabus for ISS Viva-Voce (Interview)

There is no specified syllabus for Viva - Voce but the interview is intended to supplement the written examination for testing the general and specialised knowledge and abilities of the candidate.
The Board will pay special attention to assess

  • intellectual curiosity,
  • critical powers of assimilation,
  • balance of judgment and alertness of mind,
  • the ability for social cohesion,
  • integrity of character initiative and
  • capacity for leadership.

The candidate will be expected to have taken an intelligent interest not only in his/her subjects of academic study but also in events which are happening around him/her both within and outside his/her own State or Country.

They must have knowledge in the field of Current Affairs, New Discoveries & Inventions also. The technique of the interview is not that of a strict cross-examination, but of a natural, through directed and purposive conversation intended to reveal the candidate's mental qualities and his/her grasp of problems.

UPSC ISS Job Prospects

ISS is the gateway to the central service as Group A officers. Aspirants who successfully pass ISS examination are placed in various cadre posts in the

  1. Planning commission,
  2. Planning board,
  3. Ministry of economic affairs,
  4. National sample survey and
  5. Allied offices where specialists in statistics/economics are required.

The posts are primarily located in Ministries/Departments dealing with economic and social sectors.

Besides Cadre posts, ISS officers go on deputation to serve in various domestic and international organizations such as:

  1. United Nation bodies,
  2. Foreign governments,
  3. State governments and regulatory bodies.

Officers from the service are also appointed on deputation to posts in the Central Ministries/ Departments under the Central Staffing Scheme. The places of posting are usually in the State capitals or New Delhi.

A candidate selected at a favourable age can expect to rise quite high in the career and touch the level of even Secretary to the Government of India, in any Ministry concerned with economic affairs.