Paper III of the Indian Statistical Service written examination is Statistics-Iii, and this page takes its syllabus one line at a time. Each line is reproduced as it is prescribed, then pointed at the page on this site that teaches it, with the depth stated rather than implied.
Statistics-III (Descriptive) — 200 marks, 3 hrs.
Examination Notice No. 07/2026-IES/ISS, dated 11.02.2026 — Appendix-I, Scheme of Examination, and Section-II, Standard and Syllabi12 syllabus lines: 8 taught in depth, 1 at exam level, 3 not here yet.
| Syllabus line, as prescribed | Where it is taught here | Depth |
|---|---|---|
| Population and sample; need for sampling; complete enumeration versus sampling; basic concepts; sampling and non-sampling error | Sampling Techniques Unit 1 — Sample Survey Concepts | deep |
| Methodologies in sample surveys — questionnaires, sampling design and field investigation as followed by NSSO | nothing on this site teaches it | not here |
| Subjective or purposive sampling; probability sampling; simple random sampling with and without replacement; estimation of population mean and proportion and their standard errors | Sampling Techniques Unit 2 — Simple Random Sampling | deep |
| Stratified random sampling; proportional and optimum allocation; comparison with simple random sampling for fixed sample size | Sampling Techniques Unit 3 — Stratified Random Sampling | deep |
| Ratio, product and regression methods of estimation; bias and variance to the first order of approximation; comparison with simple random sampling | Sampling Theory Unit 2 — Ratio and Regression Estimators: Exact Bias, the Difference Estimator, Separate and Combined | deep |
| Systematic sampling when N is an integer multiple of n; estimation of the mean and its standard error; comparison with simple random sampling | Sampling Techniques Unit 4 — Systematic, Cluster & Multistage Sampling | deep |
| Sampling with probability proportional to size, with and without replacement; Horvitz–Thompson estimator | Sampling Theory Unit 1 — Unequal Probability Sampling: Hansen-Hurwitz, Lahiri, Horvitz-Thompson and Yates-Grundy | deep |
| Des Raj and Das estimators for n = 2 | nothing on this site teaches it | not here |
| Equal size cluster sampling: estimators of the mean and total and their standard errors; comparison with SRS through the intra-class correlation coefficient | Sampling Theory Unit 3 — Cluster Sampling: the Intra-Cluster Correlation, the Design Effect and Optimum Cluster Size | deep |
| Multistage sampling; two-stage sampling with equal second-stage units; estimation of the mean and total | Sampling Theory Unit 4 — Two-Stage Sampling, Non-Sampling Errors, Randomized Response and Small Area Estimation | deep |
| Double sampling in ratio and regression methods of estimation | UGC NET Unit III — Sampling Methods & Design of Experiments | brief |
| Interpenetrating sub-sampling | nothing on this site teaches it | not here |
7 syllabus lines: 2 taught in depth, 5 at exam level, 0 not here yet.
| Syllabus line, as prescribed | Where it is taught here | Depth |
|---|---|---|
| Nature of econometrics; the general linear model and its extensions; ordinary least squares estimation and prediction | Econometrics Unit 1 — Basic Econometrics Econometrics Unit 2 — Models and Estimation | deep |
| Generalized least squares estimation and prediction; heteroscedastic disturbances; pure and mixed estimation | Econometrics Unit 3 — Heteroscedasticity | brief |
| Autocorrelation, its consequences and tests; Theil BLUS procedure, estimation and prediction | Econometrics Unit 5 — Autocorrelation | brief |
| Multicollinearity, its implications and tools for handling it; ridge regression | Econometrics Unit 4 — Multicollinearity | deep |
| Linear and stochastic regression; instrumental variable estimation; errors in variables; autoregressive lag model | Econometrics Unit 3 — Heteroscedasticity Econometrics Unit 5 — Autocorrelation | brief |
| Simultaneous linear equations model and its generalization; identification problem; restrictions on structural parameters; rank and order conditions | Econometrics Unit 5 — Autocorrelation UGC NET Unit VI — Linear Estimation, Regression & Econometrics | brief |
| Estimation in simultaneous equations models; recursive systems; 2SLS, limited information, k-class, 3SLS and full information maximum likelihood estimators; prediction and simultaneous confidence intervals | Econometrics Unit 5 — Autocorrelation | brief |
11 syllabus lines: 5 taught in depth, 4 at exam level, 2 not here yet.
| Syllabus line, as prescribed | Where it is taught here | Depth |
|---|---|---|
| Index numbers: price and quantity relatives; link and chain relatives; Laspeyres, Paasche, Marshall–Edgeworth and Fisher index numbers; chain base index numbers; tests for index numbers | Applied Statistics Unit 3 — Index Numbers | deep |
| Construction of index numbers of wholesale and consumer prices | Applied Statistics II Unit 2 — Index Numbers (Advanced) | deep |
| Income distribution — Pareto and Engel curves; concentration curve | Applied Statistics II Unit 3 — Demand Analysis Descriptive Statistics Unit 4 — Measures of Dispersion | brief |
| Methods of estimating national income; inter-sectoral flows; inter-industry table; role of CSO | National Income and the National Accounts — Economics (Unit 2) | deep |
| Demand analysis | Applied Statistics II Unit 3 — Demand Analysis | deep |
| Economic time series: components, additive and multiplicative models; determination of trend, seasonal and cyclical fluctuations | Applied Statistics Unit 1 — Time Series | deep |
| Time series as a discrete parameter stochastic process; autocovariance and autocorrelation functions and their properties | UGC NET Unit VII — Time Series | brief |
| Exploratory time series analysis; tests for trend and seasonality; exponential and moving average smoothing; Holt and Winters smoothing; forecasting based on smoothing | Applied Statistics Unit 2 — Seasonal Component | brief |
| Stationary processes: moving average, autoregressive, ARMA and ARIMA models; Box–Jenkins models; choice of AR and MA orders | Data Science Time Series Forecasting with ARIMA in R | brief |
| Estimation of the mean, autocovariance and autocorrelation functions under large sample theory; estimation of ARIMA parameters | nothing on this site teaches it | not here |
| Spectral analysis of weakly stationary processes; periodogram and correlogram analyses; computations based on the Fourier transform | nothing on this site teaches it | not here |
Not covered yet — 5 lines in Paper III. Read these from a standard text; this site does not yet teach them.