UGC NET Statistics Study Material

Ten units of study notes written for the UGC NET Bureau’s “NET Syllabus, Subject: Statistics, Code 107”, with that syllabus reproduced below in the document’s own words. Each line points at the section of the unit notes that teaches it and, where one exists, the full course on this site that takes it further.

Read the ten units in order — Unit I, Probability and Distributions, first; the later units build on it.
Test yourself on the model MCQs — every unit, each answer explained rather than just marked.
Work through the solved June 2026 paper.
Prepare Paper I too — the General Paper on Teaching & Research Aptitude that every UGC NET candidate sits: notes and model MCQs, unit by unit.
Check the syllabus, line by line — below, each line with the unit section and the full course that teach it.

The ten units

Unit IProbability and DistributionsProbability axioms, Bayes' theorem, random variables, MGF, standard distributions, convergence, CLT, LLN. Unit IIReal Analysis and Matrix AlgebraSequences, series, continuity, differentiation, integration, vector spaces, eigenvalues, quadratic forms. Unit IIISampling Methods and Design of ExperimentsSRS, stratified, cluster, systematic, ratio and regression estimation, ANOVA, CRD, RBD, LSD, BIBD. Unit IVEstimation TheoryUnbiasedness, MLE, MoM, UMVUE, Cramér–Rao, sufficiency, Rao–Blackwell, Lehmann–Scheffé, CIs. Unit VTesting of HypothesesNeyman–Pearson, UMP tests, LRT, SPRT, chi-square, sign, Wilcoxon, Mann–Whitney, Kruskal–Wallis. Unit VILinear Estimation, Regression Analysis and EconometricsGauss–Markov, OLS, GLS, dummy variables, multicollinearity, heteroscedasticity, autocorrelation, 2SLS. Unit VIITime SeriesACF, PACF, stationarity, AR, MA, ARMA, ARIMA, Yule–Walker, forecasting, spectral density. Unit VIIIMultivariate AnalysisMultivariate normal, Wishart, Hotelling's T², discriminant, principal components, canonical correlation. Unit IXStochastic ProcessesMarkov chains, Chapman–Kolmogorov, gambler's ruin, Poisson process, birth–death, M/M/1 queues. Unit XIndian Statistical System and Research MethodologyMoSPI, NSO, NSC, Indian statisticians, R programming, LaTeX basics.

What this page does not say. The syllabus lists topics and nothing else, so this page says nothing about marks, duration, the number of questions or eligibility. Read those in the current notification for the session you are sitting.

Five symbols in the PDF are equation objects that do not survive as text — L′ in L′Hospital, 2² and 2³, R², T² and the word Latex. They were read off the printed page and are shown as printed.

130syllabus lines
98taught in depth
32covered at exam level
0not here yet
100%covered

How to read the depth column

deep a full course unit on this site teaches it, with the derivation worked out — the link after the UGC NET section. brief the UGC NET unit notes cover it at exam level: the definition, the result and two worked examples, without the full derivation. not here nothing on this site teaches it. Every course link was checked against that page’s own text when this map was built.

Unit I — Probability and Distributions

11 lines · 11 deep · 0 brief · 0 not here · Read the Unit I notes →

Syllabus line, as prescribed	Where it is taught here	Depth
Basic concepts of probability	UGC NET Unit I — Basic Concepts of Probability Theory of Probability Unit 1 — Elementary Probability	deep
conditional probability, Bayes theorem, independent events	UGC NET Unit I — Conditional Probability and Bayes' Theorem Theory of Probability Unit 1 — Elementary Probability	deep
Random variables and distribution functions	UGC NET Unit I — Random Variables and Distribution Functions Theory of Probability Unit 2 — Univariate Random Variables	deep
expectation and moments, moment generating function	UGC NET Unit I — Expectation and Moments Theory of Probability Unit 4 — Mathematical Expectation Theory of Probability Unit 5 — Generating Functions, LLN & CLT	deep
Standard discrete and continuous univariate distributions	UGC NET Unit I — Standard Discrete Distributions Theoretical Discrete Distributions Theoretical Continuous Distributions	deep
Jointly distributed random variables, marginal and conditional distributions	UGC NET Unit I — Jointly Distributed Random Variables Theory of Probability Unit 3 — Bivariate Random Variables	deep
Chebyshev inequality	UGC NET Unit I — Chebyshev's Inequality Theory of Probability Unit 4 — Mathematical Expectation	deep
Sampling distributions, transformation of random variables	UGC NET Unit I — Sampling Distributions Distribution Theory Unit 3 — Sampling Distributions: Chi-Square, t and F Distribution Theory Unit 2 — Transformations, Truncated, Mixture and Compound Distributions	deep
Characteristic function and its properties	UGC NET Unit I — Characteristic Function Probability Theory Unit 2 — Expectation, Characteristic Functions and Inequalities	deep
Modes of convergence of random variables	UGC NET Unit I — Modes of Convergence of Random Variables Probability Theory Unit 3 — Convergence of Sequences of Random Variables	deep
weak and strong laws of large numbers, central limit theorems (i.i.d. case)	UGC NET Unit I — Weak and Strong Laws of Large Numbers Probability Theory Unit 4 — Laws of Large Numbers and Central Limit Theorems	deep

Unit II — Real Analysis and Matrix Algebra

16 lines · 10 deep · 6 brief · 0 not here · Read the Unit II notes →

Syllabus line, as prescribed	Where it is taught here	Depth
Real Analysis: Finite, countable and uncountable sets	UGC NET Unit II — Finite, Countable and Uncountable Sets Mathematical Analysis Unit 1 — Metric Spaces, Compactness and Continuity	deep
sequences of real numbers, convergence of sequences, bounded sequences, monotonic sequences, Cauchy criterion for convergence	UGC NET Unit II — Sequences of Real Numbers Mathematical Analysis Unit 1 — Metric Spaces, Compactness and Continuity	deep
Series of real numbers, convergence, tests of convergence, alternating series, absolute and conditional convergence	UGC NET Unit II — Series of Real Numbers	brief
Power series and radius of convergence	UGC NET Unit II — Power Series and Radius of Convergence	brief
Functions of a real variable: Limit, continuity, monotone functions, uniform continuity, differentiability	UGC NET Unit II — Functions of a Real Variable Mathematical Analysis Unit 1 — Metric Spaces, Compactness and Continuity	deep
Rolle’s theorem, mean value theorems, Taylor’s theorem, L′ Hospital’s rule	UGC NET Unit II — Differentiability and Mean Value Theorems	brief
Riemann integration and its properties, improper integrals	UGC NET Unit II — Riemann Integration and Improper Integrals Mathematical Analysis Unit 2 — The Riemann-Stieltjes Integral	deep
Functions of two real variables: Limit, continuity, partial derivatives, total derivative, maxima and minima, saddle point, method of Lagrange multipliers, double and triple integrals and their applications	UGC NET Unit II — Functions of Two Real Variables	brief
Matrix Algebra: Vector spaces, subspaces, span, linear independence, basis and dimension	UGC NET Unit II — Vector Spaces, Span, Linear Independence, Basis Linear Algebra & Linear Models Unit 1 — Vector Spaces, Gram-Schmidt and Generalized Inverses	deep
row space and column space of a matrix, rank and nullity, row reduced echelon form	UGC NET Unit II — Rank, Nullity, Row Reduced Echelon Form Linear Algebra & Linear Models Unit 1 — Vector Spaces, Gram-Schmidt and Generalized Inverses	deep
trace and determinant, inverse of a matrix	UGC NET Unit II — Trace, Determinant, Inverse Linear Algebra & Linear Models Unit 2 — Characteristic Roots, Cayley-Hamilton and Spectral Decomposition	deep
systems of linear equations	UGC NET Unit II — Systems of Linear Equations	brief
Gram-Schmidt orthogonalization	UGC NET Unit II — Gram–Schmidt Orthogonalization Linear Algebra & Linear Models Unit 1 — Vector Spaces, Gram-Schmidt and Generalized Inverses	deep
Characteristic roots and characteristic vectors, characteristic polynomial, Cayley-Hamilton theorem	UGC NET Unit II — Characteristic Roots and Vectors Linear Algebra & Linear Models Unit 2 — Characteristic Roots, Cayley-Hamilton and Spectral Decomposition	deep
symmetric matrices, skew-symmetric matrices, orthogonal matrices and their characteristic roots	UGC NET Unit II — Symmetric, Skew-symmetric, Orthogonal Matrices	brief
positive definite and positive semi-definite matrices and their properties, quadratic forms	UGC NET Unit II — Positive Definite Matrices and Quadratic Forms Linear Algebra & Linear Models Unit 3 — Quadratic Forms and Matrix Inequalities	deep

Unit III — Sampling Methods and Design of Experiments

16 lines · 13 deep · 3 brief · 0 not here · Read the Unit III notes →

Syllabus line, as prescribed	Where it is taught here	Depth
Sampling Methods: Simple random sampling	UGC NET Unit III — Simple Random Sampling (SRS) Sampling Techniques Unit 2 — Simple Random Sampling	deep
stratified random sampling	UGC NET Unit III — Stratified Random Sampling Sampling Techniques Unit 3 — Stratified Random Sampling	deep
systematic sampling	UGC NET Unit III — Systematic Sampling Sampling Techniques Unit 4 — Systematic, Cluster & Multistage Sampling	deep
Ratio and regression methods of estimation	UGC NET Unit III — Ratio and Regression Estimators Sampling Theory Unit 2 — Ratio and Regression Estimators: Exact Bias, the Difference Estimator, Separate and Combined	deep
cluster sampling for equal and unequal clusters	UGC NET Unit III — Cluster Sampling Sampling Theory Unit 3 — Cluster Sampling: the Intra-Cluster Correlation, the Design Effect and Optimum Cluster Size	deep
double sampling	UGC NET Unit III — Double (Two-Phase) Sampling	brief
sampling with varying probabilities with and without replacement	UGC NET Unit III — Sampling with Varying Probability (PPS) Sampling Theory Unit 1 — Unequal Probability Sampling: Hansen-Hurwitz, Lahiri, Horvitz-Thompson and Yates-Grundy	deep
Nonnegative variance estimation, ordered and unordered estimators	UGC NET Unit III — Sampling with Varying Probability (PPS)	brief
Design of Experiments: Analysis of variance in one-way and two-way classification (with and without interaction) in fixed effects model	UGC NET Unit III — One-way ANOVA (Fixed Effects) Design & Analysis of Experiments Unit 1 — Analysis of Variance (ANOVA)	deep
principles of design of experiments	UGC NET Unit III — Principles of Design of Experiments Design & Analysis of Experiments Unit 2 — Completely Randomised Design (CRD)	deep
completely randomized design, randomized block design, Latin square design	UGC NET Unit III — CRD, RBD, Latin Square Design Design & Analysis of Experiments Unit 2 — Completely Randomised Design (CRD) Design & Analysis of Experiments Unit 3 — Randomised Block Design (RBD) Design & Analysis of Experiments Unit 4 — Latin Square Design (LSD)	deep
missing plot techniques	UGC NET Unit III — Missing Plot Techniques Design & Analysis of Experiments Unit 5 — Missing Values & Efficiency Comparisons	deep
Factorial experiments- 2², 2³	UGC NET Unit III — Factorial Experiments — 2² and 2³ Design and Analysis of Experiments Unit 2 — Factorial Experiments Beyond Two Factors: 2^k, 3^2 and Single-Degree Components	deep
confounding in factorial experiments	UGC NET Unit III — Confounding in Factorial Experiments Design and Analysis of Experiments Unit 3 — Confounding, Fractional Replication, Split-Plot and Balanced Incomplete Block Designs	deep
Incomplete block designs and its intra-block and inter-block analysis, connectedness and orthogonality of block designs	UGC NET Unit III — Incomplete Block Designs & BIBD	brief
balanced incomplete block design (BIBD), inter-block analysis and recovery of intra-block information of BIBD	UGC NET Unit III — Incomplete Block Designs & BIBD Design and Analysis of Experiments Unit 3 — Confounding, Fractional Replication, Split-Plot and Balanced Incomplete Block Designs	deep

Unit IV — Estimation Theory

17 lines · 14 deep · 3 brief · 0 not here · Read the Unit IV notes →

Syllabus line, as prescribed	Where it is taught here	Depth
Point Estimation: Unbiasedness	UGC NET Unit IV — Unbiasedness Inferential Statistics Unit 1 — Theory of Estimation	deep
consistency	UGC NET Unit IV — Consistency Inferential Statistics Unit 1 — Theory of Estimation	deep
method of moments and maximum likelihood estimators	UGC NET Unit IV — Method of Moments (MoM) Inferential Statistics Unit 1 — Theory of Estimation	deep
efficiency, uniformly minimum variance unbiased estimators	UGC NET Unit IV — Efficiency and UMVUE Estimation Theory Unit 1 — UMVU Estimation, Cramér-Rao and Rao-Blackwell	deep
Rao-Cramer lower bound	UGC NET Unit IV — Cramér–Rao Lower Bound (CRLB) Estimation Theory Unit 1 — UMVU Estimation, Cramér-Rao and Rao-Blackwell	deep
sufficiency, factorization theorem	UGC NET Unit IV — Sufficiency & Factorization Theorem Estimation Theory Unit 1 — UMVU Estimation, Cramér-Rao and Rao-Blackwell	deep
minimal sufficiency, ancillary statistic	UGC NET Unit IV — Minimal Sufficiency & Ancillarity	brief
completeness	UGC NET Unit IV — Completeness Estimation Theory Unit 2 — Completeness, Lehmann-Scheffé, CAN and BAN, Jackknife and Bootstrap	deep
Rao-Blackwell theorem	UGC NET Unit IV — Rao–Blackwell Theorem Estimation Theory Unit 1 — UMVU Estimation, Cramér-Rao and Rao-Blackwell	deep
Lehmann-Scheffe theorem	UGC NET Unit IV — Lehmann–Scheffé Theorem Estimation Theory Unit 2 — Completeness, Lehmann-Scheffé, CAN and BAN, Jackknife and Bootstrap	deep
Basu’s theorem	UGC NET Unit IV — Basu's Theorem	brief
Interval estimation: method of pivoting	UGC NET Unit IV — Method of Pivoting Estimation Theory Unit 3 — U-Statistics, Interval Estimation and Tolerance Limits	deep
confidence intervals for parameters in one sample and two sample normal populations	UGC NET Unit IV — Confidence Intervals — One and Two Sample Estimation Theory Unit 3 — U-Statistics, Interval Estimation and Tolerance Limits	deep
confidence intervals based on large samples	UGC NET Unit IV — Large-Sample Confidence Intervals Inferential Statistics Unit 3 — Large Sample Tests	deep
Nonparametric Inference: Distributions of order statistics	UGC NET Unit IV — Order Statistics & Empirical Distribution Function Distribution Theory Unit 4 — Quadratic Forms and Order Statistics	deep
empirical distribution function and its properties	UGC NET Unit IV — Order Statistics & Empirical Distribution Function	brief
Rank correlation coefficients of Spearman and Kendall	UGC NET Unit IV — Rank Correlation: Spearman & Kendall Statistical Methods Unit 2 — Correlation	deep

Unit V — Testing of Hypotheses

12 lines · 11 deep · 1 brief · 0 not here · Read the Unit V notes →

Syllabus line, as prescribed	Where it is taught here	Depth
Basic concepts, construction of tests	UGC NET Unit V — Basic Concepts Inferential Statistics Unit 2 — Testing of Hypothesis	deep
Neyman-Pearson lemma	UGC NET Unit V — Neyman–Pearson Lemma Testing of Hypotheses Unit 1 — Randomized Tests and the Complete Neyman–Pearson Lemma	deep
families with monotone likelihood ratio	UGC NET Unit V — Monotone Likelihood Ratio (MLR) Testing of Hypotheses Unit 2 — UMP Tests, Monotone Likelihood Ratio and Similar Regions	deep
Uniformly most powerful, uniformly most powerful unbiased and uniformly most powerful invariant tests	UGC NET Unit V — UMP, UMPU, UMPI Tests Testing of Hypotheses Unit 2 — UMP Tests, Monotone Likelihood Ratio and Similar Regions	deep
likelihood ratio tests: applications to one sample and two sample problems	UGC NET Unit V — Likelihood Ratio Test (LRT) Testing of Hypotheses Unit 3 — The Likelihood Ratio Test, Wald and Rao Score	deep
Wald’s sequential probability ratio test, operating characteristic and average sample number	UGC NET Unit V — Wald's Sequential Probability Ratio Test (SPRT) Testing of Hypotheses Unit 4 — Sequential Analysis and Decision Theory	deep
Chi-square tests (goodness of fit, independence of attributes, homogeneity in contingency tables)	UGC NET Unit V — Chi-square Tests Inferential Statistics Unit 4 — Small Sample Tests	deep
sign test	UGC NET Unit V — Sign Test Inferential Statistics Unit 5 — Non-parametric Tests	deep
Wilcoxon signed rank test	UGC NET Unit V — Wilcoxon Signed Rank Test Inferential Statistics Unit 5 — Non-parametric Tests	deep
Mann-Whitney U-test	UGC NET Unit V — Mann–Whitney U Test (Wilcoxon Rank-Sum) Inferential Statistics Unit 5 — Non-parametric Tests	deep
linear rank tests for location and scale problems	UGC NET Unit V — Linear Rank Tests for Location and Scale	brief
Kruskal-Wallis test	UGC NET Unit V — Kruskal–Wallis Test Inferential Statistics Unit 5 — Non-parametric Tests	deep

Unit VI — Linear Estimation, Regression Analysis and Econometrics

17 lines · 11 deep · 6 brief · 0 not here · Read the Unit VI notes →

Syllabus line, as prescribed	Where it is taught here	Depth
Simple and multiple linear regression model	UGC NET Unit VI — Simple Linear Regression Statistical Methods Unit 4 — Regression Econometrics Unit 2 — Models and Estimation	deep
Gauss-Markov model, least squares and maximum likelihood estimation	UGC NET Unit VI — Gauss–Markov Theorem Linear Algebra & Linear Models Unit 4 — Linear Models: Estimability, Gauss-Markov and Aitken	deep
testing of hypothesis related to regression parameters	UGC NET Unit VI — Hypothesis Tests on Regression Coefficients	brief
Analysis of variance for linear model, R², adjusted R²	UGC NET Unit VI — ANOVA for Linear Model, R², Adjusted R² Data Science Regression — Machine Learning Unit 3 — Supervised Learning	deep
tests of linear hypothesis	UGC NET Unit VI — Tests of Linear Hypothesis	brief
generalized and weighted least squares estimation	UGC NET Unit VI — Generalized & Weighted Least Squares Linear Algebra & Linear Models Unit 4 — Linear Models: Estimability, Gauss-Markov and Aitken	deep
indicator/dummy variables	UGC NET Unit VI — Indicator/Dummy Variables Econometrics Unit 2 — Models and Estimation	deep
multicollinearity	UGC NET Unit VI — Multicollinearity Econometrics Unit 4 — Multicollinearity	deep
heteroscedasticity	UGC NET Unit VI — Heteroscedasticity Econometrics Unit 3 — Heteroscedasticity	deep
autocorrelation, Durbin-Watson test	UGC NET Unit VI — Autocorrelation & Durbin–Watson Econometrics Unit 5 — Autocorrelation	deep
logistic regression models	UGC NET Unit VI — Logistic Regression Data Science Regression — Machine Learning Unit 3 — Supervised Learning	deep
Restricted regression estimation under exact, stochastic and mixed restrictions	UGC NET Unit VI — Restricted Regression Estimation	brief
Model with stochastic regressors and errors in variable model	UGC NET Unit VI — Stochastic Regressors & Errors-in-Variables (EIV)	brief
instrumental variable estimator	UGC NET Unit VI — Instrumental Variable (IV) Estimator	brief
simultaneous equations model, identification problem	UGC NET Unit VI — Simultaneous Equations Model & Identification Econometrics Unit 5 — Autocorrelation	deep
two-stage least squares estimation	UGC NET Unit VI — 2SLS & k-class Estimator Econometrics Unit 5 — Autocorrelation	deep
k-class estimator	UGC NET Unit VI — 2SLS & k-class Estimator	brief

Unit VII — Time Series

10 lines · 7 deep · 3 brief · 0 not here · Read the Unit VII notes →

Syllabus line, as prescribed	Where it is taught here	Depth
Time series data, descriptive measures	UGC NET Unit VII — Time Series & Descriptive Measures Applied Statistics Unit 1 — Time Series	deep
autocovariance, autocorrelation functions (ACVF, ACF), and partial autocorrelation function (PACF), correlogram	UGC NET Unit VII — Autocovariance, ACF, PACF, Correlogram Data Science Time Series Analysis and Forecasting Unit 1 — Fundamentals and Stationary Processes	deep
Strong and weak stationarity, ergodicity	UGC NET Unit VII — Stationarity & Ergodicity	brief
General linear process and Wold decomposition	UGC NET Unit VII — General Linear Process & Wold Decomposition	brief
Moving Average (MA), Autoregressive (AR) and mixed ARMA processes, stationarity and invertibility conditions	UGC NET Unit VII — Moving Average (MA) Process Data Science Time Series Analysis and Forecasting Unit 2 — ARMA and Forecasting	deep
Yule–Walker equations	UGC NET Unit VII — Yule–Walker Equations	brief
Identification, estimation and order selection of AR, MA and ARMA models, forecasting with stationary and invertible processes	UGC NET Unit VII — Identification, Estimation, Order Selection Data Science Time Series Analysis and Forecasting Unit 2 — ARMA and Forecasting	deep
Non-stationary time series: random walk, ARIMA (p, d, q) models and parameter estimation	UGC NET Unit VII — Non-stationary Time Series — Random Walk & ARIMA Data Science Time Series Analysis and Forecasting Unit 3 — Non-Stationary and Seasonal Models	deep
Frequency domain analysis: Spectral representation of time series, spectral density of AR, MA and ARMA processes	UGC NET Unit VII — Spectral Analysis Data Science Time Series Analysis and Forecasting Unit 5 — Forecast Evaluation and Comparison	deep
periodogram analysis and estimation of spectral density	UGC NET Unit VII — Periodogram & Spectral Estimation Data Science Time Series Analysis and Forecasting Unit 5 — Forecast Evaluation and Comparison	deep

Unit VIII — Multivariate Analysis

8 lines · 8 deep · 0 brief · 0 not here · Read the Unit VIII notes →

Syllabus line, as prescribed	Where it is taught here	Depth
Multivariate normal distribution and its properties	UGC NET Unit VIII — Multivariate Normal Distribution Multivariate Analysis Unit 1 — Multinomial and Multivariate Normal Distributions	deep
estimation of mean vector and covariance matrix in multivariate normal distribution, distribution of sample mean vector	UGC NET Unit VIII — Estimation of Mean Vector and Covariance Matrix Multivariate Analysis Unit 1 — Multinomial and Multivariate Normal Distributions	deep
Wishart distribution and its properties	UGC NET Unit VIII — Wishart Distribution Multivariate Analysis Unit 2 — Wishart Distribution, Generalized Variance and Correlation Distributions	deep
distribution of simple, partial and multiple correlation coefficients and related tests, inference for parameters	UGC NET Unit VIII — Simple, Partial & Multiple Correlation Multivariate Analysis Unit 2 — Wishart Distribution, Generalized Variance and Correlation Distributions	deep
Test of hypothesis related to mean vector and generalized T² statistic	UGC NET Unit VIII — Hotelling's T² Statistic Multivariate Analysis Unit 3 — Hotelling's T-squared, Mahalanobis D-squared, Wilks' Lambda and Discriminant Analysis	deep
discriminant analysis	UGC NET Unit VIII — Discriminant Analysis Multivariate Analysis Unit 3 — Hotelling's T-squared, Mahalanobis D-squared, Wilks' Lambda and Discriminant Analysis	deep
principal component analysis	UGC NET Unit VIII — Principal Component Analysis (PCA) Multivariate Analysis Unit 4 — Principal Components, Canonical Correlation, Clustering, Scaling and Factor Analysis	deep
canonical correlation analysis	UGC NET Unit VIII — Canonical Correlation Analysis (CCA) Multivariate Analysis Unit 4 — Principal Components, Canonical Correlation, Clustering, Scaling and Factor Analysis	deep

Unit IX — Stochastic Processes

8 lines · 0 deep · 8 brief · 0 not here · Read the Unit IX notes →

Syllabus line, as prescribed	Where it is taught here	Depth
Markov chains with finite and countable state space, classification of states	UGC NET Unit IX — Markov Chains: Definitions	brief
Chapman- Kolmogorov equations	UGC NET Unit IX — Chapman–Kolmogorov Equations	brief
limiting behaviour of n-step transition probabilities, stationary distribution	UGC NET Unit IX — Limiting Behaviour of n-Step Probabilities	brief
Gambler’s ruin problem	UGC NET Unit IX — Gambler's Ruin Problem	brief
simple random walk	UGC NET Unit IX — Simple Random Walk	brief
Poisson process, inter-arrival and waiting time distributions	UGC NET Unit IX — Poisson Process	brief
Birth and death processes	UGC NET Unit IX — Birth and Death Processes	brief
M/M/1 queues	UGC NET Unit IX — M/M/1 Queue	brief

Unit X — Indian Statistical System and Research Methodology

15 lines · 13 deep · 2 brief · 0 not here · Read the Unit X notes →

Syllabus line, as prescribed	Where it is taught here	Depth
Indian Statistical System: Ministry of Statistics and Programme Implementation and its different wings	UGC NET Unit X — Ministry of Statistics & Programme Implementation (MoSPI) Sampling Techniques Unit 5 — National Statistical Office & Commission	deep
National Statistical Commission	UGC NET Unit X — National Statistical Commission (NSC) Sampling Techniques Unit 5 — National Statistical Office & Commission	deep
National Statistics Office	UGC NET Unit X — National Statistical Office (NSO) Sampling Techniques Unit 5 — National Statistical Office & Commission	deep
census and large sample surveys	UGC NET Unit X — Census & Large Sample Surveys Sampling Techniques Unit 5 — National Statistical Office & Commission	deep
Contributions of P C Mahalanobis, P V Sukhatme, R C Bose, S N Roy, C R Rao, and other prominent Indian Statisticians	UGC NET Unit X — Contributions of Indian Statisticians	brief
Research Methodology: ‘R’ software: R as a calculator	UGC NET Unit X — R as a Calculator R Programming Unit 1 — Basics of R for Statistical Data Handling	deep
functions and matrix operations	UGC NET Unit X — Functions and Matrix Operations R Programming Unit 1 — Basics of R for Statistical Data Handling	deep
built in functions, missing data and logical operators	UGC NET Unit X — Built-in Functions, Missing Data & Logical Operators R Programming Unit 1 — Basics of R for Statistical Data Handling	deep
Conditional executions and loops	UGC NET Unit X — Conditional Execution & Loops Computational Statistics & R Programming Unit 4 — R Programming Basics & Vectors	deep
data management with sequences, repeats, sorting, ordering and strings	UGC NET Unit X — Data Management: Sequences, Repeats, Sorting, Ordering, Strings R Programming Unit 1 — Basics of R for Statistical Data Handling	deep
lists, factors, display and formatting	UGC NET Unit X — Lists, Factors, Display & Formatting R Programming Unit 1 — Basics of R for Statistical Data Handling	deep
Data frames, data input and output	UGC NET Unit X — Data Frames, Data Input and Output R Programming Unit 1 — Basics of R for Statistical Data Handling	deep
graphics and plots	UGC NET Unit X — Graphics & Plots R Programming Unit 3 — Data Visualization in R	deep
Basics of programming, scripts and functions	UGC NET Unit X — Scripts, Functions, and Programming Basics R Programming Unit 1 — Basics of R for Statistical Data Handling	deep
Latex and other word processing software	UGC NET Unit X — LaTeX & Other Word Processing Software	brief

Where this site is thinnest

No gaps in this syllabus. Every line above points at a page on this site.

32 lines are taught only in the UGC NET unit notes, at exam level, with no full course behind them yet. They are marked brief above; Unit IX, Stochastic Processes, is the largest block of them, because no course here teaches Markov chains or queues.

Courses for this exam

The courses the syllabus map above, and the Paper I map, send you to, in learning order — 25 of them — for each line that a full course takes further than the UGC NET unit notes. Each is listed because a map links into it.

Foundations: Descriptive Statistics Foundation · Statistical Methods Foundation · Mathematical Analysis Advanced · Linear Algebra & Linear Models Advanced
Probability & distributions: Theory of Probability & Mathematical Expectations Foundation · Probability Theory Advanced · Theoretical Discrete Distributions Foundation · Theoretical Continuous Distributions Foundation · Distribution Theory Advanced
Statistical inference: Inferential Statistics Foundation · Estimation Theory Advanced · Testing of Hypotheses Advanced
Sampling & design: Sampling Techniques Foundation · Sampling Theory Advanced · Design and Analysis of Experiments Foundation · Design and Analysis of Experiments Advanced
Models & multivariate: Econometrics Foundation · Multivariate Analysis Advanced
Applied statistics: Applied Statistics Foundation · Statistical Techniques for Research Methodology Foundation
Statistical computing: Computational Statistics & R Programming Foundation · Computational Statistics & R Programming (2023 syllabus) Foundation
Data Science: Computer Fundamentals and Office Automation · Time Series Analysis and Forecasting · Machine Learning