This page maps a syllabus document supplied to this site, headed “49. AGRICULTURAL STATISTICS” and running from page 104 to page 106 of a larger combined syllabus. Each line is reproduced as the document words it, then pointed at the page on this site that teaches it, with the depth stated rather than implied.
What this document is, exactly. It carries no letterhead, no notification number, no date and no issuing authority — it is a Word file printed to PDF, and its own page numbers show it is an extract of subject 49 from a larger syllabus. So this page maps its topics and says nothing about marks, duration, paper count, negative marking or eligibility, none of which appear in it. If you have the official notification, those things are in there and not here.
deep a full unit page here, with the derivation worked out and problems solved step by step — enough to answer a descriptive paper, not just recognise the term. brief covered, but at exam-summary level: the definition, the formula and a worked example, without the derivation. not here nothing on this site teaches it. It is named rather than quietly skipped, because a map that hides its holes only tells you about them in the hall.
13 lines · 12 deep · 0 brief · 1 not here
| Syllabus line, as prescribed | Where it is taught here | Depth |
|---|---|---|
| Elements of measure theory, Borel field, Probability measure | Probability Theory (Unit 1) | deep |
| Random variable, Axiomatic approach to probability; Laws of addition and multiplication; Bayes’ theorem; Discrete and continuous variables | Theory of Probability (Unit 1) | deep |
| Mathematical expectation; Mathematical expectation of functions of random variables; Moment generating function, Characteristic function; Raw and central moments | Theory of Probability (Unit 4) Probability Theory (Unit 2) | deep |
| Functions of random variables; Distribution function and its properties; Univariate and bivariate probability distributions; Conditional and marginal distributions; Independence of random variables; Transformation of random variables | Theory of Probability (Unit 2) Theory of Probability (Unit 3) | deep |
| Cauchy-Schwarz inequality, Jensen inequality, Markov inequality; Chebyshev’s inequality | Probability Theory (Unit 2) | deep |
| Bernoulli weak law of large numbers; Kolmogorov strong law of large numbers; Central limit theorem; Demoviere- Laplace central limit theorem | Probability Theory (Unit 4) | deep |
| Bernoulli, Binomial, Poisson, Negative binomial, Geometric, Hypergeometric and Uniform distributions | Complete Study Material | deep |
| Rectangular, Normal, Exponential, Gamma, Beta, Cauchy and Lognormal distributions; Bivariate normal distribution | Complete Study Material Multivariate Analysis (Unit 1) | deep |
| Probability distributions of functions of random variables; Exponential Family of distributions; Mean and variance of above mentioned distributions | Distribution Theory (Unit 2) | deep |
| Sampling distributions; Distribution of mean, difference between two means and correlation coefficient; Central t, F and chi-square distributions, their properties and interrelationships | Distribution Theory (Unit 3) | deep |
| Variance stabilizing transformations | nothing on this site teaches it | not here |
| Correlation and regression; Multiple and partial correlation coefficients | Statistical Methods (Unit 2) Statistical Methods (Unit 3) | deep |
| Order statistics; Distribution of r th order statistics; Joint distribution of several order statistics and their functions; Distribution of range and median | Distribution Theory (Unit 4) | deep |
10 lines · 9 deep · 0 brief · 1 not here
| Syllabus line, as prescribed | Where it is taught here | Depth |
|---|---|---|
| Point estimation: unbiasedness, consistency, sufficiency, completeness; Neyman factorization theorem with application; Minimum variance unbiased estimator; Cramer Rao inequality; Rao Blackwell theorem | Estimation Theory (Unit 1) | deep |
| Methods of estimation: method of moments, method of minimum chi-square, method of maximum likelihood, their properties and applications | Inferential Statistics (Unit 1) | deep |
| confidence interval estimation for parameters of normal, exponential, binomial and poisson distributions | Estimation Theory (Unit 3) | deep |
| Testing of hypothesis; Neyman Pearson lemma; Unbiased test; Uniformly most powerful unbiased tests and their constructions | Testing of Hypotheses (Unit 1) Testing of Hypotheses (Unit 2) | deep |
| One and two-sample tests about mean, variance, proportion, simple correlation coefficient and simple regression coefficient | Inferential Statistics (Unit 3) Inferential Statistics (Unit 4) Statistical Methods (Unit 4) | deep |
| Behrens-Fisher problem; Bartlett’s chi-square test | nothing on this site teaches it | not here |
| Likelihood ratio test and its asymptotic properties | Testing of Hypotheses (Unit 3) | deep |
| Chi-square tests the goodness of fit and independence | Inferential Statistics (Unit 4) | deep |
| Non-parametric tests, one and two sample sign and Wilcoxon sign rank tests, run test for randomness, Wilcoxon-Mann- Whitney U test, Kruskal-Wallis and Friedman's tests, Kendall's coefficient of concordance | Inferential Statistics (Unit 5) | deep |
| Elements of sequential analysis; Wald's sequential probability ratio test | Testing of Hypotheses (Unit 4) | deep |
7 lines · 6 deep · 1 brief · 0 not here
| Syllabus line, as prescribed | Where it is taught here | Depth |
|---|---|---|
| Concept of random vector, expectation operator, dispersion matrix, independence of random vectors; Multinomial distribution; Multivariate normal distribution, marginal, joint and conditional distributions; Sample mean vector and its distribution; Maximum likelihood estimates of mean vector and dispersion matrx | Multivariate Analysis (Unit 1) | deep |
| Tests of hypotheses about mean vectors; Wishart distribution and its properties | Multivariate Analysis (Unit 2) | deep |
| Hotelling's T 2 and Mahalanobis’ D 2 statistics; Null distribution of Hotelling's T 2; Multivariate analysis of variance; Wilk's lambda criterion and its properties; Discriminant analysis, computation of linear discriminant function (LDF), classification between two multivariate normal populations based upon LDF and Mahalanobis’ D 2 | Multivariate Analysis (Unit 3) | deep |
| Canonical correlations; Factor analysis; Principal component analysis | Multivariate Analysis (Unit 4) | deep |
| Cluster analysis, similarities and dissimilarities, hierarchical clustering, single and complete linkage methods of clustering | Multivariate Analysis (Unit 4) | deep |
| Path analysis and computation of path coefficients; Multi- dimensional scaling | Multivariate Analysis, Conventional (STS-205 Section B) | brief |
| Hierarchical and non- hierarchical clustering algorithm | Multivariate Analysis (Unit 4) | deep |
15 lines · 9 deep · 3 brief · 3 not here
| Syllabus line, as prescribed | Where it is taught here | Depth |
|---|---|---|
| Theory of linear estimation; Gauss Markoff theorem | Linear Algebra & Linear Models (Unit 4) | deep |
| Atkins transformation | nothing on this site teaches it | not here |
| Hypothesis testing and analysis of variance; Analysis of covariance | Design and Analysis of Experiments (Unit 1) | deep |
| Random, fixed and mixed effects models; Basic principles of design of experiments; Orthogonality; Contrast, mutually orthogonal contrasts | Design & Analysis of Experiments (Unit 1) Design and Analysis of Experiments (Unit 1) | deep |
| Completely randomized, randomized complete block and latin square designs; Missing plot technique | Design & Analysis of Experiments (Unit 2) Design & Analysis of Experiments (Unit 5) | deep |
| Orthogonal and mutually orthogonal latin squares; Graeco latin square designs | nothing on this site teaches it | not here |
| Balanced incomplete block (BIB) designs, general properties, analysis without and with recovery of intra-block information, construction of BIB designs; Partially balanced incomplete block (PBIB) designs with two associate classes, general properties | Design and Analysis of Experiments (Unit 3) | deep |
| Youden square designs | Design and Analysis of Experiments (Unit 4) | deep |
| Cross-over designs | Practical Course — 5 Experiments | brief |
| Factorial experiments, confounding in 2 n and 3 n factorial experiments, partial and total confounding; Fractional factorial designs for symmetrical factorials | Design and Analysis of Experiments (Unit 2) Design and Analysis of Experiments (Unit 3) | deep |
| Asymmetrical factorials; Split-plot and strip-plot designs | nothing on this site teaches it | not here |
| Combined analysis of experiments | Complete Study Material | brief |
| Designs for fitting first order and second order response surfaces, second order rotatable designs | Design and Analysis of Experiments (Unit 4) | deep |
| Multiple comparison procedures | Design and Analysis of Experiments (Unit 1) | deep |
| Sampling in field experiments | Sampling Techniques (Unit 1) | brief |
10 lines · 9 deep · 0 brief · 1 not here
| Syllabus line, as prescribed | Where it is taught here | Depth |
|---|---|---|
| Complete survey vs sample survey; Probability sampling vs purposive sampling; Sampling error; sample space, sampling design, sampling strategy; Confidence interval | Sampling Techniques (Unit 1) | deep |
| Simple random sampling with and without replacement, estimation of population mean and population proportion | Sampling Techniques (Unit 2) | deep |
| Inverse sampling | nothing on this site teaches it | not here |
| Stratified random sampling, optimum allocation, number of strata, construction of strata boundaries; Determination of sample size | Sampling Techniques (Unit 3) | deep |
| Ratio, regression and product methods of estimation; Separate and combined ratio estimators | Sampling Theory (Unit 2) | deep |
| Cluster sampling | Sampling Theory (Unit 3) | deep |
| Multi-stage sampling with equal probability of selection of Units at each stage; Two-phase sampling; Successive sampling over two occasions | Sampling Techniques (Unit 4) Sampling Theory (Unit 4) | deep |
| Probability proportional to size sampling - Cumulative method and Lahiri's method of selection; Horvitz Thompson estimator, ordered and unordered estimators, sampling strategies due to Midzuno-Sen and Rao-Hartley-Cochran; Inclusion probability proportional to size sampling | Sampling Theory (Unit 1) | deep |
| Systematic sampling; Probability proportional to size systematic sampling | Sampling Techniques (Unit 4) | deep |
| Non-sampling errors, sources and classification, non-response in surveys; Response error, interpenetrating sub-samples, imputation methods; Warner's randomized response technique | Sampling Theory (Unit 4) | deep |
9 lines · 0 deep · 1 brief · 8 not here
| Syllabus line, as prescribed | Where it is taught here | Depth |
|---|---|---|
| Physical basis of inheritance, segregation and linkage; Analysis of segregation, detection and estimation of linkage for qualitative characters; Amount of information about linkage; Combined estimation, disturbed segregation | nothing on this site teaches it | not here |
| Gene and genotypic frequencies; Random mating; Hardy-Weinberg law of equilibrium; Disequilibrium due to linkage for two pairs of genes and sex-linked genes | nothing on this site teaches it | not here |
| Forces affecting gene frequency; Equilibrium between forces in large populations, polymorphism; Fisher’s fundamental theorem of natural selection; Random genetic drift; Effect of finite population size | nothing on this site teaches it | not here |
| Polygenic system for quantitative characters; Average effect of gene; Average effect of gene substitution; Dominance deviation; Breeding value | nothing on this site teaches it | not here |
| Epistatic interaction deviation, Genotype-environment correlation, genotype-environment interaction and its application; Multiple allelism in continuous variations; Maternal effects; Different components of genetic variance and their partitioning | nothing on this site teaches it | not here |
| Effect of inbreeding on quantitative characters; Heterosis; Inbreeding depression; Effect of inbreeding on mean and variance of quantitative characters | nothing on this site teaches it | not here |
| Resemblance between relatives; Phenotypic and genetic covariance between different relatives; Concept and estimation of genetic parameters; Heritability, repeatability and genetic correlation | nothing on this site teaches it | not here |
| Response due to selection, selection index and its applications in plant and animal genetic improvement programmes; Correlated response to selection; Restricted selection index | nothing on this site teaches it | not here |
| Survival analysis | Advanced Actuarial Statistics (Unit 1) | brief |
13 lines · 7 deep · 4 brief · 2 not here
| Syllabus line, as prescribed | Where it is taught here | Depth |
|---|---|---|
| Simple and multiple linear regression models and their analysis; Estimation and testing of regression parameters, sub-hypothesis testing, restricted estimation | Statistical Methods (Unit 4) Linear Algebra & Linear Models (Unit 4) | deep |
| Polynomial regression; Use of orthogonal polynomials | Complete Study Material | brief |
| Use of dummy variables; Regression with ordinal data | Econometrics (Unit 2) | brief |
| Selection of variables, stepwise and stagewise regressions | nothing on this site teaches it | not here |
| Regression diagnostics; Adequacy and validation of models | Machine Learning (Unit 2) | brief |
| Examination of residuals-specification error, auto- correlation, Durbin-Watson statistic, heteroscedasticity, multicollinearity | Econometrics (Unit 3) Econometrics (Unit 4) Econometrics (Unit 5) | deep |
| Weighted Least Squares | Econometrics (Unit 3) | deep |
| Components of time-series; Fitting of different trend models | Applied Statistics (Unit 1) | deep |
| Autocorrelation and partial auto-correlation functions; Correlogram; Determination of cyclical variations; Periodogram analysis | Time Series Analysis and Forecasting (Unit 1) | deep |
| Linear Stationary models-auto-Regressive, moving average and mixed processes; Linear non-stationary models; Forecasting | Time Series Analysis and Forecasting (Unit 2) Time Series Analysis and Forecasting (Unit 3) | deep |
| Indirect Least Squares; Pooling of cross-section and time-series data | Econometrics (Unit 2) | brief |
| Demand and supply curves; Determination of demand curves from market data; Engel’s curves | Applied Statistics II (Unit 3) | deep |
| Pareto curves | nothing on this site teaches it | not here |
22 lines · 8 deep · 7 brief · 7 not here
| Syllabus line, as prescribed | Where it is taught here | Depth |
|---|---|---|
| Limit and continuity; Differentiation of functions, successive differentiation, partial differentiation; Mean value theorems, Taylor and Maclaurin's series | Mathematical Analysis (Unit 1) | deep |
| Integration of rational, irrational and trigonometric functions; Differential equations of first order, linear differential equations of higher order with constant coefficients | nothing on this site teaches it | not here |
| Simple interpolation; Divided differences; Numerical differentiation and integration | R Programming (Unit 2) | brief |
| Group, ring, field and vector spaces, subspaces, basis, Galois field, Fermat's theorem and primitive elements | nothing on this site teaches it | not here |
| Linear independence and dependence of vectors, row and column spaces | Linear Algebra & Linear Models (Unit 1) | deep |
| Submatrices and partitioned matrices; Determinant, rank and inverse of a matrix; Determinant and inverse of partitioned matrices | Linear Algebra & Linear Models (Unit 1) | brief |
| Special matrices - unitary, similar, Hadamard, circulant, Helmert's, Idempotent and Orthogonal | Distribution Theory (Unit 3) | brief |
| Eigenvalues and eigenvectors; Spectral decomposition of matrices | Linear Algebra & Linear Models (Unit 2) | deep |
| Kronecker and Hadamard product of matrices, kronecker sum of matrices, permutation matrices, full rank factorization | nothing on this site teaches it | not here |
| Generalized inverses, Moore-Penrose inverse, applications of generalized inverse; Generalized inverse of partitioned matrices; Solutions of linear equations, equations having many solutions | Linear Algebra & Linear Models (Unit 1) | deep |
| Spectral decomposition of matrices; Quadratic forms | Linear Algebra & Linear Models (Unit 2) Linear Algebra & Linear Models (Unit 3) | deep |
| Optimization techniques and soft computing: Classical optimization techniques; Constrained optimization; Optimization and inequality; Numerical methods of optimization; Direct search method, sequential search method, random search method, simplex search method, gradient method and method of steepest ascent | Operations Research (Unit 1) | brief |
| Linear programming techniques - simplex method, duality and sensitivity analysis; Two-person zero-sum game and linear programming | Operations Research (Unit 3) Operations Research (Unit 5) Optimization Techniques (Unit 4) | deep |
| Integer programming; Statistical applications; Non- linear programming | nothing on this site teaches it | not here |
| Kuhn-Tucker conditions; Quadratic programming; Elements of multiple objective programming | nothing on this site teaches it | not here |
| Dynamic programming | Artificial Intelligence (Unit 3) | brief |
| Optimal control theory | nothing on this site teaches it | not here |
| Soft computing tools - Artificial Neural Network, support vector machines and probabilistic reasoning; Genetic algorithm, decision tree, Bayes classifiers, fuzzy logic | Classification — Machine Learning (Unit 4) Neural Networks and Deep Learning (Unit 1) | deep |
| Rough set | nothing on this site teaches it | not here |
| Simulation methods for various probability models; Resampling techniques | R Programming (Unit 5) | brief |
| Jackknife and Bootstrap | Estimation Theory (Unit 2) | deep |
| Monte Carlo simulation | R Programming (Unit 5) | brief |
Unit 6, Statistical Genetics, is not on this site at all. 8 of its 9 rows are red — every one except Survival analysis, which is covered elsewhere and is the only line in the unit that is. That is the single largest gap any map here reports. Population and quantitative genetics — Hardy–Weinberg equilibrium, gene and genotypic frequencies, heritability, breeding value, selection indices — is a subject this site does not teach, not a corner of one it teaches thinly. Read it from a standard text; nothing here will substitute.
And 15 lines elsewhere. These are named one by one rather than rounded off, because a candidate needs to know which book to open:
The courses this exam’s map sends you to, in learning order — 28 of them. Each is listed because the map links into it, not because it was judged relevant, so a course missing here is one no line of this syllabus points at.