Best Mathematical Statistics Books for IIT JAM 2024

The IIT JAM (Joint Admission Test for Masters) is a highly competitive exam for students who want to pursue postgraduate programs in science and technology. To help students prepare for the IIT JAM Mathematical Statistics exam 2024, we have compiled a list of the best books that cover all the essential topics required for the exam.

The Mathematical Statistics exam is one of the popular streams under JAM and requires a strong understanding of mathematical concepts.

Author/ Publication

Book’s Name


S.C. Gupta, V.K. Kapoor

Fundamentals of Mathematical Statistics

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An Introduction to Probability & Statistics

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Mc Graw Hill

Introduction to the Theory of Statistics

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JBC Press

IIT-JAM Joint Admission Test for M.Sc. Mathematical Statistics 15 Years Solved Papers

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Robert V. Hogg and Craig McKean Hogg

Introduction to Mathematical Statistics

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Fundamentals of Mathematical Statistics

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  1. Fundamentals of Mathematical Statistics (Twelfth Edition) by Sultan Chand & Sons is good for numerical competition.
  2. Don’t use them for concepts, rather go for foreign authors for understanding topics.
An Introduction to Probability and Statistics

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  1. An Introduction to Probability and Statistics, Third Edition is an ideal reference and resource for scientists and engineers in the fields of statistics, mathematics, physics, industrial management, and engineering.
Introduction to the Theory of Statistics

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  1. A comprehensive book on mathematical statistics, offering countless examples, complete derivations, and proofs.
IIT-JAM Joint Admission Test For M.Sc. Mathematical Statistics 15 Years Solved Papers (2005-2019) And 5 Model Papers

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  1. Well summed up previous year’s questions with detailed solutions, idle for various statistics objective exams. However few solutions to some questions aren’t elaborately explained.
Introduction to Mathematical Statistics, Global Edition

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  1. Comprehensive coverage of mathematical statistics – with a proven approach.
  2. Enhances student comprehension and retention with numerous, illustrative examples and exercises.

Candidates are advised to study the complete syllabus thoroughly and focus on the topics that are most important and frequently asked in the exam. Practicing previous years’ question papers and taking mock tests can also help candidates prepare effectively for the IIT JAM exam 2024.

Practice FREE Mock Test For IIT JAM Mathematical Statistics Exam Preparation 2024:

Listyaan Learning brings one of the best test series to enhance your preparation with various questions helping you score more.

Exam Name

IIT JAM (Mathematical Statistics) Mock Test – 1

Practice Free

In conclusion, these are some of the best books for the IIT JAM Mathematical Statistics exam preparation in 2024. Students can use these books as comprehensive study material to build a strong foundation and clear their concepts.

It is also important to practice regularly and solve mock papers to get an idea about the exam pattern and level of difficulty. Good luck!

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To secure an excellent rank in the IIT JAM exam, one must also give equal importance to other subjects. Here is the collective list of the best IIT JAM books to get started with your exam preparations.

IIT-JAM 2024: Mathematical Statistics Syllabus for Preparations

The Mathematical Statistics (MS) Test Paper comprises the following topics of Mathematics (about 30% weight) and Statistics (about 70% weight).

  • Mathematics

  • Sequences of real numbers, their convergence, and limits. Cauchy sequences and their convergence. Monotonic sequences and their limits. Limits of standard sequences. Infinite series and its convergence, and divergence. Convergence of series with non-negative terms. Tests for convergence and divergence of a series. Comparison test, limit comparison test, D’Alembert’s ratio test, Cauchy’s 𝑛 𝑡ℎ root test, Cauchy’s condensation test, and integral test. Absolute convergence of series. Leibnitz’s test for the convergence of alternating series. Conditional convergence. Convergence of power series and radius of convergence.
  • Limits of functions of one real variable. Continuity and differentiability of functions of one real variable. Properties of continuous and differentiable functions of one real variable. Rolle’s theorem and Lagrange’s mean value theorems. Higher-order derivatives, Leibnitz’s rule, and its applications.
  • Taylor’s theorem with Lagrange’s and Cauchy’s form of remainders. Taylor’s and Maclaurin’s series of standard functions. Indeterminate forms and L’ Hospital’s rule. Maxima and minima of functions of one real variable, critical points, local maxima and minima, global maxima and minima, and point of inflection. Limits of functions of two real variables.
  • Continuity and differentiability of functions of two real variables. Properties of continuous and differentiable functions of two real variables. Partial differentiation and total differentiation. Leibnitz’s rule for successive differentiation. Maxima and minima of functions of two real variables. Critical points, Hessian matrix, and saddle points. Constrained optimization techniques (with Lagrange multiplier).
  • Fundamental theorems of integral calculus (single integral). Leibnitz’s rule and its applications. Differentiation under the integral sign. Improper integrals. Beta and Gamma integrals: properties and relationship between them. Double integrals. Change of order of integration. Transformation of variables. Applications of definite integrals. Arc lengths, areas, and volumes.
  • Vector spaces with the real field. Subspaces and sum of subspaces. A span of a set. Linear dependence and independence. Dimension and basis. Algebra of matrices. Standard matrices (Symmetric and Skew Symmetric matrices, Hermitian and Skew Hermitian matrices, Orthogonal and Unitary matrices, Idempotent and Nilpotent matrices).
  • Definition, properties, and applications of determinants. Evaluation of determinants using transformations. Determinant of the product of matrices. Singular and nonsingular matrices and their properties. Trace of a matrix. Adjoint and inverse of a matrix and related properties. The rank of a matrix, row-rank, column-rank, standard theorems on ranks, the rank of the sum, and the product of two matrices. Row reduction and echelon forms. Partitioning of matrices and simple properties.
  • Consistent and inconsistent system of linear equations. Properties of solutions of the system of linear equations. Use of determinants in solution to the system of linear equations. Cramer’s rule. Characteristic roots and Characteristic vectors. Properties of characteristic roots and vectors. Cayley Hamilton theorem.
  • Statistics

  • Random Experiments. Sample Space and Algebra of Events (Event space). Relative frequency and Axiomatic definitions of probability. Properties of the probability function. Addition theorem of probability function (inclusion-exclusion principle). Geometric probability. Boole’s and Bonferroni’s inequalities.
  • Conditional probability and Multiplication rule. The theorem of total probability and Bayes’ theorem. Pairwise and mutual independence of events.
  • Definition of random variables. Cumulative distribution function (c.d.f.) of a random variable. Discrete and Continuous random variables. Probability mass function (p.m.f.) and Probability density function (p.d.f.) of a random variable. Distribution (c.d.f., p.m.f., p.d.f.) of a function of a random variable using transformation of the variable and Jacobian method. Mathematical expectations and moments.
  • Mean, Median, Mode, Variance, Standard deviation, Coefficient of variation, Quantiles, Quartiles, Coefficient of Variation, and measures of Skewness and Kurtosis of a probability distribution. Moment generating function (m.g.f.), its properties, and uniqueness. Markov and Chebyshev inequalities and their applications.
  • Degenerate, Bernoulli, Binomial, Negative Binomial, Geometric, Poisson, Hypergeometric, Uniform, Exponential, Double exponential, Gamma, Beta (of the first and second type), Normal and Cauchy distributions, their properties, interrelations, and limiting (approximation) cases.
  • Definition of random vectors. Joint and marginal c.d.f.s of a random vector. Discrete and continuous type random vectors. Joint and marginal p.m.f., joint and marginal p.d.f… Conditional  c.d.f., conditional p.m.f. and conditional p.d.f.. Independence of random variables.
  • Distribution of functions of random vectors using transformation of variables and Jacobian method. Mathematical expectation of functions of random vectors. Joint moments, Covariance, and Correlation. Joint moment generating function and its properties. Uniqueness of joint m.g.f. and its applications.
  • Conditional moments, conditional expectations, and conditional variance. Additive properties of Binomial, Poisson, Negative Binomial, Gamma, and Normal Distributions using their m.g.f…
  • Multinomial distribution as a generalization of binomial distribution and its properties (moments, correlation, marginal distributions, additive property). Bivariate normal distribution, its marginal and conditional distributions, and related properties.
  • Convergence in probability, convergence in distribution, and their interrelations. Weak law of large numbers and Central Limit Theorem (i.i.d. case) and their applications.
  • Definitions of a random sample, parameter, and statistic. Sampling distribution of a statistic. Order Statistics: Definition and distribution of the 𝑟 𝑡ℎ order statistic (d.f. and p.d.f. for i.i.d. case for continuous distributions).
  • Distribution (c.d.f., p.m.f., p.d.f.) of smallest and largest order statistics (i.i.d. case for discrete as well as continuous distributions). Central Chi-square distribution: Definition and derivation of p.d.f. of central 𝜒2 distribution with 𝑛 degrees of freedom (d.f.) using m.g.f.. Properties of central 𝜒2 distribution, additive property, and limiting form of central 𝜒2 distribution.
  • Central Student’s 𝒕-distribution: Definition and derivation of p.d.f. of Central Student’s 𝑡-distribution with 𝑛 d.f., Properties and limiting form of central 𝑡-distribution. Snedecor’s Central 𝑭-distribution: Definition and derivation of p.d.f. of Snedecor’s Central 𝐹-distribution with (𝑚, 𝑛) d.f.. Properties of Central 𝐹-distribution, distribution of the reciprocal of 𝐹- distribution. Relationship between 𝑡, 𝐹, and 𝜒2 distributions.
  • Unbiasedness. Sufficiency of a statistic. Factorization theorem. Complete statistic. Consistency and relative efficiency of estimators. Uniformly minimum variance unbiased estimator (UMVUE). RaoBlackwell and Lehmann-Scheffe theorems and their applications. Cramer-Rao inequality and UMVUEs.
  • Method of moments, method of maximum likelihood, invariance of maximum likelihood estimators. Least squares estimation and its applications in simple linear regression models.
  • Confidence intervals and confidence coefficient. Confidence intervals for the parameters of univariate normal, two independent normals, and exponential distributions.
  • Null and alternative hypotheses (simple and composite), Type-I and Type-II errors. Critical region. Level of significance, size, and power of a test, p-value. Most powerful critical regions and most powerful (MP) tests. Uniformly most powerful (UMP) tests. Neyman Pearson Lemma (without proof) and its applications to the construction of MP and UMP tests for parameters of single parameter parametric families. Likelihood ratio tests for parameters of univariate normal distribution.

Frequently Asked Questions:

The IIT JAM Mathematical Statistics exam is a national-level entrance exam conducted by Indian Institutes of Technology (IITs) for admission to M.Sc. Mathematical Statistics and related courses.

The IIT JAM Mathematical Statistics exam consists of 60 multiple-choice questions and is conducted for a duration of three hours. The questions are divided into three sections, namely, Section A (30 questions), Section B (10 questions), and Section C (20 questions).

The IIT JAM Mathematical Statistics exam covers topics such as probability theory, statistical inference, linear algebra, calculus, and mathematical statistics.

Some good books for IIT JAM Mathematical Statistics exam preparation include “Probability and Statistics” by Morris H. DeGroot and Mark J. Schervish, “A First Course in Probability” by Sheldon Ross, and “Mathematical Statistics” by John E. Freund.

It is not necessary to attend coaching classes for the IIT JAM Mathematical Statistics exam, but it can be helpful. Self-study with the help of good books and online resources can also be effective.


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