Best Books for IIT JAM Mathematical Statistics Preparation and latest 2022 Syllabus

Go through important IIT JAM Mathematical Statistics books that aspirants should read for important Mathematics and Statistics topics of IIT JAM 2022.

Best Books for IIT JAM Mathematical Statistics Preparation Books and latest 2022 Syllabus

IIT JAM 2022 will be held on February 13. On the exam day, IIT Roorkee will conduct the IIT JAM paper for the Mathematical Statistics course in Session-I, that is, from 9:30 am to 12:30 pm. Questions in the IIT JAM Mathematical Statistics paper is divided into two parts. The first part comprises questions from Mathematics. The weightage of this section in the exam is 40 per cent. The second part has questions from Statistics and the weightage of this section is 60 per cent.

Candidates are advised to know the IIT JAM exam pattern and syllabus thoroughly when they plan to take this popular Science entrance exam. This is necessary because there is negative marking in the exam and candidates should know the marking scheme of the exam to ensure that they score the highest marks in the exam.

Apart from this, for IIT JAM preparation, candidates should have a strategy in place. Thus, after completing the JAM syllabus 2021, candidates should practise as many question papers and mock tests for the exam as they can get their hands on.

Topics to Study in IIT JAM Mathematical Statistics

Aspirants can go through important topics that they should study to secure good marks in the IIT JAM exam for Mathematical Statistics below:

Important Topics in Mathematics
Sequences and SeriesDifferential Calculus
Integral CalculusMatrices
Important Topics in Statistics
ProbabilityRandom Variables
Standard DistributionsJoint Distributions
Sampling DistributionsLimit Theorems
EstimationTesting of Hypotheses


The Mathematical Statistics (MS) test paper comprises of Mathematics (40 % weightage) and Statistics (60 % weightage).


  • Sequences and Series : Convergence of sequences of real numbers,  Comparison, root and ratio tests for convergence of series of real numbers.
  • Differential Calculus : Limits, continuity and differentiability of functions of  one and two variables. Rolle’s theorem, mean value theorems, Taylor’s theorem, indeterminate forms, maxima and minima of functions of one and two variables.
  • Integral Calculus : Fundamental theorems of integral calculus. Double and  triple integrals, applications of definite integrals, arc lengths, areas and volumes.
  • Matrices : Rank, inverse of a matrix. Systems of linear equations. Linear  transformations, eigenvalues and eigenvectors. Cayley-Hamilton theorem,  symmetric, skew-symmetric and orthogonal matrices.


  • Probability : Axiomatic definition of probability and properties, conditional  probability, multiplication rule. Theorem of total probability. Bayes’ theorem and independence of events.
  • Random Variables : Probability mass function, probability density function  and cumulative distribution functions, distribution of a function of a random  variable. Mathematical expectation, moments and moment generating function.  Chebyshev’s inequality.
  • Standard Distributions : Binomial, negative binomial, geometric, Poisson,  hypergeometric, uniform, exponential, gamma, beta and normal distributions.  Poisson and normal approximations of a binomial distribution.
  • Joint Distributions : Joint, marginal and conditional distributions.  Distribution of functions of random variables. Joint moment generating  function. Product moments, correlation, simple linear regression. Independence of random variables. Sampling distributions: Chi-square, t and F distributions, and their properties.
  • Limit Theorems : Weak law of large numbers. Central limit theorem (i.i.d.with  finite variance case only).
  • Estimation : Unbiasedness, consistency and efficiency of estimators, method of  moments and method of maximum likelihood. Sufficiency, factorization  theorem. Completeness, Rao-Blackwell and Lehmann-Scheffe theorems,  uniformly minimum variance unbiased estimators. Rao-Cramer inequality.  Confidence intervals for the parameters of univariate normal, two independent  normal, and one parameter exponential distributions.
  • Testing of Hypotheses : Basic concepts, applications of Neyman-Pearson  Lemma for testing simple and composite hypotheses. Likelihood ratio tests for parameters of univariate normal distribution.

Best Books to Study for IIT JAM Mathematical Statistics Preparation 2022

Aspirants can go through topic-wise books that they can refer to when they are preparing for the IIT JAM Mathematical Statistics paper below.

IIT JAM Mathematical Statistics Books to Read for Mathematics

Books that aspirants should consider reading for topics in the Mathematics section of the exam are mentioned below:

Name of the BookAuthor
Mathematical AnalysisS.C. Malik
Mathematical AnalysisApostol
Principle of Mathematical AnalysisRudi
Schaum’s Outlines Integral CalculusFrank Ayres, Elliott Mendelson
Integral CalculusDr Gorakh Prasad
Vector Analysis: Schaum’s Outlines SeriesMurray Spiegel, Seymour Lipschutz, Dennis Spellman
Geometry and Vector CalculusA.R. Vasishtha
Ordinary Differential EquationPeter J. Collins, G.F. Simmons, M.D. Raisinghania

IIT JAM Mathematical Statistics Books to Read for Statistics

Books that aspirants should consider reading for topics in the Statistics section of the exam are mentioned below:

Name of the BookAuthor
Introduction to the Theory of StatisticsAlexander Mood, Franklin Graybill, Duane Boes
An Introduction to Probability and StatisticsV.K. Rohatgi

Apart from the above-mentioned books, aspirants should also go through the books mentioned below to prepare for IIT JAM exam for Mathematical Statistics 2022.

Name of the BookAuthor
IIT JAM: MSc Mathematical StatisticsAnand Kumar
Complete Resource Manual MSc MathematicsSuraj Singh
Fundamental of Mathematical StatisticsS.C. Gupta & V.K. Kapoor
Introduction to Mathematical StatisticsRobert V. Hogg and Craig Mckean Hogg

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