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IIT JAM MS

15
Years
15
Papers
788
Questions

Admission to M.Sc. Statistics programmes. Roughly: undergraduate real analysis and matrices, then probability, standard distributions, limit theorems, estimation and testing.

Exam pattern

  • Mathematics and Statistics sections
  • MCQ, MSQ and numerical-answer questions

The most recent paper solved here, IIT JAM 2026 MS, ran to 60 questions: 30 in Section A (section a), 10 in Section B (section b) and 20 in Section C (section c).

Conducted by IISc and the IITs (organising institute rotates). Patterns change from year to year, so confirm the current one in the year's official information brochure.

Topic analysis

How many questions each syllabus topic and subtopic was worth, year by year, across the 15 solved JAM MS papers (788 questions). Sittings of the same year are counted together.

Questions per subtopic per year
Topic · subtopic202620252024202320222021202020192018201720162015201420132012Total
Differential Calculus of one and two real variables, and Integral Calculus777109111162
Differential calculus of two variables4445565········33
Integral Calculus2322232········16
Double integrals···1111········4
Arc lengths, areas and volumes····111········3
Critical points, Hessian matrix, and saddle points1··1··1········3
Change of order of integration······1········1
Continuity and differentiability of functions of two real variables···1···········1
Improper integrals··1············1
Univariate Distributions67137710757
Cumulative distribution function (c.d.f.) of a random variable·243421········16
Mathematical expectation and moments·32·252········14
Probability mass function (p.m.f.) and Probability density function (p.d.f.) of a random variable224···1········9
Moment generating function (m.g.f.), its properties and uniqueness1·11121········7
Distribution (c.d.f., p.m.f., p.d.f.) of a function of a random variable using transformation of variable and Jacobian method1··2·11········5
Markov and Chebyshev inequalities, and their applications1·11··1········4
Discrete and Continuous random variables1·1············2
Estimation7114866850
Uniformly Minimum variance unbiased estimator (UMVUE)13211·1········9
Cramer-Rao inequality and UMVUEs1111·12········7
Methods of Estimation···2122········7
Confidence intervals for the parameters of univariate normal, two independent normal, and exponential distributions1·1·3·1········6
Consistency and relative efficiency of estimators·3·2··1········6
Least squares estimation and its applications in simple linear regression models21··1·1········5
Sufficiency of a statistic12·1·1·········5
Confidence intervals and confidence coefficient11·1·1·········4
Rao-Blackwell and Lehmann-Scheffe theorems and their applications·····1·········1
Matrices and Determinants665567742
Rank and nullity of a matrix, row-rank, column-rank, standard theorems on ranks, rank of the sum and the product of two matrices21·11·3········8
Trace of a matrix1··2122········8
Properties of solutions of system of linear equations1111111········7
Algebra of matrices1·111··········4
Adjoint and inverse of a matrix, and related properties··1·1··········2
Evaluation of determinants using transformations····1·1········2
Linear dependence and independence1····1·········2
Properties of characteristic roots and vectors·2·············2
Row reduction and echelon forms·····2·········2
Cayley-Hamilton theorem·1·············1
Consistent and inconsistent systems of linear equations·····1·········1
Dimension and basis·1·············1
Rn and Cn as vector spaces over real field··1············1
Use of determinants in solving the system of linear equations··1············1
Multivariate Distributions597665341
Bivariate normal distribution, its marginal and conditional distributions and related properties13134··········12
Uniqueness of joint m.g.f1332111········12
Joint moments, Covariance and Correlation1·21·3·········7
Conditional moments, conditional expectations and conditional variance111·1·2········6
Joint moment generating function and its properties·2···1·········3
Joint and marginal p.m.f., joint and marginal p.d.f1··············1
Descriptive Statistics and Probability846675440
Properties of probability function243·211········13
Theorem of total probability and Bayes' theorem2·11112········8
Descriptive Statistics3··1···········4
Sample Space and Algebra of Events (Event space)··1·111········4
Geometric probability1··11··········3
Addition theorem of probability function (inclusion-exclusion principle)···1·1·········2
Conditional probability and Multiplication rule···11··········2
Pairwise and mutual independence of events··1·1··········2
Boole's and Bonferroni's inequalities·····1·········1
Moments, central moments, skewness and kurtosis···1···········1
Testing of Hypotheses747564437
Level of significance, size and power of a test, p-value322211·········11
Uniformly most powerful (UMP) tests21411·1········10
Likelihood ratio tests for parameters of univariate normal distribution1·11211········7
Null and alternative hypotheses (simple and composite), Type-I and Type-II errors·1·1221········7
Most powerful critical regions and most powerful (MP) tests1·····1········2
Sampling Distributions545661734
Central Chi-square distribution211·1·4········9
Order Statistics21·12·1········7
Distribution1112···········5
Properties of Central F-distribution, distribution of the reciprocal of F-distribution··121·1········5
Sampling distribution of a statistic··112·1········5
Definitions of random sample, parameter and statistic·11············2
Of central x2 distribution with n degrees of freedom (d.f.) using m.g.f·····1·········1
Integral calculus5476421231
Reversing the order of a double integral·······11······2
Reversing the order of integration·········1····12
Arc length of a circular arc···········1···1
Arc length of a curve·······1·······1
Arc length of a curve from its derivative··········1····1
Arc length of a parametrised curve·········1·····1
Area between a horizontal line and a modulus curve············1··1
Area between two parabolas·········1·····1
Area between two semicircles········1······1
Area enclosed by a line, a hyperbola-type curve and the axis···········1···1
Area of a square given by an absolute-value inequality········1······1
Bounding an integral by comparison·········1·····1
Change of a double integral to polar coordinates··········1····1
Differentiability of an integral of a piecewise function········1······1
Differentiation under a variable limit·······1·······1
Integral of the greatest integer function···········1···1
Perimeter of a region bounded by a curve and its tangent·········1·····1
Recovering a function from its arc-length function·············1·1
Reversing the order of integration for a parabolic region············1··1
Roots of an integral function·········1·····1
Second derivative of an integral with a parameter in the integrand··········1····1
Triple integral by reversing the order··········1····1
Volume between a parabolic cylinder and a plane··········1····1
Volume by a double integral over a quarter disc·······1·······1
Volume of a cone by revolution···········1···1
Volume of a region by iterated integration·········1·····1
Volume of revolution about a vertical line·······1·······1
Volume of revolution about the x-axis··············11
Volume of revolution about the y-axis··········1····1
Probability6533462231
Binomial convolution of two coins·······1·······1
Binomial probability of approved packets·······1·······1
Conditional independence of events given a third········1······1
Conditional probability from probabilities of a union···········1···1
Conditional probability involving a complement·············1·1
Conditional probability with dice·······1·······1
Conditional probability with repeated independent attempts··············11
Conditional probability with three dice rolls········1······1
Conditional probability with three events·······1·······1
Consequences of positive dependence between two events··········1····1
Counting numbers with exactly two zeros··············11
Inclusion-exclusion for three events········1······1
Independence of events in a four-point space········1······1
Independence of functions of independent variables············1··1
Independent events from sum and product of probabilities··········1····1
Independent events identities·······1·······1
Inequalities between probabilities under a sum condition·········1·····1
Mean from a piecewise pmf·······1·······1
Normalising constant of a discrete pmf·········1·····1
Parallel system reliability············1··1
Probability a ball drawn from a transferred sample is white············1··1
Probability of at least one six in four rolls········1······1
Probability of no defective bolt among boxed bolts·············1·1
Probability that a random circle has area below a bound············1··1
Probability that a random matrix is orthogonal············1··1
Probability that heads exceed a die roll·········1·····1
Probability that one independent uniform is smaller than another···········1···1
Probability that the fourth player wins a dice game············1··1
Smallest number of parallel components for a reliability target···········1···1
Total probability over a random family size··········1····1
Two-stage binomial probability···········1···1
Differential calculus of one variable455583131
Bijection that is discontinuous everywhere·········1·····1
Coefficients from a local minimum condition·········1·····1
Consequences of the mean value theorem for a bounded-slope function·········1·····1
Continuity and differentiability at a point for an oscillating function············1··1
Continuity of sums and products with a jump··········1····1
Continuity with the greatest integer function········1······1
Differentiability at a point of oscillating functions··········1····1
Differentiability of a function defined separately on rationals and irrationals·············1·1
Exponential inequality···········1···1
Extrema for functions not differentiable at a point·······1·······1
Holder condition with exponent above one·······1·······1
Intermediate value theorem for a continuous function with given values···········1···1
L'Hopital applied three times············1··1
Limit needing a matching parameter·········1·····1
Limit of difference quotients········1······1
Limit of greatest-integer and fractional-part functions·······1·······1
Limits of powers times a function with Taylor cancellation··········1····1
Mean value theorem applied on two subintervals··········1····1
Minimum of a monotone function on a closed interval··········1····1
Minimum of a quartic and absence of real roots·······1·······1
Monotonicity from the sign of f f prime···········1···1
Number of real roots of a cubic···········1···1
Number of roots of a transcendental function···········1···1
Number of solutions of an equation of even functions········1······1
One-one and onto for a cubic···········1···1
One-to-the-infinity limit with an L'Hopital step········1······1
Parity of a constructed function···········1···1
Properties of a function oscillating near the origin········1······1
Rolle's theorem applied with an integrating factor············1··1
Second derivative from a sequence of equal values···········1···1
Second derivative of an integral function at zero·········1·····1
Sequences and Series of real numbers332339629
Cauchy sequences and their convergence111·13·········7
Sequences of real numbers, their convergence, and limits···1·23········6
Convergence of power series and radius of convergence111··11········5
Infinite series and its convergence, and divergence···21··········3
Tests for convergence and divergence of a series·1···11········3
Monotonic sequences and their limits1·····1········2
Absolute convergence of series·····1·········1
Convergence of series with non-negative terms·····1·········1
Limit superior and limit inferior of sequences····1··········1
Limit Theorems344422322
Central Limit Theorem (i.i.d. and finite variance case)1221·21········9
Weak law of large numbers, Strong law of large numbers, and Central Limit Theorem·1121·1········6
Convergence in distribution·1·11··········3
Their inter-relations. Weak law of large numbers1·1···1········3
Convergence in probability1··············1
Convergence of sequences4321112115
Averaged partial sums of a divergent series············1··1
Conditions for the sum of monotone sequences to converge·········1·····1
Limit of a power with vanishing exponent·········1·····1
Limit of a scaled sum of function values··············11
Limit of a sequence with an exponentially vanishing term··········1····1
Limit of a sum as a Riemann integral·······1·······1
Limit of a sum with a bounded sequence in an exponent·············1·1
Limit of the form one to the infinity········1······1
Limit using the asymptotic expansion of (1+1/n)^n·······1·······1
Limit using the harmonic sum and an nth root·······1·······1
Limit using the ratio test consequence·············1·1
Max and min of convergent sequences········1······1
Monotonicity and limit of a harmonic block········1······1
Sequence statements that are always true·······1·······1
Subsequences of a monotone bounded sequence···········1···1
Most powerful tests112212211
Critical value of a most powerful test for a Pareto-type density············1··1
Critical value of the most powerful test for a gamma rate·············1·1
Direction of the chi-square most powerful test··········1····1
Form of the most powerful test for a gamma shape···········1···1
Form of the most powerful test for a Laplace scale··········1····1
Neyman-Pearson test between two densities·······1·······1
Power of a most powerful test between two polynomial densities·············1·1
Power of a most powerful test for two polynomial densities············1··1
Power of a randomised most powerful test·········1·····1
Rejection region of the UMP test for a normal mean·········1·····1
Uniformly most powerful test for a discrete uniform········1······1
Central Limit Theorem (i.i.d. and finite variance case)1113211111
CLT for a sum of independent differences·············1·1
CLT probability at the mean··············11
CLT probability for a sum of gamma variables··········1····1
CLT with a deviation of larger order than the standard deviation···········1···1
Law of large numbers, CLT and unbiased variance···········1···1
Normal approximation for a sum of uniforms············1··1
Normal approximation of a chi-square tail········1······1
Normal approximation with continuity correction to a binomial··········1····1
Normalising constant in the CLT for an odds transform··········1····1
Shifted exponential CLT·······1·······1
Variance from a normal approximation probability·········1·····1
Maximum likelihood estimation1111419
Efficiency and unbiasedness in the normal model···········1···1
Maximum likelihood estimators for a uniform on a moving interval··········1····1
MLE at the boundary of the support·········1·····1
MLE for a discrete pmf with shared parameter········1······1
MLE for a power-law density···········1···1
MLE for a Weibull-type density············1··1
MLE of a variance with known weights···········1···1
MLE on a restricted parameter space···········1···1
MLE when mean equals variance·······1·······1
Conditional expectation32128
Conditional mean and variance for a triangular support········1······1
Conditional mean given a value of the second coordinate·········1·····1
Conditional mean on an ordering event·······1·······1
Expectation of a binomial compounded with a binomial·······1·······1
Mean of a variable from a uniform conditional···········1···1
Symmetry argument for a ratio of uniforms········1······1
Variance decomposition with a conditional uniform·······1·······1
Variance of a Poisson mixture···········1···1
Confidence intervals21111118
Confidence coefficient for a location parameter of a shifted exponential·······1·······1
Confidence interval consistent with a rejecting test···········1···1
Confidence interval from a chi-square pivot·········1·····1
Confidence intervals for an exponential rate··········1····1
Confidence intervals from the sample minimum of a shifted exponential········1······1
Confidence level of an interval from the sample minimum·············1·1
Sample size from the width of a normal confidence interval··············11
Shortest confidence interval for a difference of normal means·······1·······1
Systems of linear equations1212118
Consistency condition for a three-equation system·············1·1
Linear combination of given equations········1······1
Null vector of a matrix with unit norm·········1·····1
Parameter values giving infinitely many solutions············1··1
Solution set of a consistent system of rank two···········1···1
Solvability of a rank-one system·········1·····1
Symmetric two by two system with parameters··········1····1
Value of a parameter making a system inconsistent···········1···1
Continuous distributions122117
Beta distribution parameter from its median condition·······1·······1
Beta integral········1······1
Finding a parameter from normalisation and then a probability········1······1
Probability from a density with a heavy tail···········1···1
Probability from a piecewise density·········1·····1
Probability from a trapezoid density with an unknown constant·········1·····1
Probability of a band for the square of a variable··········1····1
Convergence of series1121117
Comparison with geometric series for oscillating terms··········1····1
Consequences of convergence of a positive series·······1·······1
Sequence and series with alternating-type terms··········1····1
Series built from a convergent and a divergent series of positive terms···········1···1
Series built from a convergent positive series········1······1
Series involving a convergent positive sequence··············11
Series with a parameter by comparison············1··1
Distribution functions1112117
Jump and continuous part of a mixed distribution function··········1····1
Jumps of a mixed distribution function········1······1
Mean from a piecewise distribution function··········1····1
Point mass from a mixed distribution function···········1···1
Point masses and moments from a step cdf·········1·····1
Probabilities from a mixed cdf with jumps·······1·······1
Probability from a mixed distribution function············1··1
Consistency of estimators122117
Bias and consistency of the sample minimum in a shifted exponential··········1····1
Consistency in a location family on a unit interval········1······1
Consistency of estimators of theta cubed········1······1
Consistency of shifts of the sample minimum·······1·······1
Consistent estimator for the scale of a Weibull-type density···········1···1
Consistent estimator for the standard deviation in a N(2 theta, theta squared) model··········1····1
Unbiasedness and inconsistency of a combined estimator·············1·1
Differential calculus of two variables11111117
Classifying the critical point of a two-variable function············1··1
Constrained minimum by symmetry········1······1
Continuity and partial derivatives at the origin··············11
Continuity at the origin of a two-variable function·············1·1
Derivative of a double integral with variable limits···········1···1
Local extrema of a function of two variables··········1····1
Local extrema of a separable function·········1·····1
Stochastic Processes3126
Interarrival and waiting times·1··1··········2
Markov chain as a graph1···1··········2
Poisson process and its properties, interarrival and waiting times1··············1
Stability of Markov chain (stationary and limiting distributions). Poisson process and its properties1··············1
Unbiased estimators121116
Smallest variance among unbiased linear combinations·········1·····1
Unbiased estimator for a mixture mean·······1·······1
Unbiased estimator of the square of a parameter from a single observation··············11
Unbiased estimators built from Bernoulli observations·········1·····1
Unbiasedness and consistency for N(theta, theta squared)···········1···1
Unbiasedness conditions for a linear estimator in regression··········1····1
Type I and Type II errors121116
Size and power for a discrete pair of distributions··········1····1
Size and power of a test on a Bernoulli sample········1······1
Size plus power for uniform distributions with parameter-dependent support········1······1
Smallest sample size for a size bound·······1·······1
Type I and II errors for an exponential test···········1···1
Type I and Type II error probabilities for a binomial test··············11
Eigenvalues and eigenvectors121116
Distinct eigenvalues of the all-ones matrix············1··1
Eigenvalues of a block triangular matrix·········1·····1
Eigenvector of an upper triangular matrix··············11
Every vector an eigenvector implies a scalar matrix·······1·······1
Householder reflection matrix·········1·····1
Trace of a matrix expression from the spectrum·············1·1
Moment generating functions31116
Binomial probability from an mgf·········1·····1
Identifying a mixture of normals from its mgf········1······1
Joint mgf as a product of binomial mgfs········1······1
Moment generating function of a binomial pmf with unknown constant···········1···1
Normal probability from an mgf············1··1
Reading moments from a product of binomial mgfs········1······1
Weak law of large numbers111115
Convergence in probability of a mean of squares··········1····1
Identifying the Poisson mean·······1·······1
Law of large numbers for a mixture density········1······1
Probability limit of a mean of squared deviations············1··1
Range of values the sample mean stays in·········1·····1
Mathematical expectation and moments21115
Existence of the second moment of a heavy-tailed density··········1····1
Logistic density mean and tail·······1·······1
Mean absolute value of a Laplace variable···········1···1
Second moment of a two-piece density·······1·······1
Skew-normal density mean and mass at the origin·············1·1
Joint densities12115
Mean of a marginal from a factorising joint density··········1····1
Mean of a marginal from a joint density on a triangle·········1·····1
Normalising constant of a joint density on a triangle·············1·1
Probability over a region of the unit cube·······1·······1
Tail probability of a sum for a joint density·········1·····1
Determinants11125
Determinant of a block matrix after row swap·······1·······1
Determinant of a product of elementary-like matrices·········1·····1
Product of eigenvalues of an inverse via the determinant··········1····1
Singular matrix has zero as an eigenvalue···········1···1
Values of x for which a matrix is singular···········1···1
Conditional distributions12115
Conditional probability from a joint pmf········11·····2
Conditional probability from a joint density··········1····1
Marginal probability from conditional information·········1·····1
Variance of a component from a joint density on a wedge············1··1
Correlation and covariance11215
Conditions on a variance-covariance matrix···········1···1
Correlation between a partial sum and a total··············11
Correlation coefficient from a joint density········1······1
Correlation of overlapping sums···········1···1
Covariance from a joint density on a triangle·········1·····1
Linear transformations1124
Identifying a map that is not linear···········1···1
Identifying linear maps············1··1
Linear map defined on a basis·······1·······1
Matrix of a linear map in a non-standard basis············1··1
Poisson distribution2114
Expectation of a factorial of a Poisson variable········1······1
Poisson approximation to a binomial·······1·······1
Probability from a Poisson mgf··········1····1
Solving for a Poisson parameter·······1·······1
Infinite series and its convergence11114
Telescoping series·········11····2
Sum of a series using the sum of odd numbers········1······1
Telescoping series by partial fractions·······1·······1
Cayley-Hamilton theorem11114
Cayley-Hamilton from a known spectrum··········1····1
Characteristic polynomial of a stochastic matrix········1······1
Inverse via the characteristic polynomial·······1·······1
Matrix equation from the characteristic polynomial···········1···1
Exponential distribution11114
Distribution of the positive part of a difference·······1·······1
Expected minimum of independent exponentials···········1···1
Survival of a series system············1··1
Tail probability beyond mean plus one standard deviation of a minimum·········1·····1
Bayes theorem11114
Posterior probability of a factory given survival·············1·1
Posterior probability of a vendor··········1····1
Posterior probability of no disease given a positive test·······1·······1
Posterior probability of the coin label········1······1
Normal distribution1124
Completing the square for a Gaussian-type density···········1···1
Interval of fixed length with maximal probability···········1···1
Lognormal probability from a normal mgf·········1·····1
Probability of a cubic polynomial being positive········1······1
Sufficiency and completeness1124
Completeness of single observations in two pmf families············1··1
Completeness, sufficiency and UMVUE in a normal scale model··········1····1
Estimators based on the mean and maximum of a uniform sample············1··1
Sufficient statistics that are not complete·········1·····1
Chebyshev inequality1113
Chebyshev bound from an mgf·······1·······1
Chebyshev lower bound for a sum of beta variables·········1·····1
Chebyshev upper bound··········1····1
Method of moments1113
Method of moments for a Bernoulli-type pmf·······1·······1
Method of moments for a symmetric uniform·········1·····1
Method of moments when the first moment is uninformative········1······1
Recurrence relations213
Monotone recursive sequence and its fixed point········1······1
Monotone recursive sequence and its limit·········1·····1
Recursive sequence started above the fixed point········1······1
Rank of a matrix123
Rank by row reduction··········1····1
Rank of a linear transformation··········1····1
Rank of a sum of outer products········1······1
Geometric distribution1113
Conditional probability for a geometric variable···········1···1
Sum of i.i.d. geometric variables········1······1
Tail of a sum of geometric variables·········1·····1
Uniform distribution123
Geometric probability for the distance between two random points··········1····1
Probability involving the distance from a uniform point to the ends··········1····1
Probability of a trigonometric event for a uniform variable·········1·····1
Properties of matrices1113
Eigenvalues of similar and conjugate products·········1·····1
Elementary matrices and products of elementary matrices············1··1
Involutory two by two matrix··········1····1
t distribution1113
Degrees of freedom and scaling of a two-sample t statistic·········1·····1
Studentising a single observation·············1·1
t statistic from a difference of adjacent observations··········1····1
UMVUE1113
UMVUE of the mean of an exponential···········1···1
UMVUE of the rate of a Laplace distribution············1··1
UMVUE of the square of a Poisson mean··········1····1
General theory of linear ODEs1113
Constant-coefficient ODE from its characteristic roots············1··1
Limit of the solution of a repeated-root ODE···········1···1
Second-order linear ODE with a repeated root··········1····1
Rank and nullity112
Null spaces of a two-by-three matrix········1······1
Rank of a product of rectangular matrices·······1·······1
Gamma and chi-square112
Chi-square distribution from the negative log of uniforms···········1···1
Transformation of a density to chi-square·······1·······1
Skew-symmetric matrices112
Eigenvalues of an odd-order skew-symmetric matrix········1······1
Properties of real skew-symmetric matrices·······1·······1
Joint distributions112
Factorising a joint Poisson pmf·······1·······1
Independence from a product form and expectation········1······1
Orthogonal matrices112
Projection from columns of an orthogonal matrix········1······1
Unit-length columns of an orthogonal matrix·······1·······1
Gamma distribution112
Gamma parameters from mean and variance and a tail probability·········1·····1
Moments of a gamma variable········1······1
Simple linear regression112
MLE of regression coefficients equals least squares········1······1
Predicted value from summary statistics of a regression···········1···1
Maximum likelihood22
MLE at the boundary of a moving support········1······1
MLE over a discrete parameter space········1······1
Expected value112
Expected maximum with a constant for a discrete uniform········1······1
Expressions equal to the variance of i.i.d. variables···········1···1
Convergence in distribution112
Limit of a weighted integral···········1···1
Probability of an exact value for a continuous limit·········1·····1
F distribution112
Identifying an F statistic·········1·····1
Reciprocal relation between F quantiles··············11
Power function112
Power of a test comparing two normal means·········1·····1
Power of a test for a uniform on a shifted interval···········1···1
Cramer-Rao lower bound112
Cramer-Rao bound for a function of the Poisson mean···········1···1
Variance values ruled out by the Cramer-Rao bound·········1·····1
Transformations of random variables112
Chi-square law from a power of a beta variable··········1····1
Expectation of a log-transformed uniform·········1·····1
Sampling distributions from normal112
Probability that one linear combination of normals exceeds another·········1·····1
Scaling constant for a chi-square with one degree of freedom··········1····1
First order linear equations112
Linear first-order equation in exact form··········1····1
Solutions of a first-order linear ODE···········1···1
Bivariate normal distribution112
Probability of a quadrant for independent standard normals··········1····1
Tail probability of a linear combination of bivariate normal variables············1··1
Binomial distribution112
Binomial parameter from a probability··········1····1
Variance of a binomial from a pmf relation··············11
Differential calculus of one and two variables11
Stationary point of a cubic form·······1·······1
Likelihood ratio tests11
Invariance of the LRT under a one-one transformation·······1·······1
Power series (of one real variable)11
Coefficient in the binomial series·······1·······1
Consistency and completeness11
Sufficiency and completeness in a curved family·······1·······1
Neyman-Pearson lemma11
Most powerful test for a Rayleigh-type family·······1·······1
Chi-square and related moments11
Negative moment of a chi-square variable·······1·······1
Power and Type-II error11
Size plus power for a binomial sum·······1·······1
Order statistics and transformations11
Ratio of absolute values of independent normals········1······1
UMVUE and completeness11
UMVUE for the rate of a gamma family········1······1
Tests of convergence11
Root test for a series with a parameter·········1·····1
Asymptotic distributions11
Limiting distribution of the maximum of exponentials··········1····1
Chi-square distribution11
Probability for a sum of two squared normals··········1····1
Bivariate distributions11
Probability that one of two i.i.d. variables is smaller··········1····1
Poisson approximation11
Poisson approximation for the number of cashews in a biscuit···········1···1
Sufficient statistics11
Sufficient statistics for a normal mean···········1···1
Symmetric distributions11
Integral of a distribution function for a symmetric density···········1···1
Chebyshev and Markov inequalities11
Smallest sample size from Chebyshev's inequality············1··1
Existence & uniqueness11
Solving a homogeneous-type first-order ODE by substitution············1··1
All topics606060606060606060606060351815788

Syllabus

From the IISc and the IITs (organising institute rotates) JAM 2026 MS syllabus. Always check it against the current notification.

Section 1

Sequences and Series of real numbers

Sequences of real numbers, their convergence, and limits. Cauchy sequences and their convergence. Monotonic sequences and their limits. Limits of standard sequences. Limit superior and limit inferior of 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 nth 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.

Section 2

Differential Calculus of one and two real variables, and Integral Calculus

Differential Calculus of one variable: 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, Lebnitz'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. Differential calculus of two variables: 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. Lebnitz'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). Integral Calculus: Fundamental theorems of integral calculus (single integral). Lebnitz's rule and its applications. Differentiation under 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.

Section 3

Matrices and Determinants

Rn and Cn as vector spaces over real field. Span of a set. Linear dependence and independence. Dimension and basis. Null space. 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 product of matrices. Singular and non-singular matrices, and their properties. Trace of a matrix. Adjoint and inverse of a matrix, and related properties. Rank and nullity of a matrix, row-rank, column-rank, standard theorems on ranks, rank of the sum and the product of two matrices. Row reduction and echelon forms. Consistent and inconsistent systems of linear equations. Properties of solutions of system of linear equations. Use of determinants in solving the system of linear equations. Cramer's rule. Characteristic roots and Characteristic vectors. Properties of characteristic roots and vectors. Cayley-Hamilton theorem. Quadratic forms, positive definite, positive semi-definite, negative definite,and negative semi-definite matrices, and their simple properties. Statistics (Sections 4 - 12)

Section 4

Descriptive Statistics and Probability

Descriptive Statistics: Concepts of sample and population. Different types of data. Tabular and graphical representation of data. Measures of central tendency (arithmetic mean, geometric mean, harmonic mean, median, mode). Measures of dispersion (range, inter quartile range, mean deviation about a point, standard deviation, variance, coefficient of variation). Moments, central moments, skewness and kurtosis. Bivariate data: Scatter diagram, covariance, simple, partial and multiple correlations (3 variables only), Spearman's rank correlation. Probability: Random Experiments. Sample Space and Algebra of Events (Event space). Relative frequency and Axiomatic definitions of probability. Properties of probability function. Addition theorem of probability function (inclusion-exclusion principle). Geometric probability. Boole's and Bonferroni's inequalities. Conditional probability and Multiplication rule. Theorem of total probability and Bayes' theorem. Pairwise and mutual independence of events.

Section 5

Univariate Distributions

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 variable and Jacobian method. Mathematical expectation and moments. Mean, Median, Mode, Variance, Standard deviation, Coefficient of variation, Quantiles, Quartiles, 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 first and second type), Normal and Cauchy distributions, their properties, interrelations, and limiting (approximation) cases.

Section 6

Multivariate Distributions

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 Bino2 mial, 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.

Section 7

Limit Theorems

Convergence in probability, convergence in mean square, almost sure convergence, convergence in distribution, and their inter-relations. Weak law of large numbers, Strong law of large numbers, and Central Limit Theorem (i.i.d. and finite variance case).

Section 8

Sampling Distributions

Definitions of random sample, parameter and statistic. Sampling distribution of a statistic. Order Statistics: Definition and distribution of the rth 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 x2 distribution with n degrees of freedom (d.f.) using m.g.f. Properties of central x2 distribution, additive property and limiting form of central x2 distribution. Central t - distribution: Definition and derivation of p.d.f. of Central t-distribution with n d.f., Properties and limiting form of central t-distribution. Central F-distribution: Definition and derivation of p.d.f. of Central Fdistribution with (m,n) d.f. Properties of Central F-distribution, distribution of the reciprocal of F-distribution. Relationship between t, F and 2 distributions.

Section 9

Estimation

Unbiasedness. Sufficiency of a statistic. Factorization theorem. Complete statistic. Consistency and relative efficiency of estimators. Uniformly Minimum variance unbiased estimator (UMVUE). Rao-Blackwell and Lehmann-Scheffe theorems and their applications. Cramer-Rao inequality and UMVUEs. Methods of Estimation: 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 normal, and exponential distributions.

Section 10

Testing of Hypotheses

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 construction of MP and UMP tests for parameter of one-parameter parametric families. Likelihood ratio tests for parameters of univariate normal distribution.

Section 11

Nonparametric Methods

Tests of randomness based on total number of runs. Empirical distribution function. Kolmogorov-Smirnov one sample test. One and two sample sign tests. Mann-Whitney test.

Section 12

Stochastic Processes

Discrete time Markov chain: transition probability matrix, higher order transition probabilities, Markov chain as a graph, Chapman-Kolmogorov equation, classification of states and chains, stability of Markov chain (stationary and limiting distributions). Poisson process and its properties, interarrival and waiting times.