IIT-JAM STATISTICS TEST SERIES SCHEDULE 2025
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TEST TYPE
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Modules
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Date
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Syllabus
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| MWT-01 |
PROBABILITY AND DISTRIBUTION FUNCTION |
24-Dec-2024 |
Probability: Random Experiments. Sample space and Algebra of Events (Event space). Relative frequency and Axiomatic
definitons of probability. Properties of probability function. Addition theorem of probability fucntion (inclusion exclusion principle).
Geometric probability. Boole's and Bonferroni's inequaliites. conditional probability and multiplication rule. theorem of total
probability and Bayes' theorem. pairwise and mutual independence of events. Univariate Distributions: Definition of random
variabels. cumulative distribution fucntion (cdf) of a random variable. Discrete and Continuous random variables. probability mass
fucntion (pmf) and probability density fucnion (pdf) of a random variabel. Distribution (cdf, pmf, pdf) 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, coefficient of variation, and measures of Skewness and
Kurtosis of a probability distribution. moment generating function (mgf), its properties and uniqueness, markov and Chebyshev
inequalities and their applications
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| MWT-02 |
UNIVARIATE DISTRIBUTION+MULTIVARIATE DIS |
27-Dec-24 |
Standard Univariate Distributions: 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.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 Binomial, Poisson, Negative Binomial, Gamma and Normal
Distributions using their m.g.f..
Standard Multivariate Distributions : 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.
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| MWT-03 |
REAL ANALYSIS |
31-Dec-24 |
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. 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 𝑡
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. Differential Calculus of one and two real variables: 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. 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).
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| MWT-04 |
LIMIT THEOREM +SAMPLING DISTRIBUTION +REG
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04-Jan-25 |
Limit Theorems: Convergence in probability, convergence in distribution and their inter relations. Weak law of large numbers and
Central Limit Theorem (i.i.d. case) and their applications.
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 Chisquare
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 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, dist.
Regression: Least squares estimation and its applications in simple linear regression models.
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| MWT-05 |
LINEAR ALGEBRA |
08-Jan-25 |
Linear algebra: Vector spaces with real field. Subspaces and sum of subspaces. 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 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 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. Partitioning of matrices and simple properties. Consistent and inconsistent system of linear equations. Properties of
solutions of 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
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| MWT-06 |
ESTIMATION THEORY |
12-Jan-25 |
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. Confidence intervals and confidence coefficient. Confidence intervals for
the parameters of univariate normal, two independent normal, and exponential distributions
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| MWT-07 |
TESTING OF HYPOTHESIS |
14-Jan-25 |
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 single parameter parametric families. Likelihood ratio tests for parameters of univariate normal distribution.
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| MWT-08 |
INTEGRAL CALCULUS |
18-Jan-25 |
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
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| FLT-01 |
Full Length Test |
20-Jan-25 |
As per Exam Pattern |
| FLT-02 |
Full Length Test |
23-Jan-25 |
As per Exam Pattern |
| FLT-03 |
Full Length Test |
26-Jan-25 |
As per Exam Pattern |
| FLT-04 |
Full Length Test |
28-Jan-25 |
As per Exam Pattern |
