JAM LEADER COURSE - II (Mathematical Statistics) 2018

Joint Admission Test for M.Sc.(IIT JAM)

This Course is designed and developed by a team of Highly Experienced and Qualified Faculties. It covers entire Syllabus and promise to make Student full prepared to give IIT JAM Exam with confidence.

Product Benefits

  • Study Anytime, Anywhere
  • Available in Digital Format
  • Full Time Faculty Support
  • Available at Reasonable Price
  • Check Out Our Introductory Discount
  • COD is also Available

Often delivered in upto 7 days in major cities, Check Availability at your city:


Highlight Section

  • 1200 + Practice Questions with Solutions
  • Complete Syllabus Covered
  • Easy to Understand Format
  • Highly Experienced and Qualified Faculty
  • Result Oriented Study Material


How Eduncle is helpful for Students?

Eduncle.com is a digitized education portal which basically serves as an effective medium between best Faculties and Students. Eduncle.com will serve as the gateway to success for Students who have the requisite talent but due to lack of effective Study Content are not able to prove their knowledge quotients.

We provide guidance to Students aspiring for different Competitive Exams. Our instructors possess several years of experience in teaching. Eduncle.com guide the Students as to how they can improve their performance. The Study Material we provide is well researched by our Experts, which covers exam Syllabus Comprehensively. The innovative approach by us makes the Student more confident at the time of Examination.

Course Structure

The Curriculum Section of  this Course covers the following Content :

  • 15 Units of Theory
  • 15 Topic wise Unit Solved Papers (USPs)
  • 5 Volume Solved Papers (VSPs)

Module: IIT - JAM THEORETICAL COURSE - Mathematical Statistics (MS)

    • Section : Units

        Lecture 1: Introduction

        Lecture 2: Sequence

        Lecture 3: Convergent and Divergent Sequences

        Lecture 4: Bounded and Unbounded Sequence

        Lecture 5: Monotonic Sequence

        Lecture 6: Infinite Series

        Lecture 7: Upper and Lower limits

        Lecture 8: Convergence Criteria for Sequences of Real Numbers

        Lecture 9: Subsequence

        Lecture 10: Cauchy Sequence

        Lecture 11: Absolute and Conditional Convergence

        Lecture 12: Tests of Convergence of Series

        Lecture 13: Convergence of the Infinite Integral

        Lecture 14: Alternating Series


        Lecture 1: Introduction

        Lecture 2: Limit of a Function of One Variable

        Lecture 3: Continuous Functions of One Variable

        Lecture 4: Derivability of a Function of One Variable

        Lecture 5: Functions of Two Variables

        Lecture 6: Limit of a Function of Two Variables

        Lecture 7: Continuous Functions of Two Variables

        Lecture 8: Derivability of a Function of Two Variables


        Lecture 1: Rolle Theorem : Statement

        Lecture 2: Mean Value Theorem

        Lecture 3: Taylor Theorem

        Lecture 4: Maxima and Minima of One Variable

        Lecture 5: Maxima and Minima of Functions of Two Variables

        Lecture 6: Indeterminate Forms


        Lecture 1: Antiderivatives - Differentiation in Reverse

        Lecture 2: Definite Integrals and Their Properties

        Lecture 3: Differentiation Under the Integral Sign

        Lecture 4: Fundamental Theorem of Calculus

        Lecture 5: Arc Length

        Lecture 6: Double Integral

        Lecture 7: Change of Order of Integration

        Lecture 8: Triple Integrals

        Lecture 9: Surface Area

        Lecture 10: Evaluation of Volumes

      • Unit 5: MATRICES

        Lecture 1: Matrices

        Lecture 2: Rank of Matrix

        Lecture 3: Inverse of a Matrix

        Lecture 4: Determinants

        Lecture 5: System of Linear Equations

        Lecture 6: Consistent and In-Consistent Non-Homogeneous Linear Equations

        Lecture 7: Eigen Values and Eigen Vectors

        Lecture 8: Linear Transformation


        Lecture 1: Introduction

        Lecture 2: Differential Equations

        Lecture 3: Cauchy’s Problem

        Lecture 4: Exact Equation and Its Solution by Insepection

        Lecture 5: Integrating Factors

        Lecture 6: Linear Equation

        Lecture 7: Equation Reducible to Linear form or Bernoulli’s Equation

        Lecture 8: Orthogonal and Oblique Trajectories

        Lecture 9: Homogeneous Differential Equations

        Lecture 10: Cauchy-Euler Equation

        Lecture 11: Linear Equations of Second Order with Variable Coefficients

        Lecture 12: Method of Variation of Parameters

      • Unit 7: PROBABILITY

        Lecture 1: Introduction

        Lecture 2: Classical Approach to Probability

        Lecture 3: Axiomatic Approach to Probability

        Lecture 4: Addition Theorems on Probability

        Lecture 5: Conditional Probability

        Lecture 6: Multiplication Theorems on Probability

        Lecture 7: Independent Events

        Lecture 8: The Law of Total Probability

        Lecture 9: Baye's Rule

      • Unit 8: RANDOM VARIABLES-I

        Lecture 1: Random Variable

        Lecture 2: Distribution Function

        Lecture 3: Discrete Random Variable

        Lecture 4: Continuous Random Variable

        Lecture 5: The Distribution of a Function of a Random Variable

        Lecture 6: Cumulative Distribution Functions


        Lecture 1: Mathematical Expectation

        Lecture 2: Covariance, Variance of Sums, and Correlations

        Lecture 3: Conditional Expectation

        Lecture 4: The variance and Standard Deviation

        Lecture 5: Moments

        Lecture 6: Moment Generating Functions

        Lecture 7: Characteristic Functions

        Lecture 8: Chebyshev’s Inequality


        Lecture 1: The Bernoulli and Binomial Random Variables

        Lecture 2: The Poisson Random Variable

        Lecture 3: The Negative Binomial Random Variable

        Lecture 4: The Hypergeometric Random Variable

        Lecture 5: The Zeta (or Zipf) Distribution

        Lecture 6: Expectation and Variance of Continuous Random Variables

        Lecture 7: The Uniform Random Variable

        Lecture 8: Normal Random Variables

        Lecture 9: Exponential Random Variables

        Lecture 10: The Gamma Distribution

        Lecture 11: The Weibull Distribution

        Lecture 12: The Cauchy Distribution

        Lecture 13: The Beta Distribution

        Lecture 14: The Normal Approximation to The Binomial Distribution

        Lecture 15: The DeMoivre-Laplace Limit Theorem


        Lecture 1: Joint Probability Law

        Lecture 2: Joint Probability Mass Function and Marginal

        Lecture 3: Joint Probability Distribution Function

        Lecture 4: Discrete Distribution Functions

        Lecture 5: Marginal Distribution Function

        Lecture 6: Joint and Marginal Density Functions

        Lecture 7: Conditional Distributions

        Lecture 8: Independent Random Variables

        Lecture 9: Regression

        Lecture 10: Pearson Product-Moment Correlation Coefficient

        Lecture 11: Pearson’s Correlation and Least Squares Regression Analysis


        Lecture 1: Exact Sampling Distributions (Chi-square Distribution)

        Lecture 2: M.G.F. of Chi-square Distribution

        Lecture 3: Cumulant Generating Function of Chi-square Distribution

        Lecture 4: Limiting Form of Chi-square Distribution for Large Degrees of Freedom

        Lecture 5: Chi-square Probability Curve

        Lecture 6: Chi-square Test of Goodness of Fit

        Lecture 7: Non-central Chi-square Distribution

        Lecture 8: Student's 't' Distrbution

        Lecture 9: Moment Generating Function of t-Distribution

        Lecture 10: Applications of t-Distribution

        Lecture 11: Non-central t-Distribution

        Lecture 12: F-statistic

        Lecture 13: Mode and Points of Inflexion of F-Distribution

        Lecture 14: Applications of F-Distribution

        Lecture 15: Relation between t and F-Distributions

        Lecture 16: Relation between F and Chi-square

        Lecture 17: Non-Central F-Distribution

      • Unit 13: LIMIT THEOREMS

        Lecture 1: Introduction

        Lecture 2: Chebyshev’s Inequality and The Weak Law of Large Numbers

        Lecture 3: The Central Limit Theorem

        Lecture 4: The Strong Law of Large Numbers

      • Unit 14: ESTIMATION

        Lecture 1: Introduction

        Lecture 2: Consistency

        Lecture 3: Unbiasedness

        Lecture 4: Efficient Estimators

        Lecture 5: Minimum Variance Unbiased (M.V.U.) Estimators

        Lecture 6: Sufficiency

        Lecture 7: Completeness

        Lecture 8: Factorization Theorem

        Lecture 9: Cramer-Rao Inequality

        Lecture 10: Rao-Blackwellisaton

        Lecture 11: Lehmann-Scheffe Theorem

        Lecture 12: Method of Maximum Likelihood Estimation

        Lecture 13: Method of Moments

        Lecture 14: Confidence Interval and Confidence Limits

        Lecture 15: Confidence Intervals for One Parameter Exponential Distributions


        Lecture 1: Introduction

        Lecture 2: Basic Concepts of Hypothesis Testing

        Lecture 3: Test of Hypothesis about Population Mean

        Lecture 4: Steps in Solving Testing of Hypothesis Problem

        Lecture 5: Most Powerful Test (MP Test)

        Lecture 6: Uniformly Most Powerful Test (UMP Test)

        Lecture 7: Neyman-Pearson Lemma for Testing Simple and Composite Hypotheses

        Lecture 8: Likelihood Ratio Tests

This Package Also Includes

About the author

Eduncle India

Eduncle.com is an educational market place where the knowledge seekers can quench their thirst for quality study material for almost any type of competitive exams like UGC-NET, IIT-JAM, IBPS, SSC etc., effortlessly and quickly.

Review & Ratings

Average Rating


0 Ratings

Detail Rating

5 Star
4 Star
3 Star
2 Star
1 Star
Aiming for success in your Exam?
Call Us: 1800-120-1021 (Toll FREE)