Syllabus & Exam Pattern Discussion

Introduction Meaning and Scope

Random Variable, Probability Density Function & Problem Solving

Random Variable, Variance & Problem Solving

Functions of Random Variables, Standard Inequality & Problem Solving

Bivariate Random Variable, Correlation & Regression

Random Variables

Mathematical Expectations, Variance of Linear Combination, and Problem Solving

Mathematical Expectation, Conditional Expectation, and Problem Solving

Mathematical Expectation

Standard Discrete Type of Distribution & Mode of Binomial Distribution

Statistics: Poisson Distribution & Problem Solving

Discrete Distributions

Special Continuous Distribution Function, Normal Distribution & Gamma Distribution

Continuous Distributions

Numerical Analysis: Simpson's Rule, Interpolation, Curve Fitting, and PYQs

Curve Fitting

Correlation & Problem Solving

Correlation & Regression, and Problem Solving

Correlation and Regression

Simple Random Sampling: Variance of Estimate & Estimator Population

Probability Proportional: Varying Probabilities & Latin Method

Sampling Survey: Sampling PPS & Hertz Thompson Estimator

Sampling and Large Sample Tests

Sampling Distribution: Chi Square Distribution & Additive Properties

Chi-Square Distribution

Estimation Theory: Methods, Properties, and Problem Solving

Theory of Estimation

Statistics: Problem Solving Related to Mean, Variance, Markov Chain, LPP & BIBD

Statistics: Problem Solving, LPP, BIBD, and Regression Analysis 

Central Limit Theorem: Problem Solving Session

Statistical Inference: Estimation Theory & Problem Solving

Good Estimator, Internal Estimator & Ancillary Statistic

Bayesian Inference & Problem Solving

Statistical Inference:  Problem Solving Session

Statistical Inference-II

Markov Chain: Stochastic Processes, Transition Matrix, and Communication Relation

Markov Chain: Classification, Periodicity, and States

Markov Chain: Classification of States & Problem Solving

Markov Chain: Limiting Behaviour, Mean, Variance, and Problem Solving

Markov Chains

Function: Joint Distribution, Joint Moment Generating Function, and Problem Solving

Distribution Function & Bivariate Random Variable: Problem Solving

Joint & Conditional Distributions

Bayesian Inference, Prior Distribution & Problem Solving

Elements of Bayesian Inference

Mathematical Expectation For Univariate & Yvarient, Variance, and Covariance

Special Univariate Distributions

Probability: Trial & Event, Classical Approach, Counting Technique, Functions, Conditional, and Theorems

Random Variable, Distribution Function & its Types, and Discrete Random Variable

Probability: Problem Solving

Random Variables: Moments, Expectation, Variance, and Functions

Distribution Function: Special, One Point, Two Point, Uniform Discrete, Poisson, Binomial Theorem, and Properties

Uniform, Normal, and Gama Distribution

Binomial Distribution & Random Variable: Problem Solving

Probability

Multivariate Analysis: Sample Mean & Multivariate Normal Distribution

Multivariate Normal Distribution: Maximum Likelihood Estimation & Distribution of Quadratic Form

Maximum Likelihood Estimation

Design of Experiment, Terminologies & Types of BIBD

Joint Distribution, Conditional probability & Moments of 2 Dimension

Parameter Estimation and Sufficient Statistics

Inference - Parameter Estimation

Descriptive Statistics

Mathematical Sciences

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CSIR-UGC NET

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Test Preparation

GATE & ESE - Civil

GATE, ESE and IIT-JAM

In this course, Pankaj Kumar will cover Statistics for CSIR, 2023 from Unit 1 to Unit 44. Topics including Sequential Analysis, Probability, and Joint Distribution Function will be discussed in detail. The course would be helpful for the aspirants preparing for CSIR-UGC NET Exams. The course will be completed in 3 months, comprising 44 sessions with a duration of 60 minutes each. The course will be covered in Hindi and the notes will also be provided in English.

Foundation Course on Statistics for CSIR-UGC NET

In this course, Harsh Jaiswal will provide in-depth knowledge of the Random Variable. The course will be helpful for aspirants preparing for CSIR NET. Learners at any stage of their preparation will be benefited from the course. All doubts related to the topic will be clarified during the doubt clearing sessions in the course. Class dates and timings along with topic details are given below. The course will be covered in Hinglish and the notes will be provided in English.

Detailed Course on Univariate Random Variable - CSIR NET Dec 2024

In this course, Pankaj Kumar will cover Statistics in 3 months, comprising 32 sessions with a duration of 90 minutes each. Topics including Markov Chain, Linear Model, Design Theory, Multivariate Analysis and more will be discussed in detail from Unit 4. The course will be covered in Hindi and the notes will be provided in English. It will be helpful for students who are preparing for CSIR-UGC NET Exams.

Concept Booster Course on CSIR NET Applied Statistics

In this course, Harsh Jaiswal will cover Probability Theory for CSIR NET from unit 1 to unit 12. Topics including Random Experiments, Sample Space, and Algebra of Events will be discussed in detail. It will be helpful for students preparing for the CSIR NET Mathematical Science Exams. The course will be completed in 25 days, comprising 12 classes with a duration of 90 minutes. The course will be covered in Hindi and notes will be provided in English.

Detailed Course on Probability Theory - CSIR NET Dec 2024

In this course, Pankaj Kumar will cover Statistics. All the important topics will be discussed in detail and would be helpful for the aspirants preparing for CSIR-UGC NET exam. Learners at any stage of their preparations will benefit from the course. The course will be covered in Hindi and notes will be provided in English.

Comprehensive Course on Statistics for CSIR-UGC NET

In this course, Pankaj Kumar will cover Statistics approx. in 2 months, comprising 52 sessions with a duration of 90 minutes each. Topics including Probability, Distribution function, Mode of Convergence, Statistical Inference, Markov Chain, Linear model, Design Theory, Sampling Survey  and more will be discussed in detail from Unit 4. The course will be covered in Hindi and the notes will be provided in English. It will be helpful for students who are preparing for CSIR-UGC NET Exams.

A Basic Foundation Course on Statistics

In this course, Gajendra Purohit will cover Mix Question Practice Course For CSIR NET 22. All the important topics will be discussed in detail and would be helpful for all aspirants preparing for the CSIR NET & GATE exam. Learners at any stage of their preparation would be benefited from the course. The course will be taught in Hindi and notes will be provided in English.

Mix Question Practice Course For CSIR NET 22

In this course, Gajendra Purohit will cover the Theory of Estimation For CSIR NET' 23. All the important topics will be discussed in detail and would be helpful for all aspirants preparing for the CSIR NET & GATE exam. Learners at any stage of their preparation would be benefited from the course. The course will be taught in Hindi and notes will be provided in English. The classes are scheduled from 22nd November at 4:00 PM and will last for 90 minutes. The course duration is 15 days.

Detail Course on Theory of Estimation for CSIR NET 2023

In this course, Gajendra Purohit will cover Marcov, LPP and Mix PYQs For CSIR NET 22. All the important topics will be discussed in detail and would be helpful for all aspirants preparing for the CSIR NET & GATE exam. Learners at any stage of their preparation would be benefited from the course. The course will be taught in Hindi and notes will be provided in English.

Descriptive Statistics

Chapters

Courses

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Introduction Meaning and Scope

Random Variables

Practice

Mathematical Expectation

Practice

Discrete Distributions

Practice

Continuous Distributions

Curve Fitting

Correlation and Regression

Sampling and Large Sample Tests

Practice

Chi-Square Distribution

Theory of Estimation

Practice

Statistical Inference-II

Practice

Markov Chains

Joint & Conditional Distributions

Elements of Bayesian Inference

Special Univariate Distributions

Probability

Maximum Likelihood Estimation

Inference - Parameter Estimation

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