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Have Doubts Regarding This Product ? Ask Your Question

  • Q1
    What is the target audience for Spectrum Probability Theory?
    A1

    The book is specifically designed for BSc 3rd year students in their 5th semester at Punjab University, Chandigarh.

  • Q2
    What foundational concepts are covered in the book?
    A2

    The book begins with a review of essential probability concepts, including basic definitions, conditional probability, and independence, which prepare students for more advanced topics

  • Q3
    Are there any practical applications of the probability concepts discussed in the book?
    A3

    Yes, the authors emphasize applications of probability theory in fields such as statistics, economics, engineering, and natural sciences, ensuring the material is relevant for various disciplines.

  • Q4
    How does the book incorporate visual aids and examples?
    A4

    The text includes tables and illustrative examples that help students visualize concepts, making complex ideas more accessible and easier to understand.

  • Q5
    Is there a focus on exam preparation within the book?
    A5

    Yes, the book is designed to aid students in preparing for exams by providing clear explanations of key concepts and including exercises for practice.

  • Q6
    How is the syllabus for the course structured?
    A6

    The syllabus is divided into two units, with each unit covering specific topics. Students are required to attempt five questions in exams, selecting at least two from each unit

  • Q7
    How does this book facilitate self-learning?
    A7

    The clear organization, comprehensive explanations, and practical examples make it an effective self-study resource for students working independently.

  • Q8
    Are there any end-of-chapter exercises or problems included in the book?
    A8

    Yes, the book includes end-of-chapter exercises designed to reinforce the concepts taught and test students' understanding, providing opportunities for practice and self-assessment.

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1.Review of nation of probability 

2.Random variables 

3.Moment generating functions 

   Skewness and Kurtosis

4.Discrete random Variable distributions 

5.Continuous random variables and Distributions 

6.Bivariate random variables 

Tables 

Paper III : PROBABILITY THEORY

Note: 1. The syllabus has been split into two Units: Unit-I and Unit-II. Four questions will be set from
each Unit.
2. A student will be asked to attempt five questions selecting at least two questions from each Unit.
Each question will carry 6 marks.
 3. The teaching time shall be five periods (45 minutes each) per paper per week including tutorial.
 4. If internal assessment is to be conducted in the form of written examinations, then there will be
only one written examination in a Semester.

SECTION A
Review of notion of Probability, conditional Probability and independence, Bayes’ Theorem.
Random Variables : Concept, probability density function, cumulative distribution function, discrete and continuous
random variables, expectations, mean, variance, moment generating function, skewness and kurtosis.
Discrete Random Variables : Bernoulli random variable, binomial random variable, negative binomial random
variable, geometric random variable, Poisson random variable.

SECTION B
Continuous Random Variables : Uniform random variable, exponential random variable, Beta random variable,
Gamma random variable, Chi-square random variable, normal random variable.
Bivariate Random Variables : Joint distribution, joint and conditional distributions, Conditional Expectations,
Independent random variables, the correlation coefficient, Bivariate normal distribution. 

Delve into the intricate world of Probability Theory with "Spectrum Probability Theory," authored by D.R. Sharma and Parminder Singh. Published by Sharma Publications, this textbook is specifically designed for BSC 3rd year students in their 5th semester at Punjab University, Chandigarh. If you're on the lookout for a comprehensive and insightful resource in probability theory, you've found it.


This well-structured book begins by laying a solid foundation with a review of the fundamental concepts of probability. It expertly introduces the notions that underpin the field, ensuring that students grasp the essential principles before navigating more complex topics. That initial groundwork is crucial as students progress through the chapters, allowing them to build confidence and a deeper understanding of the subject matter.


As you venture further into the book, the authors delve into the fascinating topic of random variables. Here, you’ll explore both discrete and continuous random variables, alongside their respective distributions. This segment is essential for students not only wishing to succeed academically but also those eager to apply probability theory in practical scenarios. Understanding random variables is indispensable in fields such as statistics, economics, engineering, and natural sciences, making this textbook relevant for various disciplines.


One of the standout features of "Spectrum Probability Theory" is its comprehensive treatment of moment-generating functions, skewness, and kurtosis. These concepts are pivotal for summarizing the characteristics of statistical distributions and are effectively presented to enhance student comprehension. By mastering these concepts, students will gain valuable skills applicable across numerous scientific and analytical contexts.


The text also provides an in-depth exploration of discrete random variable distributions, ensuring that students familiarize themselves with commonly used distributions such as the binomial, Poisson, and geometric distributions. Following this, the authors introduce continuous random variables and their associated distributions—such as the normal and exponential distributions—which are fundamental to statistical theory and practice.


Moreover, the book tackles bivariate random variables, exploring the joint behavior of two random variables and the relationships between them. This topic is critical for students who are looking to understand correlations and dependencies in data sets, skills that are highly sought after in both academic research and industry analysis.


In addition to its clear explanations and structured approach, "Spectrum Probability Theory" includes tables and illustrative examples that aid students in visualizing concepts and applying them to real-world problems. Whether you are preparing for exams or seeking to enhance your understanding of probability theory, this textbook is an essential asset for any BSC 3rd year, semester 5th student.

1.Review of nation of probability 

2.Random variables 

3.Moment generating functions 

   Skewness and Kurtosis

4.Discrete random Variable distributions 

5.Continuous random variables and Distributions 

6.Bivariate random variables 

Tables 

Have Doubts Regarding This Product ? Ask Your Question

  • Q1
    What is the target audience for Spectrum Probability Theory?
    A1

    The book is specifically designed for BSc 3rd year students in their 5th semester at Punjab University, Chandigarh.

  • Q2
    What foundational concepts are covered in the book?
    A2

    The book begins with a review of essential probability concepts, including basic definitions, conditional probability, and independence, which prepare students for more advanced topics

  • Q3
    Are there any practical applications of the probability concepts discussed in the book?
    A3

    Yes, the authors emphasize applications of probability theory in fields such as statistics, economics, engineering, and natural sciences, ensuring the material is relevant for various disciplines.

  • Q4
    How does the book incorporate visual aids and examples?
    A4

    The text includes tables and illustrative examples that help students visualize concepts, making complex ideas more accessible and easier to understand.

  • Q5
    Is there a focus on exam preparation within the book?
    A5

    Yes, the book is designed to aid students in preparing for exams by providing clear explanations of key concepts and including exercises for practice.

  • Q6
    How is the syllabus for the course structured?
    A6

    The syllabus is divided into two units, with each unit covering specific topics. Students are required to attempt five questions in exams, selecting at least two from each unit

  • Q7
    How does this book facilitate self-learning?
    A7

    The clear organization, comprehensive explanations, and practical examples make it an effective self-study resource for students working independently.

  • Q8
    Are there any end-of-chapter exercises or problems included in the book?
    A8

    Yes, the book includes end-of-chapter exercises designed to reinforce the concepts taught and test students' understanding, providing opportunities for practice and self-assessment.

Paper III : PROBABILITY THEORY

Note: 1. The syllabus has been split into two Units: Unit-I and Unit-II. Four questions will be set from
each Unit.
2. A student will be asked to attempt five questions selecting at least two questions from each Unit.
Each question will carry 6 marks.
 3. The teaching time shall be five periods (45 minutes each) per paper per week including tutorial.
 4. If internal assessment is to be conducted in the form of written examinations, then there will be
only one written examination in a Semester.

SECTION A
Review of notion of Probability, conditional Probability and independence, Bayes’ Theorem.
Random Variables : Concept, probability density function, cumulative distribution function, discrete and continuous
random variables, expectations, mean, variance, moment generating function, skewness and kurtosis.
Discrete Random Variables : Bernoulli random variable, binomial random variable, negative binomial random
variable, geometric random variable, Poisson random variable.

SECTION B
Continuous Random Variables : Uniform random variable, exponential random variable, Beta random variable,
Gamma random variable, Chi-square random variable, normal random variable.
Bivariate Random Variables : Joint distribution, joint and conditional distributions, Conditional Expectations,
Independent random variables, the correlation coefficient, Bivariate normal distribution. 

0.00

0 Overall Rating
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