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A Beginner’s Guide to Probability(Generic Elective Course) [1]

Paper Code: 
25GSTT401
Credits: 
4
Contact Hours: 
60.00
Max. Marks: 
100.00
Objective: 

This course would help the student to Develop comprehensive knowledge and skills in permutations, combinations, and probability, enhancing analytical and problem-solving abilities in statistical analysis.

 

Course Outcomes: 

Course

Course Outcomes

Learning and teaching strategies

Assessment Strategies

Course Code

Course Title

25GSTT401

Statistics: Gateway to competitive exams

(Theory)

CO 1: Evaluate and solve complex permutation scenarios, assessing the correctness and efficiency of solutions.

CO 2: Compare and contrast permutations and combinations, understanding their differences and applications.

CO 3: Analyze various types of events and their interrelationships within a sample space.

CO 4: Evaluate the results obtained from applying probability theorems, ensuring accuracy and validity.

CO 5: Apply conditional probability and Bayes’ theorem to solve numerical problems.

CO6: Contribute effectively in course-specific interaction.

Approach in teaching:

Interactive Lectures,

Group Discussion,

Classroom Assignment

Problem Solving Sessions

 

 Learning activities for the students:

Assignments

Seminar

Presentation

Subject based Activities

Classroom Quiz

Assignments

Class Test

Individual Presentation

 

12.00
Unit I: 
Permutation

Definition and Concept of Permutations, Factorial Notation, Permutations Formula, Special Cases, Practice Problems.

 

12.00
Unit II: 
Combination

Definition and Concept of Combinations, Combinations Formula, Comparison with Permutations, Special Cases, Practice Problem.

 

12.00
Unit III: 
Introduction of Probability

Basic Probability Concepts, Definition of Probability, Random experiment, sample space, Trial, Events, Types of Events, Practice Problems.

 

12.00
Unit IV: 
Theorems of Probability

Types of probability, Addition theorem of probability, Multiplication theorem of probability.

 

12.00
Unit V: 
Advanced Probability

Conditional probability, Independent events, mutual Independent events, Pairwise Independent event, Law of Total probability, Numerical on Bayes’ theorem. 

Essential Readings: 
  • Agrawal, B.L. (2013): Basic statistics, New Delhi: New Age publishers.
  • Mittal, Satyaprasad & Rao (2018): Mathematics and Statistics for Management: Himalaya Publication
  • Naval Bajpai (2013): Business Statistics: Pearson Publications
  • Ken Black (2019): Business Statistics: For Contemporary Decision Making

 

SUGGESTED READING:

  • Rosander, A.C. (1965): Elements of Probability and Principles of Statistics, Calcutta: East West Press.
  • Rohatgi, V.K. and Saleh, A.K. Md. Ehsanes (2009): An Introduction to Probability Theory and Statistics, Second Edition, John Wiley and Sons.
  • Bhat, B.R (1981): Modern Probability Theory, New Age Publishers, Third edition.
  • Kingman, J.F. & Taylor, S.J. (1996): Introduction to Measure and Probability, Cambridge Univ. Press.

 

e-RESOURCES:

 

JOURNALS:

  • Sankhya The Indian Journal of Statistics, Indian Statistical Institute
  • Journal of the Indian Society for Probability and Statistics, Indian Society for Probability and Statistics
  • Journal of the Indian Statistical Association, Indian Statistical Association
  • Statistica, Department of Statistical Sciences Paolo Fortunato, University of Bologna

 

 

Academic Year: 
2025-2026 [5]

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Source URL: https://statistics.iisuniv.ac.in/courses/subjects/beginner%E2%80%99s-guide-probabilitygeneric-elective-course-0

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[2] https://epgp.inflibnet.ac.in/
[3] https://www.academia.edu/
[4] https://www.slideshare.net/
[5] https://statistics.iisuniv.ac.in/academic-year/2025-2026