Back
Home: Part-I | Home: Part-II | Home: Part-III | Home: Part-IV | Home: BSc | Home: MSc | Curriculum: Current | Curriculum: Archive

**** Part-1***********

MAK
Normal
MAK
2
17
2020-02-21T04:39:00Z
2020-02-21T04:39:00Z
2
556
3174
26
7
3723
14.00

Clean
Clean
false
5.5 pt
2
2
false
false
false
EN-US
X-NONE
X-NONE

B.Stat-101: Probability
Course Code : B.Stat-101
Course Title : Probability
Course Type : Major
Level/Term and Section : B.Sc. Honours Part – I
Academic Session : 2019 – 2020
Course Instructor :
Pre-requisite (If any) :
Higher Secondary Level Mathematics: Knowledge of Permutation, Combination, Differentiation and Integration
Credit Value : 4
Total Marks : 100 (Examination 80, Tutorial/Terminal 15, and Attendance 5)

** Part-2***********

COURSE DESCRIPTION:
This course explores the basic concept of modern probability theory and its applications for decision making in economics, business, other fields of social and natural sciences. This course is heavily oriented towards the formulation of mathematical concepts on basics in sets and probability theory, including probability, conditional probability, random variables, mathematical expectation and variance, probability generating function and special discrete probability distributions with practical applications.
COURSE OBJECTIVES (CO):
To provide students with a formal treatment on sets and probability theory
1)
 Equipping students with essential tools for statistical analyses at the undergraduate level and fostering understanding through real-world statistical applications
2)
 Developing ideas, learn commonly used probability distributions
3)
 To build ability of conducting basic experimental probability outcomes
COURSE LEARNING OUTCOME (CLO):
Upon successful completion of this course, a student will be able to
1)
 Develop problem solving techniques needed to accurately calculate probabilities
2)
 Apply problem-solving techniques to solving real-world events
3)
 Use and manipulate the axioms of probability comfortably to derive the results other set operations
4)
 Understand the concept of random variables in discrete and continuous cases with derive the distribution functions (Joint, marginal and conditional) for both cases
5)
 Apply selected probability distribution to solve problem in discrete cares
6)
 Use Venn diagrams to represents the results of set operations
7)
 Present the analysis of derive statistics to all relevant and academic audiences.
COURSE PLAN / SCHEDULE:  
CLO Topics to be covered Teaching-Learning Strategies  Assessment Techniques  No. of Lectures
1
Sets: Sets, type of sets with their operations and applications.
 Lecturing with Multimedia Projector, Interactive Board and Q/A session Assignment, Class Tests, Presentation, Final Exam.
10
2
Combinatories: Principles of counting, Review of permutations, combinations and series.
5
3
Probability: Sample space and events, Probability of an event, Frequency limit and probability, Axioms of probability, Addition law of probability, Guessing and classical occupancy problems, Applications of Bose-Einstein statistics, Conditional probability, Multiplication law of probability, Partitions, Bayes’ theorem with applications, Independent events, Dependent and independent trials, Other aspects of probability.
15
4
Random Variables: Basic concepts, Discrete and continuous random variables, Density and distribution functions, Mathematical expectation and variance, Joint distributions, Independent variable, Marginal and conditional distributions, Conditional expectation and variance, Moment generating functions, Cumulant generating functions, Characteristic function, Functions of a random variable, Markov and Chebyshev inequalities, Law of large number, Central limit theorem.
16
5
Discrete Distributions: Bernoulli, Binomial, Multinomial, Negative Binomial, Poisson, Geometric and Hyper Geometric distributions.
14
Assessment Strategy Evaluation Policy (Grading System) and make-up procedures: According to the ordinance.

** Part-3**********

Main Books Recommended:
Ross, S. M. (2014). Introduction to probability models. Academic press.
Hogg, R. and A.T. Craig (2002): Introduction to Mathematical Statistics, 5th ed., Pearson Education Asia.
 —
References:
Chung, K. L. (2012). Elementary probability theory with stochastic processes. Springer Science & Business Media.
DeGroot, M. H., & Schervish, M. J. (2012). Probability and statistics. Pearson Education.
Feller, W. (2008). An introduction to probability theory and its applications (Vol. 2). John Wiley & Sons.
Lipschutz, S. and J. Schiller (2011). Introduction to Probability and Statistics, McGraw-Hill, N.Y.
Marx, M. L., & Larsen, R. J. (2006). Introduction to mathematical statistics and its applications. Pearson/Prentice Hall.
Schinazi, R. B. (2011). Probability with statistical applications. Springer.

****************