B.Tech Engineering Mathematics IV (MA2201) Course Introduction

This document serves as an introduction to the Engineering Mathematics IV (MA2201) course for B.Tech students at Manipal University, covering the academic year 2020-2021. The course, with 3 credits, focuses on probability and statistics, essential for computer science fields. It outlines the course outcomes, which include applying probability, understanding random variables, using correlation, comprehending sampling theory, and applying statistics for hypothesis testing. The assessment plan includes sessional exams, quizzes, assignments, and an end-term exam. The syllabus covers set theory, probability axioms, random variables, distributions (Binomial, Poisson, Normal, Chi-square), functions of random variables, sampling theory, and hypothesis testing. The document also lists the references used for the course. This provides a comprehensive overview of the course structure, content, and assessment criteria.

COURSE NAME: ENGINEERING MATHEMATICS-IV
COURSE CODE : MA 2201
LECTURE SERIES NO : 01 (ONE)
CREDITS : 03
MODE OF DELIVERY : ONLINE (POWER POINT PRESENTATION)
FACULTY : DR ANAMIKA JAIN
EMAIL-ID : [e-mail]
PROPOSED DATE OF DELIVERY: 15 February 2021
B.TECH SECOND YEAR
ACADEMIC YEAR: 2020-2021

SESSION OUTCOME “TO DEVELOP THE UNDERS
OF THE COURSE’

ASSESSMENT CRITERIA'S
ASSIGNMENT
QUIZ
MID TERM EXAMINATION –I & II
END TERM EXAMINATION

PROGRAM
OUTCOMES
MAPPING WITH
CO1
 Engineering Knowledge: Apply the knowledge of
mathematics, science, engineering fundamentals,
and an engineering specialization to the solution of
complex engineering problems.

Introduction about the course

Course Name : Engineering mathematics-IV
Course Code : MA 2201
Course Credits: 3 Credits
L T P C : 2 1 0 3
Introduction about the course

INTRODUCTION
This course is offered by Dept.of Mathematics & Statistics as a regular course t
make the students acquainted with the subject of probability and statistics
early stage.Probability and statistics is an importantfoundation forcomputer
science fields such as machine learning,artificial intelligence,computer graphics,
randomized algorithms,image processing,and scientificsimulations.In this
course,students willexpand their knowledge of probabilistic methods and app
them to diverse computationalproblems.The firstpart of the course offers in
depth knowledge of probability theory (random event, probability, characte
random variables,probabilitydistributions and momentgenerating functions)
which is necessaryfor simulation ofrandom processes.In the second part,
sampling theory is discussed. Each concept is explained through various ex
and application-oriented problems.

Course Outcomes: At the end of the course, students will be able to
[2201. 1] Apply the concept of probability and related theorems in solving various real world prob
[2201.2]
Understand the key concept of random variable, its probability distributions including m
expectation, variance and moments.
[2201.3]
Implement the variation and the relation between two random variables by using the co
correlation.
[2201.4]
Comprehend the concept of random sample and its sampling distribution which will enha
logical & analytical skills.
[2201.5]
Apply the statistics for testing the significance of the given large and small sample data
t-test, F-test and Chi-square test.

ASSESMENT PLAN
Criteria Description Max.
Marks
Internal Assessment
(Summative)
Sessional Exam I 20
Sessional Exam II 20
In class Quizzes and Assignments ,
Activity feedbacks (Accumulated and
Averaged)
20
End Term Exam
(Summative)
End Term Exam 40
Total 100

SYLLABUS
Basic Set theory,Axioms of probability,Sample space,conditionalprobability,total
probability theorem, Baye's theorem. One dimensional and two dimensiona
variables,mean and variance,properties,Chebyschev'sinequality,correlation
coefficient,Distributions,Binomial,Poisson,Normaland Chisquare.Functions of
random variables:One dimensionaland Two dimensional,F & T distributions,
Moment generatingfunctions,Samplingtheory,Central limit theorem,Point
estimation,MLE,Intervalestimation,Test of Hypothesis:significance level,certain
best tests; Chi square test.

REFERENCES BOOKS:
1. P. L. Meyer,Introduction to probability and StatisticalApplications,(2e),Oxford
and IBH publishing, 1980.
2. Miller, Freund and Johnson, Probability and Statistics for Engineers, (8e),
Hall of India, 2011.
3. Hogg and Craig, Introduction to mathematical statistics, (6e), Pearson Ed
2012.
4. Sheldon M Ross, Introduction to Probability and Statistics for Engineers a
Scientists, Elsevier, 2010