BIRLA INSTITUTE OF TECHNOLOGY & SCIENCE, PILANI
WORK INTEGRATED LEARNING PROGRAMMES
Digital
Part A: Content Design
Course Title
Quantitative Methods
Course No
Merge
Credit Units
4
Credit Model
Instructor InCharge
Sachin Gupta
Course Objectives (CO)
CO-1
To provide an understanding of concepts and techniques of Business
Statistics
CO-2
To provide an understanding of concepts and techniques of Management
Science
CO-3
To Provide an understanding of data analysis and its interpretation
CO-4
Using computer software to solve Business Statistics and Management
Science problems.
Textbooks (T)
T-1
(Business Statistics)
T-2
(Management Science)
Reference Books (R)
R-1
Bruce L. Bowerman, Richard T. O’ Connell and Emily S.
Murphee, Business Statistics in Practice. fifth edition. Tata
Mcgraw-Hill. 2010.
Frederick S. Hiller, Gerald J. Lieberman, Bodibrata Nag,
Preetam Basu. Introduction to operations research. Ninth
edition. Tata McGraw Hill. 2012.
Ken Black. Business Statistics. Fifth edition. Wiley. 2010.
(for Business Statistics)
R-2
(for Business Statistics)
R-3
(for Management Science)
R-4
(for Management Science)
Amir D Aczel, JayavelSounderpandian, PalanisamySaravanan
and Rohot Joshi. Complete Business Statistics. Seventh
edition. McGraw Hill. 2012
Fredrick S Hillier and Mark S Hillier. Introduction to
Management Science. Fourth edition. McGraw Hill. 2015.
Barry Render, Ralph M Stair, Michael E Hanna and TN Badri.
Quantitative Analysis for Management. Tenth edition.
Pearson Education. 2011.
Content Structure
Part-I: Business Statistics
1. Data and Statistics
1.1 Applications in Business and Economics
1.2 Data
1.3 Data Sources
1.4 Descriptive Statistics
1.5 Statistical Inference
1.6 Computers and Statistical Analysis
2. Descriptive Statistics: Tabular and Graphical Presentations
2.1 Summarizing Qualitative Data
2.2 Summarizing Quantitative Data
2.3 Cross Tabulation and Scatter Diagrams
3. Descriptive Statistics: Numerical Measures
3.1 Measures of Location
3.2 Measures of Variability
3.3 Measures of Shape, Relative Location and Detecting Outliers
3.4 Exploratory Data Analysis
3.5 Measures of Association between Two Variables
4. Probability
4.1 Basic probability concepts
4.2 Conditional probability
4.3 Bayes’ Theorem
5. Discrete probability distributions
5.1 Probability distribution for a discrete random variable
5.2 Binomial distribution
5.3 Poison distribution
6. Continuous Probability distribution
6.1 Uniform probability distributions
6.2 Normal probability distribution
7. Sampling and Sampling Distributions
7.1 Simple Random Sampling
7.2 Point Estimation
7.3 Introduction to Sampling Distribution
7.4 Sampling Distribution of the Mean
7.5 Sampling Distribution of Proportion
7.6 Properties of Point Estimators
7.8 Other Sampling Methods
8. Interval Estimation
8.1 Confidence interval estimation for the mean (σ known)
8.2 Confidence interval estimation for the mean (σ unknown)
8.3 Determining sample size
8.4 Confidence interval estimation for the proportion
9. Fundamentals of Hypothesis Testing
9.1 Hypothesis testing methodology
9.2 Z test of Hypothesis for the mean (σ known)
9.3 t test of Hypothesis for the mean (σ unknown)
9.4 Z test of Hypothesis for the proportion
10. Two-Sample Tests
10.1 Comparing means of two independent populations
10.2 Comparing means of two related populations
10.3 Comparing two population proportions
11. Inferences about Population Variances
11.1 Inferences about a Population Variance
11.2 Inferences about Two Population Variances
12. Tests of Goodness of Fit and Independence
12.1 Goodness of Fit Test: A Multinomial Population
12.1 Test of Independence
13. Experimental Design and ANOVA
13.1 An Introduction
13.2 ANOVA and the Completely Randomized Design
14. Simple Linear Regression
14.1 Simple Linear Regression Model
14.2 Least Square method
14.3 Coefficient of Determination
14.4 Model Assumptions
14.5 Testing for Significance
14.6 Computer Solution
15. Multiple Regression
15.1 The Multiple Regression Model and the least square estimates
15.2 R square and the adjusted R square
15.3 The Overall F test
15.4 Dummy Variable Regression
16. Time Series Forecasting
16.1 Time series component and model
16.2 Time series Regression model, Forecasting Errors
16.3 Exponential smoothing model
16.4 Introduction to Auto-regression model
Part-II: Management Science
17 An Introduction to Linear Programming
17.1 A simple optimization problem
17.2 Graphical solution procedure
17.3 Extreme points and the optimal solution
17.7 Special cases
17.6 General Linear Programming Notation
17.7 Solving LP using Excel Solver
18. Transportation Problem
18.1 Transportation Model
18.2 Feasible solution
18.3 Optimal Solution
19. Assignment Problem
19.1 Assignment Problem
19.2 Hungarian Method
Learning Outcomes (LO)
LO-No
Learning Outcome
LO-1
The student should be able to formulate and solve problems related to
topics covered in this course.
LO-2
The student should be able to solve the problems using Microsoft Excel.
Part B: Learning Plan
Academic Term
Course Title
Course No
Lead Instructor
Second Semester 2018-2019
Quantitative Methods
PDMM ZC417 / CGMB ZC431
Sachin Gupta
Live Session 1
Content
Reference
Topic
Pre CH
1
Data & Statistics
Study/HW Resource Reference
Read Chapter 1 (T1)
Watch Video Recordings
During CH
1
Introduction, Data & Statistics
Chapter 1 (T1)
Post CH
1
Data & Statistics
Solve selected textbook problems
Topic
Study/HW Resource Reference
Read Chapters 1, 2, 3 (T1)
Watch Video Recordings
Type
Live Session 2 and 3
Type
Content
Reference
Pre CH
2, 3
During CH
2, 3
Descriptive Statistics
Descriptive Statistics
2, 3
Descriptive Statistics
Solve selected textbook problems
Content
Reference
Topic
Pre CH
4,5
Probability, Discrete random Variable
Study/HW Resource Reference
Read Chapter 4;
Watch Video Recordings
During CH
4,5
Probability, Discrete random Variable
Chapter 4,5 (4.1 – 4.3) (T1)
Post CH
4,5
Probability, Discrete random Variable
Solve selected textbook problems
Post CH
Chapters 1, 2, 3 (T1)
Live Session 4
Type
Live Session 5
Type
Content
Reference
Pre CH
6
During CH
6
Post CH
6
Topic
Continuous Random variable,
Probability Distributions
Continuous Random variable,
Probability Distributions
Continuous Random variable,
Probability Distributions
Study/HW Resource Reference
Read Chapter 5 (T1) and Chapter 6
(T1);
Watch Video Recordings
Chapter 6 (6.1, 6.2, 6.4) (T1)
Solve selected textbook problems
Live Session 6
Type
Content
Reference
Pre CH
7,8
During CH
7,8
Topic
Sampling and Sampling
Distributions, Confidence Interval
Sampling and Sampling
Distributions, Confidence Interval
Study/HW Resource Reference
Read Chapter 7 (T1);
Watch Video Recordings
Chapter 7,8 (7.1 to 7.8) (T1)
Post CH
7,8
Sampling and Sampling
Distributions, Confidence Interval
Solve selected textbook problems
Live Session 7
Type
Content
Reference
Pre CH
9
During CH
9
Post CH
9
Topic
Hypothesis Testing
Hypothesis Testing
Study/HW Resource Reference
Read Chapter 8 and 9 (T1);
Watch Video Recordings
Chapter 8 (8.1 and 8.4) (T1), Chapter
9 (9.1 – 9.5) (T1)
Hypothesis Testing
Solve selected textbook problems
Live Session 8
Content
Reference
Topic
Study/HW Resource Reference
Pre CH
10
Two Sample Tests
Read Chapter 10 (T1)
During CH
10
Two Sample Tests
Chapter 10 (10.1 – 10.4) (T1)
Post CH
10
Two Sample Tests
Solve selected textbook problems
Topic
Inference about Variances, Test of
Goodness of Fit & Independence
Inference about Variances, Test of
Goodness of Fit & Independence
Study/HW Resource Reference
Type
Live Session 9
Type
Content
Reference
Pre CH
11,12
During CH
11,12
Read Chapter 11,12 (T1)
Chapter 11,12 (T1)
Inference about Variances, Test of
Goodness of Fit & Independence
Post CH
11,12
Solve selected textbook problems
Live Session 10
Type
Content
Reference
Pre CH
13
During CH
13
Topic
Study/HW Resource Reference
Simple regression Analysis
Simple regression Analysis
Read Chapter 13 (T1)
Chapter 13 (T1)
Simple regression Analysis
Post CH
13
Solve selected textbook problems
Live Session 11
Type
Pre CH
Content
Reference
14,16
Topic
Multiple Regression analysis, times
series
Study/HW Resource Reference
Read Chapter 14,16 (T1);
Watch Video Recordings
14,16
During CH
14,16
Post CH
Multiple Regression analysis, Time
series
Multiple Regression analysis, time
series
Chapter 14,16 (13.1 – 13.2) (T1)
Solve selected textbook problems
Live Session 12
Type
Pre CH
Content
Reference
3,4
3,4
During CH
3,4
Post CH
Topic
Linear Programming
Graphical solution
Linear Programming
Graphical solution
Linear Programming
Graphical solution
Study/HW Resource Reference
Read Chapter 3,4(T2);
Watch Video Recordings
Topic
Linear Programming
Simplex method
Linear Programming
Simplex method
Linear Programming
Simplex method
Study/HW Resource Reference
Read Chapter 5(T2);
Watch Video Recordings
Chapter 3,4(T2)
Solve selected textbook problems
Live Session 13
Type
Pre CH
Content
Reference
5
5
During CH
5
Post CH
Chapter 5(T2)
Solve selected textbook problems
Live Session 14
Content
Reference
Topic
Pre CH
8
Transportation Problem
Study/HW Resource Reference
Read Chapter 8(T2);
Watch Video Recordings
During CH
8
Transportation Problem
Chapter 8(T2)
Post CH
8
Transportation Problem
Solve selected textbook problems
Topic
Study/HW Resource Reference
Read Chapter 8(T2);
Watch Video Recordings
Type
Live Session 15
Type
Content
Reference
Pre CH
8
During CH
8
Assignment Problem
Assignment Problem
Chapter 8(T2)
Assignment Problem
Post CH
8
Solve selected textbook problems
Live Session 16
Content
Reference
Topic
Study/HW Resource Reference
Pre CH
18
Introduction to Queuing Models
Notes
During CH
18
Introduction to EOQ Model
Notes
Post CH
18
Introduction to project management
Notes
Type
Evaluation Scheme:
Legend: EC = Evaluation Component; AN = After Noon Session; FN = Fore Noon Session
No
Name
Type
Duration Weight Day, Date, Session, Time
EC-1
Quiz-I
Online
5%
Quiz-II
5%
Experiential
15%
Learning
EC-2
Mid-Semester Test
Closed Book 2 hours
30%
21st September 2019, 10hrs – 12hrs
EC-1
Comprehensive
Open Book
3 hours
45%
30th November 2019, 09hrs – 12hrs
Exam
Syllabus for Mid-semester examination (Closed Book): Topics in Session Nos. 1 to 8
Syllabus for End-semester examination (Open Book): All topics in Session Nos. 1 to 16.
Experiential Learning: There will be one assignment which will focus on Descriptive Statistics.
Important links and information:
Elearn portal: https://elearn.bits-pilani.ac.in
Students are expected to visit the Elearn portal on a regular basis and stay up to date with the
latest announcements and deadlines.
Contact sessions:Students should attend the online lectures as per the schedule provided on the
Elearn portal.
Evaluation Guidelines:
1. EC-1 consists of either two Assignments or three Quizzes. Students will attempt them through
the course pages on the Elearn portal. Announcements will be made on the portal, in a timely
manner.
2. For Closed Book tests: No books or reference material of any kind will be permitted.
3. For Open exams: Use of books and any printed / written reference material (filed or bound) is
permitted. However, loose sheets of paper will not be allowed. Use of calculators is permitted
in all exams. Laptops/Mobiles of any kind are not allowed. Exchange of any material is not
allowed. Book
4. If a student is unable to appear for the Regular Test/Exam due to genuine exigencies, the
student should follow the procedure to apply for the Make-Up Test/Exam which will be made
available on the Elearn portal. The Make-Up Test/Exam will be conducted only at selected
exam centres on the dates to be announced later.
It shall be the responsibility of the individual student to be regular in maintaining the self-study
schedule as given in the course handout, attend the online lectures, and take all the prescribed
evaluation components such as Assignment/Quiz, Mid-Semester Test and Comprehensive Exam
according to the evaluation scheme provided in the handout.